Computing

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to very much further sort and understand.

General

See also Computer#Emulation, Virtualisation, Data, *nix

A program is many things. It is a piece of text typed by a programmer, it is the directing force that makes the computer do what it does, it is data in the computer's memory, yet it controls the actions performed on this same memory. Analogies that try to compare programs to objects we are familiar with tend to fall short, but a superficially fitting one is that of a machine. The gears of a mechanical watch fit together ingeniously, and if the watchmaker was any good, it will accurately show the time for many years. The elements of a program fit together in a similar way, and if the programmer knows what he is doing, the program will run without crashing.
A computer is a machine built to act as a host for these immaterial machines. Computers themselves can only do stupidly straightforward things. The reason they are so useful is that they do these things at an incredibly high speed. A program can, by ingeniously combining many of these simple actions, do very complicated things. ... When a program works, it is beautiful. The art of programming is the skill of controlling complexity. The great program is subdued, made simple in its complexity.

-- http://eloquentjavascript.net/chapter1.html

Basics









  • Pipe Logic - "In this model, a UNIX pipe acts like a wire, that is, a conductor with parasitic capacitance."
  • Simple Made Easy - Rich Hickey emphasizes simplicity’s virtues over easiness’, showing that while many choose easiness they may end up with complexity, and the better way is to choose easiness along the simplicity path. ]



Reference

  • WikiChip - a semiconductor and computer engineering technology website. We detail historical and modern electronic systems and technologies as well as related topics.





Books


News and blogs





History






  • https://en.wikipedia.org/wiki/Stepped_reckoner - a digital mechanical calculator invented by the German mathematician Gottfried Wilhelm Leibniz around 1672 and completed in 1694. The name comes from the translation of the German term for its operating mechanism, Staffelwalze, meaning 'stepped drum'. It was the first calculator that could perform all four arithmetic operations. Its intricate precision gearwork, however, was somewhat beyond the fabrication technology of the time; mechanical problems, in addition to a design flaw in the carry mechanism, prevented the machines from working reliably.






















  • https://en.wikipedia.org/wiki/Altair_8800 - a microcomputer designed in 1975 based on the Intel 8080 CPU. Interest grew quickly after it was featured on the cover of the January 1975 issue (published in late November 1974) of Popular Electronics, and was sold by mail order through advertisements there, in Radio-Electronics, and in other hobbyist magazines. The designers hoped to sell a few hundred build-it-yourself kits to hobbyists, and were surprised when they sold thousands in the first month.











  • The Jargon File - a comprehensive compendium of hacker slang illuminating many aspects of hackish tradition, folklore, and humor.


People

















Computation

See also Maths#Logic, Hardware



  • https://en.wikipedia.org/wiki/Theory_of_computation - branch that deals with how efficiently problems can be solved on a model of computation, using an algorithm. The field is divided into three major branches: automata theory and language, computability theory, and computational complexity theory.
  • https://en.wikipedia.org/wiki/Model_of_computation - the definition of the set of allowable operations used in computation and their respective costs. It is used for measuring the complexity of an algorithm in execution time and or memory space: by assuming a certain model of computation, it is possible to analyze the computational resources required or to discuss the limitations of algorithms or computers.
  • https://en.wikipedia.org/wiki/Abstract_machine - abstract machines are often used in thought experiments regarding computability or to analyze the complexity of algorithms (see computational complexity theory). A typical abstract machine consists of a definition in terms of input, output, and the set of allowable operations used to turn the former into the latter. The best-known example is the Turing machine.





Automata theory

  • https://en.wikipedia.org/wiki/Automata_theory - the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science, under discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-acting".

Automata theory is closely related to formal language theory. An automaton is a finite representation of a formal language that may be an infinite set. Automata are often classified by the class of formal languages they can recognize. Automata play a major role in theory of computation, compiler construction, artificial intelligence, parsing and formal verification.



An automaton can be said to recognize a string if we view the content of its tape as input. In other words, the automaton computes a function that maps strings into the set {0,1}. Alternatively, we can say that an automaton generates strings, which means viewing its tape as an output tape. On this view, the automaton generates a formal language, which is a set of strings. The two views of automata are equivalent: the function that the automaton computes is precisely the indicator function of the set of strings it generates. The class of languages generated by finite automata is known as the class of regular languages.



  • The language of languages - explains grammars and common notations for grammars, such as Backus-Naur Form (BNF), Extended Backus-Naur Form (EBNF) and regular extensions to BNF.


  • YouTube: Computers Without Memory - Computerphile - They're called 'Finite State Automata" and occupy the centre of Chomsky's Hierarchy - Professor Brailsford explains the ultimate single purpose computer.




  • https://en.wikipedia.org/wiki/Nested_stack_automaton - a finite automaton that can make use of a stack containing data which can be additional stacks. Like a stack automaton, a nested stack automaton may step up or down in the stack, and read the current symbol; in addition, it may at any place create a new stack, operate on that one, eventually destroy it, and continue operating on the old stack. This way, stacks can be nested recursively to an arbitrary depth; however, the automaton always operates on the innermost stack only.



Computability theory

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See also Maths#Type theory

  • https://en.wikipedia.org/wiki/Computability_theory - also called recursion theory, is a branch of mathematical logic, of computer science, and of the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees. The basic questions addressed by recursion theory are "What does it mean for a function on the natural numbers to be computable?" and "How can noncomputable functions be classified into a hierarchy based on their level of noncomputability?". The answers to these questions have led to a rich theory that is still being actively researched. The field has since grown to include the study of generalized computability and definability.



  • https://en.wikipedia.org/wiki/Computable_function - the basic objects of study in computability theory. Computable functions are the formalized analogue of the intuitive notion of algorithm. They are used to discuss computability without referring to any concrete model of computation such as Turing machines or register machines. Any definition, however, must make reference to some specific model of computation but all valid definitions yield the same class of functions. Particular models of computability that give rise to the set of computable functions are the Turing-computable functions and the μ-recursive functions.


  • https://en.wikipedia.org/wiki/Turing_degree - or degree of unsolvability of a set of natural numbers measures the level of algorithmic unsolvability of the set. The concept of Turing degree is fundamental in computability theory, where sets of natural numbers are often regarded as decision problems; the Turing degree of a set tells how difficult it is to solve the decision problem associated with the set. That is, to determine whether an arbitrary number is in the given set.






  • https://en.wikipedia.org/wiki/Grzegorczyk_hierarchy - a hierarchy of functions used in computability theory (Wagner and Wechsung 1986:43). Every function in the Grzegorczyk hierarchy is a primitive recursive function, and every primitive recursive function appears in the hierarchy at some level. The hierarchy deals with the rate at which the values of the functions grow; intuitively, functions in lower level of the hierarchy grow slower than functions in the higher levels.





  • https://en.wikipedia.org/wiki/Recursion_(computer_science) - in computer science is a method where the solution to a problem depends on solutions to smaller instances of the same problem (as opposed to iteration). The approach can be applied to many types of problems, and recursion is one of the central ideas of computer science.
  • https://en.wikipedia.org/wiki/Recursive_set - a set of natural numbers is called recursive, computable or decidable if there is an algorithm which terminates after a finite amount of time and correctly decides whether or not a given number belongs to the set. A more general class of sets consists of the recursively enumerable sets, also called semidecidable sets. For these sets, it is only required that there is an algorithm that correctly decides when a number is in the set; the algorithm may give no answer (but not the wrong answer) for numbers not in the set. A set which is not computable is called noncomputable or undecidable.


  • https://en.wikipedia.org/wiki/Recursively_enumerable_language - also recognizable, partially decidable, semidecidable, Turing-acceptable or Turing-recognizable, if it is a recursively enumerable subset in the set of all possible words over the alphabet of the language, i.e., if there exists a Turing machine which will enumerate all valid strings of the language. Recursively enumerable languages are known as type-0 languages in the Chomsky hierarchy of formal languages. All regular, context-free, context-sensitive and recursive languages are recursively enumerable. The class of all recursively enumerable languages is called RE.
  • https://en.wikipedia.org/wiki/Recursively_enumerable_set - a set S of natural numbers is called recursively enumerable, computably enumerable, semidecidable, provable or Turing-recognizable if: There is an algorithm such that the set of input numbers for which the algorithm halts is exactly S, or equivalently; There is an algorithm that enumerates the members of S. That means that its output is simply a list of the members of S: s1, s2, s3, ... . If necessary, this algorithm may run forever. The first condition suggests why the term semidecidable is sometimes used; the second suggests why computably enumerable is used. The abbreviations r.e. and c.e. are often used, even in print, instead of the full phrase. In computational complexity theory, the complexity class containing all recursively enumerable sets is RE.



  • The Holy Trinity - "The doctrine of computational trinitarianism holds that computation manifests itself in three forms: proofs of propositions, programs of a type, and mappings between structures. These three aspects give rise to three sects of worship: Logic, which gives primacy to proofs and propositions; Languages, which gives primacy to programs and types; Categories, which gives primacy to mappings and structures. The central dogma of computational trinitarianism holds that Logic, Languages, and Categories are but three manifestations of one divine notion of computation. There is no preferred route to enlightenment: each aspect provides insights that comprise the experience of computation in our lives." [23]


  • Physics, Topology, Logic and Computation: A Rosetta Stone [24] - "In physics, Feynman diagrams are used to reason about quantum processes. In the 1980s, it became clear that underlying these diagrams is a powerful analogy between quantum physics and topology: namely, a linear operator behaves very much like a "cobordism". Similar diagrams can be used to reason about logic, where they represent proofs, and computation, where they represent programs. With the rise of interest in quantum cryptography and quantum computation, it became clear that there is extensive network of analogies between physics, topology, logic and computation. In this expository paper, we make some of these analogies precise using the concept of "closed symmetric monoidal category". We assume no prior knowledge of category theory, proof theory or computer science."







  • Where LISP Fits - [26] - "These simplifications made LISP into a way of describing computable functions much neater than the Turing machines or the general recursive definitions used in recursive function theory. The fact that Turing machines constitute an awkward programming language doesn’t much bother recursive function theorists, because they almost never have any reason to write particular recursive definitions, since the theory concerns recursive functions in general. They often have reason to prove that recursive functions with specific properties exist, but this can be done by an informal argument without having to write them down explicitly. In the early days of computing, some people developed programming languages based on Turing machines; perhaps it seemed more scientific. Anyway, I decided to write a paper describing LISP both as a programming language and as a formalism for doing recursive function theory."




Turing machine

Lambda calculus

  • https://en.wikipedia.org/wiki/Lambda_calculus - a formal system in mathematical logic for expressing computation based on function abstraction and application using variable binding and substitution. It is a universal model of computation that can be used to simulate any single-taped Turing machine and was first introduced by mathematician Alonzo Church in the 1930s as part of an investigation into the foundations of mathematics. Lambda calculus consists of constructing lambda terms and performing reduction operations on them.

Lambda calculus has applications in many different areas in mathematics, philosophy, linguistics, and computer science. Lambda calculus has played an important role in the development of the theory of programming languages. Functional programming languages implement the lambda calculus. Lambda calculus also is a current research topic in Category theory.


  • https://en.wikipedia.org/wiki/Church_encoding - a means of representing data and operators in the lambda calculus. The data and operators form a mathematical structure which is embedded in the lambda calculus. The Church numerals are a representation of the natural numbers using lambda notation. The method is named for Alonzo Church, who first encoded data in the lambda calculus this way.

Terms that are usually considered primitive in other notations (such as integers, booleans, pairs, lists, and tagged unions) are mapped to higher-order functions under Church encoding. The Church-Turing thesis asserts that any computable operator (and its operands) can be represented under Church encoding. In the untyped lambda calculus the only primitive data type is the function.


  • https://en.wikipedia.org/wiki/μ-recursive_function - a class of partial functions from natural numbers to natural numbers that are "computable" in an intuitive sense. In fact, in computability theory it is shown that the μ-recursive functions are precisely the functions that can be computed by Turing machines. The μ-recursive functions are closely related to primitive recursive functions, and their inductive definition (below) builds upon that of the primitive recursive functions. However, not every μ-recursive function is a primitive recursive function—the most famous example is the Ackermann function. Other equivalent classes of functions are the λ-recursive functions and the functions that can be computed by Markov algorithms. The set of all recursive functions is known as R in computational complexity theory.




  • PDF: A short introduction to the Lambda Calculus - Achim Jung, March 18, 2004. The lambda calculus can appear arcane on first encounter. Viewed purely as a “naming device”, however, it is a straighforward extension of ordinary mathematical notation.





  • https://en.wikipedia.org/wiki/SKI_combinator_calculus - a combinatory logic, a computational system that may be perceived as a reduced version of untyped lambda calculus. It can be thought of as a computer programming language, though it is not convenient for writing software. Instead, it is important in the mathematical theory of algorithms because it is an extremely simple Turing complete language. It was introduced by Moses Schönfinkel[1] and Haskell Curry. All operations in lambda calculus can be encoded via abstraction elimination into the SKI calculus as binary trees whose leaves are one of the three symbols S, K, and I (called combinators).



  • The pattern calculus - "There is a significant class of operations such as mapping that are common to all data structures. The goal of generic programming is to support these operations on arbitrary data types without having to recode for each new type. The pattern calculus and combinatory type system reach this goal by representing each data structure as a combination of names and a finite set of constructors. These can be used to define generic functions by pattern-matching programs in which each pattern has a different type. Evaluation is type-free. Polymorphism is captured by quantifying over type variables that represent unknown structures. A practical type inference algorithm is provided."


Typed




  • https://en.wikipedia.org/wiki/Lambda-mu_calculus - an extension of the lambda calculus introduced by M. Parigot. It introduces two new operators: the μ operator (which is completely different both from the μ operator found in computability theory and from the μ operator of modal μ-calculus) and the bracket operator. Semantically these operators correspond to continuations, found in some functional programming languages. Proof-theoretically, it provides a well-behaved formulation of classical natural deduction.

One of the main goals of this extended calculus is to be able to describe expressions corresponding to theorems in classical logic. According to the Curry–Howard isomorphism, lambda calculus on its own can express theorems in intuitionistic logic only, and several classical logical theorems can't be written at all. However with these new operators one is able to write terms that have the type of, for example, Peirce's law.


  • https://en.wikipedia.org/wiki/System_F - also known as the (Girard–Reynolds) polymorphic lambda calculus or the second-order lambda calculus, is a typed lambda calculus that differs from the simply typed lambda calculus by the introduction of a mechanism of universal quantification over types. System F thus formalizes the notion of parametric polymorphism in programming languages, and forms a theoretical basis for languages such as Haskell and ML. System F was discovered independently by logician Jean-Yves Girard (1972) and computer scientist John C. Reynolds (1974).

Whereas simply typed lambda calculus has variables ranging over functions, and binders for them, System F additionally has variables ranging over types, and binders for them. The upper-case Lambda character is traditionally used to denote type-level functions, as opposed to the lower-case lambda which is used for value-level functions.


  • https://en.wikipedia.org/wiki/System_U - System U and System U− are pure type systems, i.e. special forms of a typed lambda calculus with an arbitrary number of sorts, axioms and rules (or dependencies between the sorts). They were both proved inconsistent by Jean-Yves Girard in 1972. This result led to the realization that Martin-Löf's original 1971 type theory was inconsistent as it allowed the same "Type in Type" behaviour that Girard's paradox exploits.


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  • https://en.wikipedia.org/wiki/Process_calculus - or process algebras - are a diverse family of related approaches for formally modelling concurrent systems. Process calculi provide a tool for the high-level description of interactions, communications, and synchronizations between a collection of independent agents or processes. They also provide algebraic laws that allow process descriptions to be manipulated and analyzed, and permit formal reasoning about equivalences between processes (e.g., using bisimulation). Leading examples of process calculi include CSP, CCS, ACP, and LOTOS. More recent additions to the family include the π-calculus, the ambient calculus, PEPA, the fusion calculus and the join-calculus


Computational complexity




  • https://en.wikipedia.org/wiki/Time_complexity - the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input. The time complexity of an algorithm is commonly expressed using big O notation, which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity. For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n for any n (bigger than some n0), the asymptotic time complexity is O(n3).


  • https://en.wikipedia.org/wiki/Big_O_notation - a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Edmund Landau and Paul Bachmann, collectively called Bachmann-Landau notation or asymptotic notation.










  • https://en.wikipedia.org/wiki/Parameterized_complexity - a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output. The complexity of a problem is then measured as a function in those parameters. This allows the classification of NP-hard problems on a finer scale than in the classical setting, where the complexity of a problem is only measured by the number of bits in the input.









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  • https://en.wikipedia.org/wiki/Code_as_data - In computer science, the expressions code as data and data as code refer to the duality between code and data, that allows computers to treat instructions in a programming language as data handled by a running program.











  • https://en.wikipedia.org/wiki/Symbolic_execution - also symbolic evaluation, is a means of analyzing a program to determine what inputs cause each part of a program to execute. An interpreter follows the program, assuming symbolic values for inputs rather than obtaining actual inputs as normal execution of the program would, a case of abstract interpretation. It thus arrives at expressions in terms of those symbols for expressions and variables in the program, and constraints in terms of those symbols for the possible outcomes of each conditional branch.







Architecture

See also Electronics

  • https://www.youtube.com/watch?v=Tg5gJxXBh8s George Dyson on the Origins of the Digital Universe] - The talk focuses on the work done at The Institute for Advanced Study in Princeton New Jersey by such renowned scientists as John von Neumann and Kurt Godel.
  • https://en.wikipedia.org/wiki/Harvard_architecture - a computer architecture with physically separate storage and signal pathways for instructions and data. The term originated from the Harvard Mark I relay-based computer, which stored instructions on punched tape (24 bits wide) and data in electro-mechanical counters. These early machines had data storage entirely contained within the central processing unit, and provided no access to the instruction storage as data. Programs needed to be loaded by an operator; the processor could not initialize itself.


  • https://en.wikipedia.org/wiki/Von_Neumann_architecture - meaning has evolved to be any stored-program computer in which an instruction fetch and a data operation cannot occur at the same time because they share a common bus. This is referred to as the von Neumann bottleneck and often limits the performance of the system. The design of a von Neumann architecture is simpler than the more modern Harvard architecture which is also a stored-program system but has one dedicated set of address and data buses for reading data from and writing data to memory, and another set of address and data buses for fetching instructions.


  • https://en.wikipedia.org/wiki/Modified_Harvard_architecture - a variation of the Harvard computer architecture that allows the contents of the instruction memory to be accessed as if it were data. Most modern computers that are documented as Harvard architecture are, in fact, Modified Harvard architecture.


  • https://en.wikipedia.org/wiki/Flynn's_taxonomy - a classification of computer architectures, proposed by Michael Flynn in 1966. The classification system has stuck, and has been used as a tool in design of modern processors and their functionalities.


  • OpenCores - Since 1999, OpenCores is the most prominent online community for the development of gateware IP (Intellectual Properties) Cores. It is the place where such cores are shared and promoted in the spirit of Free and Open Source collaboration.
    • http://opencores.org/projects - main objective is to design and publish core designs under a license for hardware modeled on the Lesser General Public License (LGPL) for software. We are committed to the ideal of freely available, freely usable and re-usable open source hardware.

Instructions

  • https://en.wikipedia.org/wiki/Microarchitecture - sometimes abbreviated to µarch or uarch, also called computer organization, is the way a given instruction set architecture (ISA) is implemented on a processor. A given ISA may be implemented with different microarchitectures; implementations may vary due to different goals of a given design or due to shifts in technology. Computer architecture is the combination of microarchitecture and instruction set design.
  • https://en.wikipedia.org/wiki/Instruction_set - or instruction set architecture (ISA), is the part of the computer architecture related to programming, including the native data types, instructions, registers, addressing modes, memory architecture, interrupt and exception handling, and external I/O. An ISA includes a specification of the set of opcodes (machine language), and the native commands implemented by a particular processor.



  • https://en.wikipedia.org/wiki/Microcode - a layer of hardware-level instructions or data structures involved in the implementation of higher level machine code instructions in central processing units, and in the implementation of the internal logic of many channel controllers, disk controllers, network interface controllers, network processors, graphics processing units, and other hardware. It resides in special high-speed memory and translates machine instructions into sequences of detailed circuit-level operations. It helps separate the machine instructions from the underlying electronics so that instructions can be designed and altered more freely. It also makes it feasible to build complex multi-step instructions while still reducing the complexity of the electronic circuitry compared to other methods. Writing microcode is often called microprogramming and the microcode in a particular processor implementation is sometimes called a microprogram.




  • https://en.wikipedia.org/wiki/Machine_code - a set of instructions executed directly by a computer's central processing unit (CPU). Each instruction performs a very specific task, such as a load, a jump, or an ALU operation on a unit of data in a CPU register or memory. Every program directly executed by a CPU is made up of a series of such instructions. Numerical machine code (i.e., not assembly code) may be regarded as the lowest-level representation of a compiled or assembled computer program or as a primitive and hardware-dependent programming language. While it is possible to write programs directly in numerical machine code, it is tedious and error prone to manage individual bits and calculate numerical addresses and constants manually. It is thus rarely done today, except for situations that require extreme optimization or debugging.
  • https://en.wikipedia.org/wiki/Word_(computer_architecture) - term for the natural unit of data used by a particular processor design. A word is basically a fixed-sized group of digits (binary or decimal) that are handled as a unit by the instruction set or the hardware of the processor. The number of digits in a word (the word size, word width, or word length) is an important characteristic of any specific processor design or computer architecture.
  • https://en.wikipedia.org/wiki/Operand#Computer_science - the part of a computer instruction which specifies what data is to be manipulated or operated on, while at the same time representing the data itself. A computer instruction describes an operation such as add or multiply X, while the operand (or operands, as there can be more than one) specify on which X to operate as well as the value of X. Additionally, in assembly language, an operand is a value (an argument) on which the instruction, named by mnemonic, operates. The operand may be a processor register, a memory address, a literal constant, or a label.


  • https://en.wikipedia.org/wiki/Bytecode - also known as p-code (portable code), is a form of instruction set designed for efficient execution by a software interpreter. Unlike human-readable source code, bytecodes are compact numeric codes, constants, and references (normally numeric addresses) which encode the result of parsing and semantic analysis of things like type, scope, and nesting depths of program objects. They therefore allow much better performance than direct interpretation of source code.

"fundamentally the same as machine code, in that it describes low level operations such as reading and writing memory, and basic calculations. Bytecode is typically conceived to be produced when COMPILING a higher level language, for example PHP or Java, and unlike machine code for many hardware based processors, may have operations to support specific features of the higher level language. A key difference is that the processor of bytecode is usually a program, though processors have been created for interpreting some bytecode specifications, e.g. a processor called SOAR (Smalltalk On A RISC) for Smalltalk bytecode. While you wouldn't typically call native machine code bytecode, for some types of processors such as CISC and EISC (e.g. Linn Rekursiv, from the people who made record players), the processor itself contains a program that is interpreting the machine instructions, so there are parallels." [47]

Assembly



  • Rappel - assembly REPL. It works by creating a shell ELF, starting it under ptrace, then continiously rewriting/running the .text section, while showing the register states. It's maybe half done right now, and supports Linux x86, amd64, and armv7 (no thumb) at the moment. [50]



https://github.com/osandov/asmase - a REPL for assembly language using an LLVM backend. It provides an accurate interactive environment for several architectures.


  • https://en.wikipedia.org/wiki/Disassembler - a computer program that translates machine language into assembly language—the inverse operation to that of an assembler. A disassembler differs from a decompiler, which targets a high-level language rather than an assembly language. Disassembly, the output of a disassembler, is often formatted for human-readability rather than suitability for input to an assembler, making it principally a reverse-engineering tool.



  • yasp - a fully functional web-based assembler development environment, including a real assembler, emulator and debugger. The assembler dialect is custom and very simple so as to keep the learning curve as shallow as possible. It also features some hardware-elements (LED, Potentiometer, Button, etc.). The goal of this project is to create an environment in which students can learn the assembly language so that they understand computers better. Furthermore it allows them to experiment without the fear of breaking something. The original project team of yasp consists of Robert Fischer and Michael "luto" Lutonsky. For more information take a look at the about-section in the IDEs menu. [57]


  • Easy 6502 - how to get started writing 6502 assembly language. The 6502 processor was massive in the seventies and eighties, powering famous computers like the BBC Micro, Atari 2600, Commodore 64, Apple II, and the Nintendo Entertainment System. Bender in Futurama has a 6502 processor for a brain. Even the Terminator was programmed in 6502.




Processing



  • https://en.wikipedia.org/wiki/Voltage_regulator_module - VRM), sometimes called processor power module (PPM), is a buck converter that provides a microprocessor the appropriate supply voltage, converting +5 V or +12 V to a much lower voltage required by the CPU, allowing processors with different supply voltage to be mounted on the same motherboard.


CPU







  • https://en.wikipedia.org/wiki/Control_unit - a component of a computer's central processing unit (CPU) that directs operation of the processor. It controls communication and co-ordination between input/output devices. It reads and interprets instructions and determines the sequence for processing the data.








  • https://en.wikipedia.org/wiki/Cache_coherence - the uniformity of shared resource data that ends up stored in multiple local caches. When clients in a system maintain caches of a common memory resource, problems may arise with incoherent data, which is particularly the case with CPUs in a multiprocessing system. In the illustration on the right, consider both the clients have a cached copy of a particular memory block from a previous read. Suppose the client on the bottom updates/changes that memory block, the client on the top could be left with an invalid cache of memory without any notification of the change. Cache coherence is intended to manage such conflicts by maintaining a coherent view of the data values in multiple caches.




  • https://en.wikipedia.org/wiki/NX_bit - stands for No-eXecute, a technology used in CPUs to segregate areas of memory for use by either storage of processor instructions (code) or for storage of data, a feature normally only found in Harvard architecture processors. However, the NX bit is being increasingly used in conventional von Neumann architecture processors, for security reasons.



  • https://en.wikipedia.org/wiki/Branch_predication - a strategy in computer architecture design for mitigating the costs usually associated with conditional branches, particularly branches to short sections of code. It does this by allowing each instruction to conditionally either perform an operation or do nothing.









  • https://en.wikipedia.org/wiki/Processor_affinity - or CPU pinning or "cache affinity", enables the binding and unbinding of a process or a thread to a central processing unit (CPU) or a range of CPUs, so that the process or thread will execute only on the designated CPU or CPUs rather than any CPU. This can be viewed as a modification of the native central queue scheduling algorithm in a symmetric multiprocessing operating system. Each item in the queue has a tag indicating its kin processor. At the time of resource allocation, each task is allocated to its kin processor in preference to others.

Microprocessor

Coprocessor

  • https://en.wikipedia.org/wiki/Coprocessor - a computer processor used to supplement the functions of the primary processor (the CPU). Operations performed by the coprocessor may be floating point arithmetic, graphics, signal processing, string processing, encryption or I/O Interfacing with peripheral devices. By offloading processor-intensive tasks from the main processor, coprocessors can accelerate system performance. Coprocessors allow a line of computers to be customized, so that customers who do not need the extra performance need not pay for it.


  • https://en.wikipedia.org/wiki/Floating-point_unit - FPU, colloquially a math coprocessor) is a part of a computer system specially designed to carry out operations on floating point numbers. Typical operations are addition, subtraction, multiplication, division, square root, bitshifting. Some systems (particularly older, microcode-based architectures) can also perform various transcendental functions such as exponential or trigonometric calculations, though in most modern processors these are done with software library routines. In a general purpose computer architectures, one or more FPUs may be integrated with the central processing unit; however many embedded processors do not have hardware support for floating-point operations.


  • https://en.wikipedia.org/wiki/Controller_(computing) - a chip, an expansion card, or a stand-alone device that interfaces with a peripheral device. This may be a link between two parts of a computer (for example a memory controller that manages access to memory for the computer) or a controller on an external device that manages the operation of (and connection with) that device.

The term is sometimes used in the opposite sense to refer to a device by which the user controls the operation of the computer, as in game controller. In desktop computers the controller may be a plug in board, a single integrated circuit on the motherboard, or an external device. In mainframes the controller is usually either a separate device attached to a channel or integrated into the peripheral.




  • https://en.wikipedia.org/wiki/Compute_kernel - a routine compiled for high throughput accelerators (such as GPUs), DSPs or FPGAs, separate from (but used by) a main program. They are sometimes called compute shaders, sharing execution units with vertex shaders and pixel shaders on GPUs, but are not limited to execution on one class of device, or graphics APIs.

DSP


FPGA


CISC

x86






RISC

MIPS


SPARC
Power


Alpha
ARM
RISC-V
  • lowRISC - producing fully open hardware systems. From the processor core to the development board, our goal is to create a completely open computing eco-system. Our open-source SoC (System-on-a-Chip) designs will be based on the 64-bit RISC-V instruction set architecture. Volume silicon manufacture is planned as is a low-cost development board. There are more details on our plans in these slides from a recent talk [82] [83]



  • PULP Platform - What you will get is a competitive, state-of-the-art 32-bit processor based on the RISC-V architecture, with a rich set of peripherals, and full debug support. At ETH Zurich and Universita’ di Bologna we have put many of the ideas that we have developed through our research on ultra-low-power parallel processing (PULP project) into PULPino. It is the little hip brother to its more serious bigger brothers.





Other





GPU





TPU

  • https://en.wikipedia.org/wiki/Tensor_processing_unit - are application-specific integrated circuits (ASIC) developed specifically for machine learning. Compared to graphics processing units (which as of 2016 are frequently used for the same tasks), they are designed explicitly for a higher volume of reduced precision computation with higher IOPS per watt (e.g. as little as 8-bit precision), and lack hardware for rasterisation/texture mapping. The chip has been specifically designed for Google's TensorFlow framework, however Google still uses CPUs and GPUs for other machine learning. Other AI accelerator designs are appearing from other vendors also and are aimed at embedded and robotics markets.


The Mill


Parallel computing




  • https://en.wikipedia.org/wiki/SIMD - Single instruction, multiple data (SIMD) is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. Such machines exploit data level parallelism, but not concurrency: there are simultaneous (parallel) computations, but only a single process (instruction) at a given moment. SIMD is particularly applicable to common tasks such as adjusting the contrast in a digital image or adjusting the volume of digital audio. Most modern CPU designs include SIMD instructions to improve the performance of multimedia use. Not to be confused with SIMT which utilizes threads.





GPGPU





CUDA

  • https://en.wikipedia.org/wiki/CUDA - (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenACC and OpenCL; and HIP by compiling such code to CUDA. When CUDA was first introduced by Nvidia, the name was an acronym for Compute Unified Device Architecture, but Nvidia subsequently dropped the common use of the acronym.




OpenCL

  • https://en.wikipedia.org/wiki/OpenCL - a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators. OpenCL specifies programming languages (based on C99 and C++11) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. OpenCL provides a standard interface for parallel computing using task- and data-based parallelism.



  • https://en.wikipedia.org/wiki/SYCL - a higher-level programming model for OpenCL as a single-source domain specific embedded language (DSEL) based on pure C++11 for SYCL 1.2.1 to improve programming productivity. This is a standard developed by Khronos Group, announced in March 2014.


ROCm

  • ROCm - the first open-source HPC/Hyperscale-class platform for GPU computing that’s also programming-language independent. We are bringing the UNIX philosophy of choice, minimalism and modular software development to GPU computing. The new ROCm foundation lets you choose or even develop tools and a language run time for your application.ROCm is built for scale; it supports multi-GPU computing in and out of server-node communication through RDMA. It also simplifies the stack when the driver directly incorporates RDMA peer-sync support.ROCm has a rich system run time with the critical features that large-scale application, compiler and language-run-time development requires.HSA Compliant Runtime and Driver for AMD RADEON GPU’s

Microcontroller

  • https://en.wikipedia.org/wiki/Microcontroller - a small computer (SoC) on a single integrated circuit containing a processor core, memory, and programmable input/output peripherals. Program memory in the form of Ferroelectric RAM, NOR flash or OTP ROM is also often included on chip, as well as a typically small amount of RAM. Microcontrollers are designed for embedded applications, in contrast to the microprocessors used in personal computers or other general purpose applications consisting of various discrete chips.






Memory



There are four major storage levels:

  1. Internal – Processor registers and cache.
  2. Main – the system RAM and controller cards.
  3. On-line mass storage – Secondary storage.
  4. Off-line bulk storage – Tertiary and Off-line storage.




  • https://en.wikipedia.org/wiki/NVDIMM NVDIMM - a type of random-access memory for computers. Non-volatile memory is memory that retains its contents even when electrical power is removed, for example from an unexpected power loss, system crash, or normal shutdown. "Dual in-line" identifies the memory as using the DIMM package. NVDIMMs improve application performance and system crash recovery time. They enhance solid-state drive (SSD) endurance and reliability. "Non-volatile" products may use volatile memory during normal operation and dump the contents into non-volatile memory if the power fails, using an on-board backup power source. Volatile memory is faster than non-volatile; it is byte-addressable; and it can be written to arbitrarily, without concerns about wear and device lifespan. However, including a second memory to achieve non-volatility (and the on-board backup power source) increases the product cost compared to volatile memory.






  • https://en.wikipedia.org/wiki/Memory_rank - a set of DRAM chips connected to the same chip select, which are therefore accessed simultaneously. In practice all DRAM chips share all of the other command and control signals, and only the chip select pins for each rank are separate (the data pins are shared across ranks).



  • https://en.wikipedia.org/wiki/Address_space - defines a range of discrete addresses, each of which may correspond to a network host, peripheral device, disk sector, a memory cell or other logical or physical entity.





  • https://en.wikipedia.org/wiki/Bank_switching - a technique used in computer design to increase the amount of usable memory beyond the amount directly addressable by the processor. It can be used to configure a system differently at different times; for example, a ROM required to start a system from diskette could be switched out when no longer needed.


  • https://en.wikipedia.org/wiki/Slab_allocation - a memory management mechanism intended for the efficient memory allocation of kernel objects. It eliminates fragmentation caused by allocations and deallocations. The technique is used to retain allocated memory that contains a data object of a certain type for reuse upon subsequent allocations of objects of the same type. It is analogous to an object pool, but only applies to memory, not other resources. Slab allocation was first introduced in the Solaris 5.4 kernel by Jeff Bonwick. It is now widely used by many Unix and Unix-like operating systems including FreeBSD and Linux.



  • https://en.wikipedia.org/wiki/Executable_space_protection - the marking of memory regions as non-executable, such that an attempt to execute machine code in these regions will cause an exception. It makes use of hardware features such as the NX bit, or in some cases software emulation of those features.


  • https://en.wikipedia.org/wiki/Virtual_memory - a memory management technique that is implemented using both hardware and software. It maps memory addresses used by a program, called virtual addresses, into physical addresses in computer memory.


  • https://en.wikipedia.org/wiki/Page_(computer_memory) - memory page, or virtual page is a fixed-length contiguous block of virtual memory, described by a single entry in the page table. It is the smallest unit of data for memory management in a virtual memory operating system.
  • https://en.wikipedia.org/wiki/Page_table - the data structure used by a virtual memory system in a computer operating system to store the mapping between virtual addresses and physical addresses. Virtual addresses are used by the accessing process, while physical addresses are used by the hardware, or more specifically, by the RAM subsystem.
  • https://en.wikipedia.org/wiki/Page_fault - a type of interrupt, called trap, raised by computer hardware when a running program accesses a memory page that is mapped into the virtual address space, but not actually loaded into main memory. When handling a page fault, the operating system generally tries to make the required page accessible at the location in physical memory, or terminates the program in case of an illegal memory access. Contrary to what "fault" might suggest, valid page/hard faults are not errors, and are common and necessary to increase the amount of memory available to programs in any operating system that utilizes virtual memory,


  • http://en.wikipedia.org/wiki/Call_stack - a stack data structure that stores information about the active subroutines of a computer program. This kind of stack is also known as an execution stack, control stack, run-time stack, or machine stack, and is often shortened to just "the stack". Although maintenance of the call stack is important for the proper functioning of most software, the details are normally hidden and automatic in high-level programming languages. Many computer instruction sets provide special instructions for manipulating stacks.

A call stack is used for several related purposes, but the main reason for having one is to keep track of the point to which each active subroutine should return control when it finishes executing.


  • https://en.wikipedia.org/wiki/Non-uniform_memory_access - a computer memory design used in multiprocessing, where the memory access time depends on the memory location relative to the processor. Under NUMA, a processor can access its own local memory faster than non-local memory (memory local to another processor or memory shared between processors). The benefits of NUMA are limited to particular workloads, notably on servers where the data are often associated strongly with certain tasks or users.


  • http://en.wikipedia.org/wiki/Pointer_(computer_programming) - a programming language object, whose value refers to (or "points to") another value stored elsewhere in the computer memory using its address. A pointer references a location in memory, and obtaining the value stored at that location is known as dereferencing the pointer.




Storage


Other


Security



ASIC



Interrupts

  • https://en.wikipedia.org/wiki/Interrupt - a signal to the processor emitted by hardware or software indicating an event that needs immediate attention. An interrupt alerts the processor to a high-priority condition requiring the interruption of the current code the processor is executing. The processor responds by suspending its current activities, saving its state, and executing a function called an interrupt handler (or an interrupt service routine, ISR) to deal with the event. This interruption is temporary, and, after the interrupt handler finishes, the processor resumes normal activities.


  • https://en.wikipedia.org/wiki/Interrupt_request_(PC_architecture) - or IRQ, is a hardware signal sent to the processor that temporarily stops a running program and allows a special program, an interrupt handler, to run instead. Hardware interrupts are used to handle events such as receiving data from a modem or network card, key presses, or mouse movements.

Interrupt lines are often identified by an index with the format of IRQ followed by a number. For example, on the Intel 8259 family of PICs there are eight interrupt inputs commonly referred to as IRQ0 through IRQ7. In x86 based computer systems that use two of these PICs, the combined set of lines are referred to as IRQ0 through IRQ15. Technically these lines are named IR0 through IR7, and the lines on the ISA bus to which they were historically attached are named IRQ0 through IRQ15. Newer x86 systems integrate an Advanced Programmable Interrupt Controller (APIC) that conforms to the Intel APIC Architecture. These APICs support a programming interface for up to 255 physical hardware IRQ lines per APIC, with a typical system implementing support for only around 24 total hardware lines.

  • https://en.wikipedia.org/wiki/Interrupt_handler - also known as an interrupt service routine or ISR, a callback function in microcontroller firmware, an operating system or a device driver, whose execution is triggered by the reception of an interrupt. In general, interrupts and their handlers are used to handle high-priority conditions that require the interruption of the current code the processor is executing.

Interrupt handlers have a multitude of functions, which vary based on the reason the interrupt was generated and the speed at which the interrupt handler completes its task. For example, pressing a key on a computer keyboard, or moving the mouse, triggers interrupts that call interrupt handlers which read the key, or the mouse's position, and copy the associated information into the computer's memory. An interrupt handler is a low-level counterpart of event handlers. These handlers are initiated by either hardware interrupts or interrupt instructions in software, and are used for servicing hardware devices and transitions between protected modes of operation such as system calls.


  • https://en.wikipedia.org/wiki/Exception_handling - the process of responding to the occurrence, during computation, of exceptions – anomalous or exceptional conditions requiring special processing – often changing the normal flow of program execution. It is provided by specialized programming language constructs or computer hardware mechanisms.


  • https://en.wikipedia.org/wiki/Message_Signaled_Interrupts - an alternative in-band method of signaling an interrupt, using special in-band messages to replace traditional out-of-band assertion of dedicated interrupt lines. While more complex to implement in a device, message signaled interrupts have some significant advantages over pin-based out-of-band interrupt signaling. Message signaled interrupts are supported in PCI bus since its version 2.2, and in later available PCI Express bus. Some non-PCI architectures also use message signaled interrupts.


  • https://github.com/Irqbalance/irqbalance - a daemon to help balance the cpu load generated by interrupts across all of a systems cpus. Irqbalance identifies the highest volume interrupt sources, and isolates them to a single unique cpu, so that load is spread as much as possible over an entire processor set, while minimizing cache miss rates for irq handlers.


Runtime






  • https://en.wikipedia.org/wiki/Green_threads - threads that are scheduled by a runtime library or virtual machine (VM) instead of natively by the underlying operating system. Green threads emulate multithreaded environments without relying on any native OS capabilities, and they are managed in user space instead of kernel space, enabling them to work in environments that do not have native thread support.

Programming

See also Languages, Development, Maths#Software 2







  • Programming from the Ground Up Book - an introductory book to programming and computer science using assembly language. It assumes the reader has never programmed before, and introduces the concepts of variables, functions, and flow control. The reason for using assembly language is to get the reader thinking in terms of how the computer actually works underneath. Knowing how the computer works from a "bare-metal" standpoint is often the difference between top-level programmers and programmers who can never quite master their art. [104]


  • https://en.wikipedia.org/wiki/Dynamic_programming_language - a term used in computer science to describe a class of high-level programming languages which, at runtime, execute many common programming behaviors that static programming languages perform during compilation. These behaviors could include extension of the program, by adding new code, by extending objects and definitions, or by modifying the type system. Although similar behaviours can be emulated in nearly any language, with varying degrees of difficulty, complexity and performance costs, dynamic languages provide direct tools to make use of them. Many of these features were first implemented as native features in the Lisp programming language. Most dynamic languages are also dynamically typed, but not all are. Dynamic languages are frequently (but not always) referred to as "scripting languages", although the term "scripting language" in its narrowest sense refers to languages specific to a given run-time environment.



  • Livecoding.tv - A livestreaming platform for coders to share their code and hang out



Semantics describe the logical entities of a programming language and their interactions. Syntax defines how these are expressed in characters.





  • The Three Virtues of a GREAT Programmer - According to Larry Wall(1), the original author of the Perl programming language, there are three great virtues of a programmer; Laziness, Impatience and Hubris
  • Laziness: The quality that makes you go to great effort to reduce overall energy expenditure. It makes you write labor-saving programs that other people will find useful and document what you wrote so you don't have to answer so many questions about it.
  • Impatience: The anger you feel when the computer is being lazy. This makes you write programs that don't just react to your needs, but actually anticipate them. Or at least pretend to.
  • Hubris: The quality that makes you write (and maintain) programs that other people won't want to say bad things about.
  • 2nd State of the Onion - "Most of you are familiar with the virtues of a programmer. There are three, of course: laziness, impatience, and hubris. These are virtues of passion. They are not, however, virtues of community. The virtues of community sound like their opposites: diligence, patience, and humility. They're not really opposites, because you can do them all at the same time. It's another matter of perspective. These are the virtues that have brought us this far. These are the virtues that will carry our community into the future, if we do not abandon them."



Syntax


  • https://en.wikipedia.org/wiki/Lexical_grammar - the process of converting a sequence of characters (such as in a computer program or web page) into a sequence of tokens (strings with an identified "meaning"). A program that performs lexical analysis may be called a lexer, tokenizer, or scanner (though "scanner" is also used to refer to the first stage of a lexer). Such a lexer is generally combined with a parser, which together analyze the syntax of programming languages, web pages, and so forth.

For instance, the lexical grammar for many programming languages specifies that a string literal starts with a " character and continues until a matching " is found (escaping makes this more complicated), that an identifier is an alphanumeric sequence (letters and digits, usually also allowing underscores, and disallowing initial digits), and that an integer literal is a sequence of digits. So in the following character sequence "abc" xyz1 23 the tokens are string, identifier and number (plus whitespace tokens) because the space character terminates the sequence of characters forming the identifier. Further, certain sequences are categorized as keywords – these generally have the same form as identifiers (usually alphabetical words), but are categorized separately; formally they have a different token type.

  • https://en.wikipedia.org/wiki/Lexical_analysis - the process of converting a sequence of characters (such as in a computer program or web page) into a sequence of tokens (strings with an identified "meaning"). A program that performs lexical analysis may be called a lexer, tokenizer, or scanner (though "scanner" is also used to refer to the first stage of a lexer). Such a lexer is generally combined with a parser, which together analyze the syntax of programming languages, web pages, and so forth.


  • https://en.wikipedia.org/wiki/Language_construct - a syntactically allowable part of a program that may be formed from one or more lexical tokens in accordance with the rules of a programming language. The term "language construct" is often used as a synonym for control structure, and should not be confused with a function.


  • https://en.wikipedia.org/wiki/Syntactic_noise - syntax within a programming language that makes the programming language more difficult to read and understand for humans. It fills the language with excessive clutter that makes it a hassle to write code. Syntactic noise is considered to be the opposite of syntactic sugar, which is syntax that makes a programming language more readable and enjoyable for the programmer.


  • https://en.wikipedia.org/wiki/Catalan_number - a sequence of natural numbers that occur in various counting problems, often involving recursively-defined objects. They are named after the Belgian mathematician Eugène Charles Catalan (1814–1894).

Using zero-based numbering, the nth Catalan number is given directly in terms of binomial coefficients. The first Catalan numbers for n = 0, 1, 2, 3, … are: 1, 1, 2, 5, 14, 42, 132, 429, 1430, 4862, 16796, 58786, 208012, 742900, 2674440, 9694845, 35357670, 129644790, 477638700, 1767263190, 6564120420, 24466267020, 91482563640, 343059613650, 1289904147324, 4861946401452, … (sequence A000108 in OEIS).


  • https://en.wikipedia.org/wiki/Dyck_language - the language consisting of balanced strings of square brackets [ and ]. It is important in the parsing of expressions that must have a correctly nested sequence of brackets, such as arithmetic or algebraic expressions.



  • https://en.wikipedia.org/wiki/Reserved_word - also known as a reserved identifier is a word that cannot be used as an identifier, such as the name of a variable, function, or label – it is "reserved from use". This is a syntactic definition, and a reserved word may have no meaning.

A closely related and often conflated notion is a keyword which is a word with special meaning in a particular context. This is a semantic definition. By contrast, names in a standard library but not built into the language are not considered reserved words or keywords. The terms "reserved word" and "keyword" are often used interchangeably – one may say that a reserved word is "reserved for use as a keyword" – and formal use varies from language to language; for this article we distinguish as above.

In general reserved words and keywords need not coincide, but in most modern languages keywords are a subset of reserved words, as this makes parsing easier, since keywords cannot be confused with identifiers. In some languages, like C or Python, reserved words and keywords coincide, while in other languages, like Java, all keywords are reserved words, but some reserved words are not keywords – these are "reserved for future use". In yet other languages, such as ALGOL and PL/I there are keywords but no reserved words, with keywords being distinguished from identifiers by other means.




  • https://en.wikipedia.org/wiki/Declaration_(computer_programming) - specifies properties of an identifier: it declares what a word (identifier) means. Declarations are most commonly used for functions, variables, constants, and classes, but can also be used for other entities such as enumerations and type definitions. Beyond the name (the identifier itself) and the kind of entity (function, variable, etc.), declarations typically specify the data type (for variables and constants), or the type signature (for functions); types may also include dimensions, such as for arrays. A declaration is used to announce the existence of the entity to the compiler; this is important in those strongly typed languages that require functions, variables, and constants, and their types to be specified with a declaration before use, and is used in forward declaration. The term "declaration" is frequently contrasted with the term "definition", but meaning and usage varies significantly between languages
  • https://en.wikipedia.org/wiki/Initialization_(programming) - the assignment of an initial value for a data object or variable. The manner in which initialization is performed depends on programming language, as well as type, storage class, etc., of an object to be initialized. Programming constructs which perform initialization are typically called initializers and initializer lists. Initialization is distinct from (and preceded by) declaration, although the two can sometimes be conflated in practice. The complement of initialization is finalization, which is primarily used for objects, but not variables. Initialization is done either by statically embedding the value at compile time, or else by assignment at run time.
  • https://en.wikipedia.org/wiki/Finalization - the process of preparing an object for deallocation; strictly speaking, finalization is all parts of object destruction until memory deallocation itself. Finalization is formally complementary to initialization, which is the part of object creation that follows allocation, but differs significantly in practice


  • https://en.wikipedia.org/wiki/Directive_(programming) - a language construct that specifies how a compiler (or assembler or interpreter) should process its input. Directives are not part of the language proper – they are not part of the grammar, and may vary from compiler to compiler – but instead function either as an in-band form of a command-line option, specifying compiler behavior, or are processed by a preprocessor. In some cases directives specify global behavior, while in other cases they only affect a local section, such as a block of programming code. In some cases, such as some C pragmas, directives are optional compiler hints, and may be ignored, but normally they are prescriptive, and must be followed. However, a directive does not perform any action in the language itself, but rather only a change in the behavior of the compiler.

This term could be used to refer to proprietary third party tags and commands (or markup) embedded in code that result in additional executable processing that extend the existing compiler, assembler and language constructs present in the development environment. The term "directive" is also applied in a variety of ways that are similar to the term command.


  • https://en.wikipedia.org/wiki/First-class_citizen - In programming language design, a first-class citizen (also type, object, entity, or value) in a given programming language is an entity which supports all the operations generally available to other entities. These operations typically include being passed as an argument, returned from a function, and assigned to a variable.



  • https://en.wikipedia.org/wiki/Indentation_style - a convention governing the indentation of blocks of code to convey the program's structure. This article largely addresses the free-form languages, such as C programming language and its descendants, but can be (and frequently is) applied to most other programming languages (especially those in the curly bracket family), where whitespace is otherwise insignificant. Indent style is just one aspect of programming style.


  • https://en.wikipedia.org/wiki/Syntactic_sugar - syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use: things can be expressed more clearly, more concisely, or in an alternative style that some may prefer.

For example, many programming languages provide special syntax for referencing and updating array elements. Abstractly, an array reference is a procedure of two arguments: an array and a subscript vector, which could be expressed as get_array(Array, vector(i,j)). Instead, many languages provide syntax like Array[i,j]. Similarly an array element update is a procedure of three arguments, something like set_array(Array, vector(i,j), value), but many languages provide syntax like Array[i,j] = value.

Specifically, a construct in a language is called syntactic sugar if it can be removed from the language without any effect on what the language can do: functionality and expressive power will remain the same. Language processors, including compilers, static analyzers, and the like, often expand sugared constructs into more fundamental constructs before processing, a process sometimes called "desugaring".




  • https://en.wikipedia.org/wiki/Backus–Naur_Form - BNF (Backus Normal Form or Backus–Naur Form) is one of the two main notation techniques for context-free grammars, often used to describe the syntax of languages used in computing, such as computer programming languages, document formats, instruction sets and communication protocols; the other main technique for writing context-free grammars is the van Wijngaarden form. They are applied wherever exact descriptions of languages are needed: for instance, in official language specifications, in manuals, and in textbooks on programming language theory. Many extensions and variants of the original Backus–Naur notation are used; some are exactly defined, including Extended Backus–Naur Form (EBNF) and Augmented Backus–Naur Form (ABNF).


  • https://en.wikipedia.org/wiki/Parsing_expression_grammar - or PEG, is a type of analytic formal grammar, i.e. it describes a formal language in terms of a set of rules for recognizing strings in the language. The formalism was introduced by Bryan Ford in 2004 and is closely related to the family of top-down parsing languages introduced in the early 1970s. Syntactically, PEGs also look similar to context-free grammars (CFGs), but they have a different interpretation: the choice operator selects the first match in PEG, while it is ambiguous in CFG. This is closer to how string recognition tends to be done in practice, e.g. by a recursive descent parser. Unlike CFGs, PEGs cannot be ambiguous; if a string parses, it has exactly one valid parse tree. It is conjectured that there exist context-free languages that cannot be parsed by a PEG, but this is not yet proven. PEGs are well-suited to parsing computer languages, but not natural languages where their performance is comparable to general CFG algorithms such as the Earley algorithm

Compared to pure regular expressions (i.e. without back-references), PEGs are strictly more powerful, but require significantly more memory. For example, a regular expression inherently cannot find an arbitrary number of matched pairs of parentheses, because it is not recursive, but a PEG can. However, a PEG will require an amount of memory proportional to the length of the input, while a regular expression matcher will require only a constant amount of memory.


Values and types

  • https://en.wikipedia.org/wiki/Value_(computer_science) - an expression which cannot be evaluated any further (a normal form). The members of a type are the values of that type. For example, the expression 1 + 2 is not a value as it can be reduced to the expression 3. This expression cannot be reduced any further (and is a member of the type Nat) and therefore is a value. The "value of a variable" is given by the corresponding mapping in the environment. In languages with assignable variables it becomes necessary to distinguish between the r-value (or contents) and the l-value (or location) of a variable. In declarative (high-level) languages, values have to be referentially transparent. This means that the resulting value is independent of the location in which a (sub-)expression needed to compute the value is stored. Only the contents of the location (the bits, whether they are 1 or 0) and their interpretation are significant.



  • https://en.wikipedia.org/wiki/Data_type - simply type is a classification identifying one of various types of data, such as real, integer or Boolean, that determines the possible values for that type; the operations that can be done on values of that type; the meaning of the data; and the way values of that type can be stored.


  • https://en.wikipedia.org/wiki/Type_theory - any of a class of formal systems, some of which can serve as alternatives to set theory as a foundation for all mathematics. In type theory, every "term" has a "type" and operations are restricted to terms of a certain type.

Type systems


  • https://en.wikipedia.org/wiki/Type_system - a collection of rules that assign a property called a type to constructs such as variables, expressions, functions or modules—a computer program is composed of. reduces bugs by defining interfaces between different parts of a computer program, and then checking that the parts have been connected in a consistent way. This checking can happen statically (at compile time), dynamically (at run time), or as a combination thereof.

Type systems have other purposes as well, such as enabling certain compiler optimizations, allowing for multiple dispatch, providing a form of documentation, etc. A type system associates a type with each computed value and, by examining the flow of these values, attempts to ensure or prove that no type errors can occur. The particular type system in question determines exactly what constitutes a type error, but in general the aim is to prevent operations expecting a certain kind of value from being used with values for which that operation does not make sense (logic errors); memory errors will also be prevented. Type systems are often specified as part of programming languages, and built into the interpreters and compilers for them; although the type system of a language can be extended by optional tools that perform additional kinds of checks using the language's original type syntax and grammar.







  • https://en.wikipedia.org/wiki/Type_conversion - typecasting, and coercion are different ways of, implicitly or explicitly, changing an entity of one data type into another. This is done to take advantage of certain features of type hierarchies or type representations. One example would be small integers, which can be stored in a compact format and converted to a larger representation when used in arithmetic computations. In object-oriented programming, type conversion allows programs to treat objects of one type as one of their ancestor types to simplify interacting with them.

Type aliasing provides a way to redefine existing types as new type names. For example, type aliases may be used to define names for object types, effectively modeling interface types.


  • https://en.wikipedia.org/wiki/Type_inference - automatic deduction of the type of an expression in a programming language. If some, but not all, type annotations are already present it is referred to as type reconstruction. The opposite operation of type inference is called type erasure.


  • https://en.wikipedia.org/wiki/Strong_and_weak_typing - in general, a strongly typed language is more likely to generate an error or refuse to compile if the argument passed to a function does not closely match the expected type. On the other hand, a very weakly typed language may produce unpredictable results or may perform implicit type conversion. A different but related concept is latent typing.



  • https://en.wikipedia.org/wiki/Structural_type_system - a major class of type system, in which type compatibility and equivalence are determined by the type's actual structure or definition, and not by other characteristics such as its name or place of declaration. Structural systems are used to determine if types are equivalent and whether a type is a subtype of another. It contrasts with nominative systems, where comparisons are based on the names of the types or explicit declarations, and duck typing, in which only the part of the structure accessed at runtime is checked for compatibility.
  • https://en.wikipedia.org/wiki/Nominative_type_system - or nominative type system (or name-based type system) is a major class of type system, in which compatibility and equivalence of data types is determined by explicit declarations and/or the name of the types. Nominal systems are used to determine if types are equivalent, as well as if a type is a subtype of another. It contrasts with structural systems, where comparisons are based on the structure of the types in question and do not require explicit declarations.


  • https://en.wikipedia.org/wiki/Automatic_variable - a local variable which is allocated and deallocated automatically when program flow enters and leaves the variable's scope. The scope is the lexical context, particularly the function or block in which a variable is defined. Local data is typically (in most languages) invisible outside the function or lexical context where it is defined. Local data is also invisible and inaccessible to a called function,[note 1] but is not deallocated, coming back in scope as the execution thread returns to the caller.


  • https://en.wikipedia.org/wiki/Principal_type - In type theory, a type system is said to have the principal type property if, given a term and an environment, there exists a principal type for this term in this environment, i.e. a type such that all other types for this term in this environment are an instance of the principal type.
  • https://en.wikipedia.org/wiki/Duck_typing - style of typing in which an object's methods and properties determine the valid semantics, rather than its inheritance from a particular class or implementation of a specific interface


  • https://en.wikipedia.org/wiki/Substructural_type_system - family of type systems analogous to substructural logics where one or more of the structural rules are absent or allowed under controlled circumstances. Such systems are useful for constraining access to system resources such as files, locks and memory by keeping track of changes of state that occur and preventing invalid states


  • https://en.wikipedia.org/wiki/Kind_%28type_theory%29 - the type of a type constructor or, less commonly, the type of a higher-order type operator. A kind system is essentially a simply typed lambda calculus "one level up", endowed with a primitive type, denoted * and called "type", which is the kind of any data type which does not need any type parameters.


  • https://en.wikipedia.org/wiki/Hindley%E2%80%93Milner_type_system - a classical type system for the lambda calculus with parametric polymorphism, first described by J. Roger Hindley and later rediscovered by Robin Milner. Luis Damas contributed a close formal analysis and proof of the method in his PhD thesis.mong HM's more notable properties is completeness and its ability to deduce the most general type of a given program without the need of any type annotations or other hints supplied by the programmer. Algorithm W is a fast algorithm, performing type inference in almost linear time with respect to the size of the source, making it practically usable to type large programs.[note 1] HM is preferably used for functional languages. It was first implemented as part of the type system of the programming language ML. Since then, HM has been extended in various ways, most notably by constrained types as used in Haskell.




  • Ideology - Some people claim that unit tests make type systems unnecessary: "types are just simple unit tests written for you, and simple unit tests aren't the important ones". Other people claim that type systems make unit tests unnecessary: "dynamic languages only need unit tests because they don't have type systems." What's going on here? These can't both be right. We'll use this example and a couple others to explore the unknown beliefs that structure our understanding of the world.

Machine data

Machine data types - All data in computers based on digital electronics is represented as bits (alternatives 0 and 1) on the lowest level. The smallest addressable unit of data is usually a group of bits called a byte (usually an octet, which is 8 bits). The unit processed by machine code instructions is called a word (as of 2011, typically 32 or 64 bits). Most instructions interpret the word as a binary number, such that a 32-bit word can represent unsigned integer values from 0 to 2^{32}-1 or signed integer values from -2^{31} to 2^{31}-1. Because of two's complement, the machine language and machine doesn't need to distinguish between these unsigned and signed data types for the most part.

There is a specific set of arithmetic instructions that use a different interpretation of the bits in word as a floating-point number. Machine data types need to be exposed or made available in systems or low-level programming languages, allowing fine-grained control over hardware. The C programming language, for instance, supplies integer types of various widths, such as short and long. If a corresponding native type does not exist on the target platform, the compiler will break them down into code using types that do exist. For instance, if a 32-bit integer is requested on a 16 bit platform, the compiler will tacitly treat it as an array of two 16 bit integers.

Boolean

  • https://en.wikipedia.org/wiki/Boolean_data_type - a data type, having two values (usually denoted true and false), intended to represent the truth values of logic and Boolean algebra. It is named after George Boole, who first defined an algebraic system of logic in the mid 19th century. The Boolean data type is primarily associated with conditional statements, which allow different actions and change control flow depending on whether a programmer-specified Boolean condition evaluates to true or false. It is a special case of a more general logical data type; logic does not always have to be Boolean.
1,0

Numbers




  • https://en.wikipedia.org/wiki/Signedness - a property of data types representing numbers in computer programs. A numeric variable is signed if it can represent both positive and negative numbers, and unsigned if it can only represent non-negative numbers (zero or positive numbers).

As signed numbers can represent negative numbers, they lose a range of positive numbers that can only be represented with unsigned numbers of the same size (in bits) because half the possible values are non-positive values (so if an 8-bit is signed, positive unsigned values 128 to 255 are gone while -128 to 127 are present). Unsigned variables can dedicate all the possible values to the positive number range. For example, a two's complement signed 16-bit integer can hold the values −32768 to 32767 inclusively, while an unsigned 16 bit integer can hold the values 0 to 65535. For this sign representation method, the leftmost bit (most significant bit) denotes whether the value is positive or negative (0 for positive, 1 for negative).

Floating point


  • https://en.wikipedia.org/wiki/IEEE_754 - a technical standard for floating-point computation established in 1985 by the Institute of Electrical and Electronics Engineers (IEEE). The standard addressed many problems found in the diverse floating point implementations that made them difficult to use reliably and portably. Many hardware floating point units now use the IEEE 754 standard.



Strings

  • https://en.wikipedia.org/wiki/String_(computer_science) - traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation). A string is generally considered a data type and is often implemented as an array data structure of bytes (or words) that stores a sequence of elements, typically characters, using some character encoding. String may also denote more general arrays or other sequence (or list) data types and structures.Depending on programming language and precise data type used, a variable declared to be a string may either cause storage in memory to be statically allocated for a predetermined maximum length or employ dynamic allocation to allow it to hold a variable number of elements.When a string appears literally in source code, it is known as a string literal or an anonymous string.[1]In formal languages, which are used in mathematical logic and theoretical computer science, a string is a finite sequence of symbols that are chosen from a set called an alphabet.



to sort

  • https://en.wikipedia.org/wiki/Reference_type - a data type that refers to an object in memory. A pointer type on the other hand refers to a memory address. Reference types can be thought of as pointers that are implicitly dereferenced. The objects being referred to are dynamically allocated on the heap whereas value types are allocated automatically on the stack. In languages supporting garbage collection the objects being referred to are destroyed automatically after they become unreachable.
  • https://en.wikipedia.org/wiki/Passive_data_structure - also termed a plain old data structure, or plain old data (POD)), is a term for a record, to contrast with objects. It is a data structure that is represented only as passive collections of field values (instance variables), without using object-oriented features. Passive data structures are appropriate when there is a part of a system where it should be clearly indicated that the detailed logic for data manipulation and integrity are elsewhere. PDSs are often found at the boundaries of a system, where information is being moved to and from other systems or persistent storage and the problem domain logic that is found in other parts of the system is irrelevant. For example, PDS would be convenient for representing the field values of objects that are being constructed from external data, in a part of the system where the semantic checks and interpretations needed for valid objects are not applied yet.


  • https://en.wikipedia.org/wiki/Reference_(computer_science) - a value that enables a program to indirectly access a particular datum, such as a variable or a record, in the computer's memory or in some other storage device. The reference is said to refer to the datum, and accessing the datum is called dereferencing the reference. A reference is distinct from the data itself. Typically, for references to data stored in memory on a given system, a reference is implemented as the physical address of where the data is stored in memory or in the storage device. For this reason, a reference is often erroneously confused with a pointer or address, and is said to "point to" the data. However a reference may also be implemented in other ways, such as the offset (difference) between the datum's address and some fixed "base" address, as an index into an array, or more abstractly as a handle. More broadly, in networking, references may be network addresses, such as URLs.

The concept of reference must not be confused with other values (keys or identifiers) that uniquely identify the data item, but give access to it only through a non-trivial lookup operation in some table data structure. References are widely used in programming, especially to efficiently pass large or mutable data as arguments to procedures, or to share such data among various uses. In particular, a reference may point to a variable or record that contains references to other data. This idea is the basis of indirect addressing and of many linked data structures, such as linked lists. References can cause significant complexity in a program, partially due to the possibility of dangling and wild references and partially because the topology of data with references is a directed graph, whose analysis can be quite complicated.


  • https://en.wikipedia.org/wiki/Symbol_(programming) - a primitive datatype whose instances have a unique human-readable form. Symbols can be used as identifiers. In some programming languages, they are called atoms.In the most trivial implementation, they are essentially named integers (e.g. the enumerated type in C).




  • https://en.wikipedia.org/wiki/Literal_(computer_programming) - a notation for representing a fixed value in source code. Almost all programming languages have notations for atomic values such as integers, floating-point numbers, and strings, and usually for booleans and characters; some also have notations for elements of enumerated types and compound values such as arrays, records, and objects. An anonymous function is a literal for the function type.

In contrast to literals, variables or constants are symbols that can take on one of a class of fixed values, the constant being constrained not to change. Literals are often used to initialize variables, for example, in the following, 1 is an integer literal and the three letter string in "cat" is a string literal: 

int a = 1;
String s = "cat";

In lexical analysis, literals of a given type are generally a token type, with a grammar rule, like "a string of digits" for an integer literal. Some literals are specific keywords, like true for the boolean literal "true". In some object-oriented languages (like ECMAScript), objects can also be represented by literals. Methods of this object can be specified in the object literal using function literals.



  • https://en.wikipedia.org/wiki/Record_(computer_science) - also called struct or compound data, is a basic data structure. A record is a collection of fields, possibly of different data types, typically in fixed number and sequence. The fields of records may also be called elements, though these risk confusion with elements of a collection, or members, particularly in object-oriented programming. A tuple may or may not be considered a record, and vice versa, depending on conventions and the specific programming language.










  • https://en.wikipedia.org/wiki/Abstract_data_type - a mathematical model for data types where a data type is defined by its behavior (semantics) from the point of view of a user of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations. This contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user.


  • https://en.wikipedia.org/wiki/Semaphore_(programming) - a variable or abstract data type that is used for controlling access, by multiple processes, to a common resource in a concurrent system such as a multiprogramming operating system. A trivial semaphore is a plain variable that is changed (for example, incremented or decremented, or toggled) depending on programmer-defined conditions. The variable is then used as a condition to control access to some system resource.

Data structures







Array




  • https://en.wikipedia.org/wiki/Lookup_table - an array that replaces runtime computation with a simpler array indexing operation. The savings in terms of processing time can be significant, since retrieving a value from memory is often faster than undergoing an "expensive" computation or input/output operation. The tables may be precalculated and stored in static program storage, calculated (or "pre-fetched") as part of a program's initialization phase (memoization), or even stored in hardware in application-specific platforms. Lookup tables are also used extensively to validate input values by matching against a list of valid (or invalid) items in an array and, in some programming languages, may include pointer functions (or offsets to labels) to process the matching input. FPGAs also make extensive use of reconfigurable, hardware-implemented, lookup tables to provide programmable hardware functionality.



Tree




B-tree


to sort



The difference between arrays and linked lists are:

- Arrays are linear data structures. Linked lists are linear and non-linear data structures. - Linked lists are linear for accessing, and non-linear for storing in memory - Array has homogenous values. And each element is independent of each other positions. Each node in the linked list is connected with its previous node which is a pointer to the node. - Array elements can be modified easily by identifying the index value. It is a complex process for modifying the node in a linked list. - Array elements can not be added, deleted once it is declared. The nodes in the linked list can be added and deleted from the list.










Mutability

Evaluation

An expression evaluates to a value. A statement does something. 

x = 1
y = x + 1     # an expression
print y       # a statement, prints 2



  • https://en.wikipedia.org/wiki/Strict_function - in the denotational semantics of programming languages is a function f where f\left(\perp\right) = \perp. The entity \perp, called bottom, denotes an expression which does not return a normal value, either because it loops endlessly or because it aborts due to an error such as division by zero. A function which is not strict is called non-strict. A strict programming language is one in which user-defined functions are always strict.

Intuitively, non-strict functions correspond to control structures. Operationally, a strict function is one which always evaluates its argument; a non-strict function is one which may not evaluate some of its arguments. Functions having more than one parameter may be strict or non-strict in each parameter independently, as well as jointly strict in several parameters simultaneously.


  • https://en.wikipedia.org/wiki/Non-strict_programming_language - A strict programming language is one in which only strict functions (functions whose parameters must be evaluated completely before they may be called) may be defined by the user. A non-strict programming language allows the user to define non-strict functions, and hence may allow lazy evaluation.



Operators

  • https://en.wikipedia.org/wiki/Operator_(programming) - constructs which behave generally like functions, but which differ syntactically or semantically from usual functions. Common simple examples include arithmetic (addition with +, comparison with >) and logical operations (such as AND or &&). More involved examples include assignment (usually = or :=), field access in a record or object (usually .), and the scope resolution operator (often ::). Languages usually define a set of built-in operators, and in some cases allow user-defined operators.

Functions

  • http://en.wikipedia.org/wiki/Subroutine or function is a sequence of program instructions that perform a specific task, packaged as a unit. This unit can then be used in programs wherever that particular task should be performed. Subprograms may be defined within programs, or separately in libraries that can be used by multiple programs. In different programming languages a subroutine may be called a procedure, a function, a routine, a method, or a subprogram. The generic term callable unit is sometimes used.


  • http://en.wikipedia.org/wiki/Closure_(computer_science) - also lexical closures or function closures are a technique for implementing lexically scoped name binding in languages with first-class functions. Operationally, a closure is a record storing a function[a] together with an environment:[1] a mapping associating each free variable of the function (variables that are used locally, but defined in an enclosing scope) with the value or storage location to which the name was bound when the closure was created.[b] A closure—unlike a plain function—allows the function to access those captured variables through the closure's reference to them, even when the function is invoked outside their scope.


  • http://en.wikipedia.org/wiki/Call_site - a call site of a function or subroutine is the location (line of code) where the function is called (or may be called, through dynamic dispatch). A call site is where zero or more arguments are passed to the function, and zero or more return values are received.


  • https://en.wikipedia.org/wiki/Language_binding - an application programming interface (API) that provides glue code specifically made to allow a programming language to use a foreign library or operating system service (one that is not native to that language).


  • https://en.wikipedia.org/wiki/Wrapper_function - a subroutine in a software library or a computer program whose main purpose is to call a second subroutine[1] or a system call with little or no additional computation. Wrapper functions are used to make writing computer programs easier by abstracting away the details of a subroutine's underlying implementation.


  • http://en.wikipedia.org/wiki/Anonymous_function - also function literal or lambda abstraction is a function definition that is not bound to an identifier. Anonymous functions are often: arguments being passed to higher-order functions, or used for constructing the result of a higher-order function that needs to return a function. If the function is only used once, or a limited number of times, an anonymous function may be syntactically lighter than using a named function. Anonymous functions are ubiquitous in functional programming languages and other languages with first-class functions, where they fulfill the same role for the function type as literals do for other data types.


  • http://en.wikipedia.org/wiki/Function_type - the type of a variable or parameter to which a function has or can be assigned, or an argument or result type of a higher-order function taking or returning a function.


  • http://en.wikipedia.org/wiki/Function_prototype - or function interface is a declaration of a function that specifies the function's name and type signature (arity, parameter types, and return type), but omits the function body. The term is particularly used in C and C++. While a function definition specifies how the function does what it does (the "implementation"), a function prototype merely specifies its interface, i.e. what data types go in and come out of it.

In a prototype, parameter names are optional (and in C/C++ have function prototype scope, meaning their scope ends at the end of the prototype), however, the type is necessary along with all modifiers (e.g. if it is a pointer or a const parameter). In object-oriented programming, interfaces and abstract methods serve much the same purpose.




  • http://en.wikipedia.org/wiki/Callback_(computer_science) - a piece of executable code that is passed as an argument to other code, which is expected to call back (execute) the argument at some convenient time. The invocation may be immediate as in a synchronous callback, or it might happen at a later time as in an asynchronous callback. In all cases, the intention is to specify a function or subroutine as an entity that is, depending on the language, more or less similar to a variable. Programming languages support callbacks in different ways, often implementing them with subroutines, lambda expressions, blocks, or function pointers.


  • http://en.wikipedia.org/wiki/Tail_call - a subroutine call performed as the final action of a procedure. If a tail call might lead to the same subroutine being called again later in the call chain, the subroutine is said to be tail-recursive, which is a special case of recursion. Tail recursion (or tail-end recursion) is particularly useful, and often easy to handle in implementations.


  • http://en.wikipedia.org/wiki/Function_pointer - Instead of referring to data values, a function pointer points to executable code within memory. When dereferenced, a function pointer can be used to invoke the function it points to and pass it arguments just like a normal function call. Such an invocation is also known as an "indirect" call, because the function is being invoked indirectly through a variable instead of directly through a fixed name or address. Function pointers can be used to simplify code by providing a simple way to select a function to execute based on run-time values.


  • http://en.wikipedia.org/wiki/Funarg_problem - refers to the difficulty in implementing first-class functions (functions as first-class objects) in programming language implementations so as to use stack-based memory allocation of the functions. The difficulty only arises if the body of a nested function refers directly (i.e., not via argument passing) to identifiers defined in the environment in which the function is defined, but not in the environment of the function call. To summarize the discussion below, two standard resolutions are to either forbid such references or to create closures.



  • https://en.wikipedia.org/wiki/Inline_expansion - or inlining, is a manual or compiler optimization that replaces a function call site with the body of the called function. Inline expansion is similar to macro expansion, but occurs during compilation, without changing the source code (the text), while macro expansion occurs prior to compilation, and results in different text that is then processed by the compiler. Inlining is an important optimization, but has complicated effects on performance.[1] As a rule of thumb, some inlining will improve speed at very minor cost of space, but excess inlining will hurt speed, due to inlined code consuming too much of the instruction cache, and also cost significant space.


  • https://en.wikipedia.org/wiki/Defunctionalization - refers to a compile-time transformation which eliminates higher-order functions, replacing them by a single first-order apply function. The technique was first described by John C. Reynolds in his 1972 paper, "Definitional Interpreters for Higher-Order Programming Languages". Reynolds' observation was that a given program contains only finitely many function abstractions, so that each can be assigned (and replaced by) a unique identifier. Every function application within the program is then replaced by a call to the apply function with the function identifier as the first argument. The apply function's only job is to dispatch on this first argument, and then perform the instructions denoted by the function identifier on the remaining arguments.






Hash function


Polymorphism


  • https://en.wikipedia.org/wiki/Ad_hoc_polymorphism - a kind of polymorphism in which polymorphic functions can be applied to arguments of different types, because a polymorphic function can denote a number of distinct and potentially heterogeneous implementations depending on the type of argument(s) to which it is applied. It is also known as function overloading or operator overloading. The term ad hoc in this context is not intended to be pejorative; it refers simply to the fact that this type of polymorphism is not a fundamental feature of the type system.


  • https://en.wikipedia.org/wiki/Parametric_polymorphism - a way to make a language more expressive, while still maintaining full static type-safety. Using parametric polymorphism, a function or a data type can be written generically so that it can handle values identically without depending on their type. Such functions and data types are called generic functions and generic datatypes respectively and form the basis of generic programming.




Control structures







  • https://en.wikipedia.org/wiki/Generator_(computer_science) - a special routine that can be used to control the iteration behaviour of a loop. In fact, all generators are iterators. A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values. However, instead of building an array containing all the values and returning them all at once, a generator yields the values one at a time, which requires less memory and allows the caller to get started processing the first few values immediately. In short, a generator looks like a function but behaves like an iterator.

Generators can be implemented in terms of more expressive control flow constructs, such as coroutines or first-class continuations. Generators, also known as semicoroutines, are a special case of (and weaker than) coroutines, in that they always yield control back to the caller (when passing a value back), rather than specifying a coroutine to jump to

Algorithms

See also Computing#Computational complexity, Maths




  • https://en.wikipedia.org/wiki/Algorithmic_information_theory - a subfield of information theory and computer science that concerns itself with the relationship between computation and information. According to Gregory Chaitin, it is "the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously."









  • https://en.wikipedia.org/wiki/Assertion_(software_development) - a statement that a predicate (Boolean-valued function, a true–false expression) is expected to always be true at that point in the code. If an assertion evaluates to false at run time, an assertion failure results, which typically causes the program to crash, or to throw an assertion exception.
  • https://en.wikipedia.org/wiki/Invariant_%28computer_science%29 - a condition that can be relied upon to be true during execution of a program, or during some portion of it. It is a logical assertion that is held to always be true during a certain phase of execution. For example, a loop invariant is a condition that is true at the beginning and end of every execution of a loop.


Libraries


  • PDF: How To Write Shared Libraries - Ulrich Drepper, December 10, 2011. Abstract: Today, shared libraries are ubiquitous. Developers use them for multiple reasons and create them just as they would create application code. This is a problem, though, since on many platforms some additional techniques must be applied even to generate decent code. Even more knowledge is needed to generate optimized code. This paper introduces the required rules and techniques. In addition, it introduces the concept of ABI (Application Binary Interface) stability and shows how to manage it.


System calls

See *nix

Macros

Metaprogramming

  • https://en.wikipedia.org/wiki/Metaprogramming - the writing of computer programs with the ability to treat programs as their data. It means that a program could be designed to read, generate, analyse or transform other programs, and even modify itself while running. In some cases, this allows programmers to minimize the number of lines of code to express a solution (hence reducing development time), or it gives programs greater flexibility to efficiently handle new situations without recompilation. The language in which the metaprogram is written is called the metalanguage. The language of the programs that are manipulated is called the object language. The ability of a programming language to be its own metalanguage is called reflection or reflexivity

Monads

Aspect of functional. See Haskell, etc. for related.

Events


Messaging

See also Network#Messaging, Data#Serialization


  • https://en.wikipedia.org/wiki/Messaging_pattern - In software architecture, a messaging pattern is a network-oriented architectural pattern which describes how two different parts of a message passing system connect and communicate with each other. In telecommunications, a message exchange pattern (MEP) describes the pattern of messages required by a communications protocol to establish or use a communication channel. There are two major message exchange patterns — a request–response pattern, and a one-way pattern. For example, HTTP is a request–response pattern protocol, and UDP is a one-way pattern.






  • https://en.wikipedia.org/wiki/Interface_description_language - or interface definition language (IDL), is a specification language used to describe a software component's application programming interface (API). IDLs describe an interface in a language-independent way, enabling communication between software components that do not share one language, for example, between those written in C++ and those written in Java.IDLs are commonly used in remote procedure call software. In these cases the machines at either end of the link may be using different operating systems and computer languages. IDLs offer a bridge between the two different systems.


Apache Thrift

  • Apache Thrift - framework for scalable cross-language services development, combines a software stack with a code generation engine to build services that work efficiently and seamlessly between C++, Java, Python, PHP, Ruby, Erlang, Perl, Haskell, C#, Cocoa, JavaScript, Node.js, Smalltalk, OCaml and Delphi and other languages. Thrift allows you to define data types and service interfaces in a simple definition file. Taking that file as input, the compiler generates code to be used to easily build RPC clients and servers that communicate seamlessly across programming languages. Instead of writing a load of boilerplate code to serialize and transport your objects and invoke remote methods, you can get right down to business.


zeromq

  • zeromq - Distributed Messaging. Connect your code in any language, on any platform. Carries messages across inproc, IPC, TCP, TIPC, multicast. Smart patterns like pub-sub, push-pull, and router-dealer. High-speed asynchronous I/O engines, in a tiny library. Backed by a large and active open source community. Supports every modern language and platform. Build any architecture: centralized, distributed, small, or large. Free software with full commercial support.

hyperglyph

MessagePack

  • MessagePack - an efficient binary serialization format. It lets you exchange data among multiple languages like JSON but it's faster and smaller. For example, small integers (like flags or error code) are encoded into a single byte, and typical short strings only require an extra byte in addition to the strings themselves.

AMQP

  • AMQP - Advanced Message Queuing Protocol, an open standard for passing business messages between applications or organizations. It connects systems, feeds business processes with the information they need and reliably transmits onward the instructions that achieve their goals.

RabbitMQ

  • RabbitMQ - the most widely deployed open source message broker. RabbitMQ is lightweight and easy to deploy on premises and in the cloud. It supports multiple messaging protocols. RabbitMQ can be deployed in distributed and federated configurations to meet high-scale, high-availability requirements.

tosort

Concurrency

  • https://en.wikipedia.org/wiki/Concurrent_computing - a form of computing in which several computations are executing during overlapping time periods—concurrently—instead of sequentially (one completing before the next starts). This is a property of a system—this may be an individual program, a computer, or a network—and there is a separate execution point or "thread of control" for each computation ("process"). A concurrent system is one where a computation can make progress without waiting for all other computations to complete—where more than one computation can make progress at "the same time".



  • Concurrency is not parallelism - The Go Blog - But when people hear the word concurrency they often think of parallelism, a related but quite distinct concept. In programming, concurrency is the composition of independently executing processes, while parallelism is the simultaneous execution of (possibly related) computations. Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once. With talk from Rob Pike on Go.

Hand wavey definition

  • Concurrency = dealing with a lot of thigns at one
  • Parallelism = doing a lot of things at once


  • https://en.wikipedia.org/wiki/Communicating_sequential_processes - a formal language for describing patterns of interaction in concurrent systems. It is a member of the family of mathematical theories of concurrency known as process algebras, or process calculi, based on message passing via channels. CSP was highly influential in the design of the occam programming language, and also influenced the design of programming languages such as Limbo, RaftLib, Go, Crystal, and Clojure's core.async.CSP was first described in a 1978 paper by Tony Hoare, but has since evolved substantially. CSP has been practically applied in industry as a tool for specifying and verifying the concurrent aspects of a variety of different systems, such as the T9000 Transputer, as well as a secure ecommerce system. The theory of CSP itself is also still the subject of active research, including work to increase its range of practical applicability (e.g., increasing the scale of the systems that can be tractably analyzed)





  • https://en.wikipedia.org/wiki/Mutual_exclusion - the requirement of ensuring that no two concurrent processes are in their critical section at the same time; it is a basic requirement in concurrency control, to prevent race conditions. Here, a critical section refers to a period when the process accesses a shared resource, such as shared memory. The requirement of mutual exclusion was first identified and solved by Edsger W. Dijkstra in his seminal 1965 paper titled Solution of a problem in concurrent programming control, and is credited as the first topic in the study of concurrent algorithms.


  • https://en.wikipedia.org/wiki/Critical_section - a part of a multi-process program that may not be concurrently executed by more than one of the program's processes.[a] In other words, it is a piece of a program that requires mutual exclusion of access.[1] Typically, the critical section accesses a shared resource, such as a data structure, a peripheral device, or a network connection, that does not allow multiple concurrent accesses.





  • https://en.wikipedia.org/wiki/Lock_(computer_science) - or mutex (from mutual exclusion) is a synchronization mechanism for enforcing limits on access to a resource in an environment where there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy.


Futures and promises

  • http://en.wikipedia.org/wiki/Futures_and_promises - future, promise, delay, and deferred refer to constructs used for synchronizing in some concurrent programming languages. They describe an object that acts as a proxy for a result that is initially unknown, usually because the computation of its value is yet incomplete.

Parallelism

  • https://en.wikipedia.org/wiki/Parallel_computing - a type of computation in which many calculations or the execution of processes are carried out concurrently. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but it's gaining broader interest due to the physical constraints preventing frequency scaling.[2] As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.


  • https://en.wikipedia.org/wiki/Analysis_of_parallel_algorithms - Like in the analysis of "ordinary", sequential, algorithms, one is typically interested in asymptotic bounds on the resource consumption (mainly time spent computing), but the analysis is performed in the presence of multiple processor units that cooperate to perform computations. Thus, one can determine not only how many "steps" a computation takes, but also how much faster it becomes as the number of processors goes up.



  • https://en.wikipedia.org/wiki/Bit-level_parallelism - a form of parallel computing based on increasing processor word size. Increasing the word size reduces the number of instructions the processor must execute in order to perform an operation on variables whose sizes are greater than the length of the word. (For example, consider a case where an 8-bit processor must add two 16-bit integers. The processor must first add the 8 lower-order bits from each integer, then add the 8 higher-order bits, requiring two instructions to complete a single operation. A 16-bit processor would be able to complete the operation with single instruction.) Originally, all electronic computers were serial (single-bit) computers. The first electronic computer that was not a serial computer—the first bit-parallel computer—was the 16-bit Whirlwind from 1951. From the advent of very-large-scale integration (VLSI) computer chip fabrication technology in the 1970s until about 1986, advancements in computer architecture were done by increasing bit-level parallelism,[1] as 4-bit microprocessors were replaced by 8-bit, then 16-bit, then 32-bit microprocessors. This trend generally came to an end with the introduction of 32-bit processors, which have been a standard in general purpose computing for two decades. Only recently, with the advent of x86-64 architectures, have 64-bit processors become commonplace. On 32-bit processors, external data bus width continues to increase. For example, DDR1 SDRAM transfers 128 bits per clock cycle. DDR2 SDRAM transfers a minimum of 256 bits per burst.


  • https://en.wikipedia.org/wiki/Instruction-level_parallelism - a measure of how many of the instructions in a computer program can be executed simultaneously. There are two approaches to instruction level parallelism: in hardware and in software. Hardware level works upon dynamic parallelism whereas, the software level works on static parallelism. Dynamic parallelism means the processor decides at run time which instructions to execute in parallel, whereas static parallelism means the compiler decides which instructions to execute in parallel. The Pentium processor works on the dynamic sequence of parallel execution but the Itanium processor works on the static level parallelism.


  • https://en.wikipedia.org/wiki/Data_parallelism - a form of parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel.


  • https://en.wikipedia.org/wiki/Task_parallelism - also known as function parallelism and control parallelism, is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data. A common type of task parallelism is pipelining which consists of moving a single set of data through a series of separate tasks where each task can execute independently of the others.

Garbage collection



Paradigms





  • The Rule of Least Power - When designing computer systems, one is often faced with a choice between using a more or less powerful language for publishing information, for expressing constraints, or for solving some problem. This finding explores tradeoffs relating the choice of language to reusability of information. The "Rule of Least Power" suggests choosing the least powerful language suitable for a given purpose.


Imperative

  • http://en.wikipedia.org/wiki/Imperative_programming - a programming paradigm that uses statements that change a program's state. In much the same way that the imperative mood in natural languages expresses commands, an imperative program consists of commands for the computer to perform. Imperative programming focuses on describing how a program operates.
  • http://en.wikipedia.org/wiki/Procedural_programming - a programming paradigm, derived from structured programming, based upon the concept of the procedure call. Procedures, also known as routines, subroutines, or functions (not to be confused with mathematical functions, but similar to those used in functional programming), simply contain a series of computational steps to be carried out. Any given procedure might be called at any point during a program's execution, including by other procedures or itself. Procedural programming languages include C, Go, Fortran, Pascal, and BASIC. Computer processors provide hardware support for procedural programming through a stack register and instructions for calling procedures and returning from them. Hardware support for other types of programming is possible, but no attempt was commercially successful (for example Lisp machines or Java processors).


  • http://en.wikipedia.org/wiki/Structured_programming - a programming paradigm aimed at improving the clarity, quality, and development time of a computer program by making extensive use of subroutines, block structures, for and while loops—in contrast to using simple tests and jumps such as the goto statement which could lead to "spaghetti code" which is difficult both to follow and to maintain. It emerged in the late 1950s with the appearance of the ALGOL 58 and ALGOL 60 programming languages, with the latter including support for block structures.

Contributing factors to its popularity and widespread acceptance, at first in academia and later among practitioners, include the discovery of what is now known as the structured program theorem in 1966, and the publication of the influential "Go To Statement Considered Harmful" open letter in 1968 by Dutch computer scientist Edsger W. Dijkstra, who coined the term "structured programming". Structured programming is most frequently used with deviations that allow for clearer programs in some particular cases, such as when exception handling has to be performed.

Recursive

Object orientated





  • https://en.wikipedia.org/wiki/Method_(computer_programming) - a procedure associated with an object. An object is made up of data and behavior, which form the interface that an object presents to the outside world. Data is represented as properties of the object and behavior as methods. For example, a Window object would have methods such as open and close, while its state (whether it is opened or closed) would be a property.








  • https://en.wikipedia.org/wiki/Resource_acquisition_is_initialization - a programming idiom used in several object-oriented languages to describe a particular language behavior. In RAII, holding a resource is a class invariant, and is tied to object lifetime: resource allocation (or acquisition) is done during object creation (specifically initialization), by the constructor, while resource deallocation (release) is done during object destruction (specifically finalization), by the destructor. Thus the resource is guaranteed to be held between when initialization finishes and finalization starts (holding the resources is a class invariant), and to be held only when the object is alive. Thus if there are no object leaks, there are no resource leaks.




Data-orientated

  • https://en.wikipedia.org/wiki/Data-oriented_design - a program optimization approach motivated by efficient usage of the CPU cache, used in video game development. The approach is to focus on the data layout, separating and sorting fields according to when they are needed, and to think about transformations of data. Proponents include Mike Acton and Scott Meyers.

The claim is that traditional object-oriented programming (OOP) design principles result in poor data locality, more so if runtime polymorphism (dynamic dispatch) is used (which is especially problematic on some processors). Although OOP does superficially seem to organise code around data, the practice is quite different. OOP is actually about organising source code around data types, rather than physically grouping individual fields and arrays in a format efficient for access by specific functions. It also often hides layout details under abstraction layers, while a data-oriented programmer wants to consider this first and foremost.




Declarative

  • http://en.wikipedia.org/wiki/Declarative_programming - a programming paradigm—a style of building the structure and elements of computer programs—that expresses the logic of a computation without describing its control flow. Declarative programming often considers programs as theories of a formal logic, and computations as deductions in that logic space. Declarative programming may greatly simplify writing parallel programs. Common declarative languages include those of database query languages (e.g., SQL, XQuery), regular expressions, logic programming, functional programming, and configuration management systems.

We could do this in an imperative style like so: 

var numbers = [1,2,3,4,5]
var doubled = []

for(var i = 0; i < numbers.length; i++) {
  var newNumber = numbers[i] * 2
  doubled.push(newNumber)
}
console.log(doubled) //=> [2,4,6,8,10]

We explicitly iterate over the length of the array, pull each element out of the array, double it, and add the doubled value to the new array, mutating the doubled array at each step until we are done. A more declarative approach might use the Array.map function and look like: 

var numbers = [1,2,3,4,5]
 
var doubled = numbers.map(function(n) {
  return n * 2
})
console.log(doubled) //=> [2,4,6,8,10]

map creates a new array from an existing array, where each element in the new array is created by passing the elements of the original array into the function passed to map (function(n) { return n*2 } in this case). What the map function does is abstract away the process of explicitly iterating over the array, and lets us focus on what we want to happen. Note that the function we pass to map is pure; it doesn't have any side effects (change any external state), it just takes in a number and returns the number doubled.



Functional



  • λ Lessons - Pattern matching, first-class functions, and abstracting over recursion in Haskell. This is a short, interactive lesson that teaches core functional programming concepts. It was designed to transform the way you think about performing operations on lists of things, by showing you how functions are executed. [178]










Dataflow

See Dataflow

  • http://en.wikipedia.org/wiki/Dataflow_programming - a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages, and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing.


  • http://en.wikipedia.org/wiki/Reactive_programming - a programming paradigm oriented around data flows and the propagation of change. This means that it should be possible to express static or dynamic data flows with ease in the programming languages used, and that the underlying execution model will automatically propagate changes through the data flow.


  • https://en.wikipedia.org/wiki/Flow-based_programming - FBP, a programming paradigm that defines applications as networks of "black box" processes, which exchange data across predefined connections by message passing, where the connections are specified externally to the processes. These black box processes can be reconnected endlessly to form different applications without having to be changed internally. FBP is thus naturally component-oriented. FBP is a particular form of dataflow programming based on bounded buffers, information packets with defined lifetimes, named ports, and separate definition of connections.


  • Apache NiFi is a dataflow system based on the concepts of flow-based programming. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. NiFi has a web-based user interface for design, control, feedback, and monitoring of dataflows. It is highly configurable along several dimensions of quality of service, such as loss-tolerant versus guaranteed delivery, low latency versus high throughput, and priority-based queuing. NiFi provides fine-grained data provenance for all data received, forked, joined cloned, modified, sent, and ultimately dropped upon reaching its configured end-state. [188]

Constraint


Logic

  • https://en.wikipedia.org/wiki/Logic_programming - a programming paradigm based on formal logic. A program written in a logic programming language is a set of sentences in logical form, expressing facts and rules about some problem domain. Major logic programming language families include Prolog, Answer set programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses and are read declaratively as logical implications.

Patterns

See also Organisation#Patterns, Development



"ExtremeProgramming -- all programming is maintenance."


  • Design Patterns Library - a source for information about all aspects of software patterns and pattern languages. If you are new to patterns, James Coplien and Richard Gabriel have created a succinct pattern definition.Patterns and Pattern Languages are ways to describe best practices, good designs, and capture experience in a way that it is possible for others to reuse this experience. The Hillside Group takes pleasure in sponsoring many different PLoP conferences that are provided for the betterment of the pattern community.














MV*

"create your views, express your models or develop a controller"


Push/pull


Graphics

See Graphics, UI, GUI, Wayland, 3D


  • https://en.wikipedia.org/wiki/Layout_engine - a computer program that combines content and formatting information for electronic or printed display. It is not a stand-alone program but a core component of larger programs. There are different types of layout engines. One is the web browser engines used in web browsers and email clients. Another type is the layout managers used in widget toolkits.


to sort

SDL

  • Eco - a prototype editor for editing composed languages. It is not feature complete, it is not intended for production, and it does have bugs. Eco is distributed under a BSD/MIT license.



Noise


Optimisation

Areas

See also Games#Software, AI, Audio, etc.

Command-line

Compression

Network



Graphics


3D


Virtual / augmented reality


  • https://en.wikipedia.org/wiki/X_Reality_(XR) - defined as: a form of “mixed reality environment that comes from the fusion (union) of ... ubiquitous sensor/actuator networks and shared online virtual worlds....”. It encompasses a wide spectrum of hardware and software, including sensory interfaces, applications, and infrastructures, that enable content creation for virtual reality (VR), mixed reality (MR), augmented reality (AR), cinematic reality (CR). With these tools, users generate new forms of reality by bringing digital objects into the physical world and bringing physical world objects into the digital world.Its meaning has more recently been broadened to include technologies of extended ("cyborg" or wearable) intelligence in regards to the IEEE Council on Extended Intelligence (CXI).

Distributed



Virus


Social



to sort

For kids

Scratch

Snap

to sort














Future






Performance



  • https://en.wikipedia.org/wiki/Cyclomatic_complexity - a software metric (measurement), used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. It was developed by Thomas J. McCabe, Sr. in 1976.

Quantum computing




Cool

Humour

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