Computing

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to very much further sort and understand.

General

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







Eric Steven Raymond;

  • 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/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.




People

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.

  • 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.




Cellular automaton

Computability theory

to sort

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." [20]


  • Physics, Topology, Logic and Computation: A Rosetta Stone [21] - "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 - [23] - "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,[1][2] collectively called Bachmann-Landau notation or asymptotic notation.






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  • 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.[1][2] 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." [42]

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. [45]



  • 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 is 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. [51]


  • 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

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/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.








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 [72] [73]



  • 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





Parallel computing



GPU


GPGPU

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[1]), and lack hardware for rasterisation/texture mapping.[2] The chip has been specifically designed for Google's TensorFlow framework, however Google still uses CPUs and GPUs for other machine learning.[3] Other AI accelerator designs are appearing from other vendors also and are aimed at embedded and robotics markets.

The Mill

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/Nearline_storage - For example, always-on spinning hard disk drives are online storage, while spinning drives that spin down automatically, such as in massive arrays of idle disks (MAID), are nearline storage.


  • 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

Emulation

Security


ASIC

Operating system

See also *nix, Virtualisation, Distros, Android, Windows, Apple

  • OSDev.org - wiki that provides information about the creation of operating systems and serves as a community for those people interested in OS creation

Kernel

Types







  • Mirage OS is a library operating system that constructs unikernels for secure, high-performance network applications across a variety of cloud computing and mobile platforms. Code can be developed on a normal OS such as Linux or MacOS X, and then compiled into a fully-standalone, specialised unikernel that runs under the Xen hypervisor.
  • includeOS - allows you to run your application in the cloud without an operating system. IncludeOS adds operating system functionality to your application allowing you to create performant, secure and resource efficient virtual machines. IncludeOS applications boot in tens of milliseconds and require only a few megabytes of disk and memory.


  • https://en.wikipedia.org/wiki/Exokernel - force as few abstractions as possible on developers, enabling them to make as many decisions as possible about hardware abstractions. Exokernels are tiny, since functionality is limited to ensuring protection and multiplexing of resources, which are vastly simpler than conventional microkernels' implementation of message passing and monolithic kernels' implementation of abstractions.








  • https://en.wikipedia.org/wiki/Language-based_system - a type of operating system that uses language features to provide security, instead of or in addition to hardware mechanisms. In such systems, code referred to as the trusted base is responsible for approving programs for execution, assuring they cannot perform operations detrimental to the system's stability without first being detected and dealt with.

Implementations

See also *nix


Runtime





Hacking

https://emily.st/2015/01/27/reverse-engineering/ [96]

Areas

Command-line

GUI


Compression

Network



Graphics


3D


Audio

See also Audio#Programming

Virtual / augmented reality

Distributed



NLP


Virus

Bots

Machine learning





"In applications of "usual" machine learning, there is typically a strong focus on the feature engineering part; the model learned by an algorithm can only be so good as its input data. Of course, there must be sufficient discriminatory information in our dataset, however, the performance of machine learning algorithms can suffer substantially when the information is buried in meaningless features. The goal behind deep learning is to automatically learn the features from (somewhat) noisy data; it's about algorithms that do the feature engineering for us to provide deep neural network structures with meaningful information so that it can learn more effectively. We can think of deep learning as algorithms for automatic "feature engineering," or we could simply call them "feature detectors," which help us to overcome the vanishing gradient challenge and facilitate the learning in neural networks with many layers."













  • Caffe - a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.


  • Torch - a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.



  • Data Science Machine - an end-to-end software system that is able to automatically develop predictive models from relational data. The Machine was created by Max Kanter and Kalyan Verramachaneni at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The system automates two of the most human-intensive components of a data science endeavor: feature engineering, and selection and tuning of the machine learning methods that build predictive models from those features. First, an algorithm called Deep Feature Synthesis automatically engineers features. Next, through an approach called Deep Mining, the Machine composes a generalized machine learning pipeline that includes dimensionality reduction methods, feature selection methods, clustering, and classifier design. Finally, it tunes the parameters through a Gaussian Copula Process.




  • TensorFlow - an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.










Artificial intelligence



  • OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

Since our research is free from financial obligations, we can better focus on a positive human impact. We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as is possible safely.

The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field. [140]

Gaming

  • Godot Engine - Free and open source 2D and 3D game engine - provides a huge set of common tools, so you can just focus on making your game without reinventing the wheel. Godot is completely free and open source under the very permissive MIT license. No strings attached, no royalties, nothing. Your game is yours, down to the last line of engine code.

Social


For kids

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

to sort

  • 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.




  • RRDtool is the OpenSource industry standard, high performance data logging and graphing system for time series data. RRDtool can be easily integrated in shell scripts, perl, python, ruby, lua or tcl applications.






modelling;

  • QEforge is a web portal offering support to researchers active in the field of computer simulation and numerical modeling of matter and materials at the atomic scale. The most popular source code management (CVS, SVN or Git ) systems, mailing lists, public forums, download space, wiki pages, and much more are provided through the Gforge engine.
  • UbiGraph is a tool for visualizing dynamic graphs. The basic version is free, and talks to Python, Ruby, PHP, Java, C, C++, C#, Haskell, and OCaml.
  • Hunspell is the spell checker of LibreOffice, OpenOffice.org, Mozilla Firefox 3 & Thunderbird, Google Chrome, and it is also used by proprietary software packages, like Mac OS X, InDesign, memoQ, Opera and SDL Trados.


  • NuPIC - the Numenta Platform for Intelligent Computing, comprises a set of learning algorithms that were first described in a white paper published by Numenta in 2009. The learning algorithms faithfully capture how layers of neurons in the neocortex learn.






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