Return to Parallel Programming, Parallelism, Asynchronous Programming, Asynchrony, Concurrency Fundamentals, Concurrency, Functional Programming Fundamentals, Functional Programming and Concurrency, Fundamentals, Programming Fundamentals, Awesome Async

• Introduction to Parallel Programming
• Intro to Parallel Programming
• Parallel Programming Introduction
• Parallel Programming Intro
• Parallel Programming Fundamental
• Basics of Parallel Programming
• Basic of Parallel Programming
• Parallel Programming Basic
• Parallel Programming Basics
• Fundamentals of Parallel Programming
• Fundamental of Parallel Programming
• Parallel Programming Fundamental

• Intro to Parallelism
• Parallelism Introduction
• Parallelism Intro
• Parallelism Fundamentals
• Parallelism Fundamental
• Basics of Parallelism
• Basic of Parallelism
• Parallelism Basic
• Parallelism Basics
• Fundamentals of Parallelism
• Fundamental of Parallelism
• Parallelism Fundamentals
• Parallelism Fundamental

Parallelism

• Async programming vs Parallel programming

Snippet from Wikipedia: Parallelism

Parallelism may refer to:

• Angle of parallelism, in hyperbolic geometry, the angle at one vertex of a right hyperbolic triangle that has two hyperparallel sides
• Axial parallelism, a type of motion characteristic of a gyroscope and astronomical bodies
• Conscious parallelism or also tacit parallelism, price-fixing between competitors that occurs without any communication between the parties
• Parallel computing, the simultaneous execution on multiple processors of different parts of a program
  • In the analysis of parallel algorithms, the maximum possible speedup of a computation
• Parallel evolution, the independent emergence of a similar trait in different unrelated species
• Parallel (geometry), the property of parallel lines
• Parallelism (grammar), a balance of two or more similar words, phrases, or clauses
• Parallelism (rhetoric), the chief rhetorical device of Biblical poetry in Hebrew
• Psychophysical parallelism, the theory that the conscious and nervous processes vary concomitantly
• Parallel harmony/doubling, or harmonic parallelism, in music

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Snippet from Wikipedia: Parallel computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. 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 has gained broader interest due to the physical constraints preventing frequency scaling. 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.

In computer science, parallelism and concurrency are two different things: a parallel program uses multiple CPU cores, each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads) without necessarily completing each one. A program can have both, neither or a combination of parallelism and concurrency characteristics.

Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks.

In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, but explicitly parallel algorithms, particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs, of which race conditions are the most common. Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting optimal parallel program performance.

A theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahl's law, which states that it is limited by the fraction of time for which the parallelization can be utilised.

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Snippet from Wikipedia: Asynchrony (computer programming)

Asynchrony, in computer programming, refers to the occurrence of events independent of the main program flow and ways to deal with such events. These may be "outside" events such as the arrival of signals, or actions instigated by a program that take place concurrently with program execution, without the program hanging to wait for results. Asynchronous input/output is an example of the latter case of asynchrony, and lets programs issue commands to storage or network devices that service these requests while the processor continues executing the program. Doing so provides a degree of parallelism.

A common way for dealing with asynchrony in a programming interface is to provide subroutines that return a future or promise that represents the ongoing operation, and a synchronizing operation that blocks until the future or promise is completed. Some programming languages, such as Cilk, have special syntax for expressing an asynchronous procedure call.

Examples of asynchrony include the following:

• Asynchronous procedure call, a method to run a procedure concurrently, a lightweight alternative to threads.
• Ajax is a set of client-side web technologies used by the client to create asynchronous I/O web applications.
• Asynchronous method dispatch (AMD), a data communication method used when there is a need for the server side to handle a large number of long lasting client requests. Using synchronous method dispatch (SMD), this scenario may turn the server into an unavailable busy state resulting in a connection failure response caused by a network connection request timeout. The servicing of a client request is immediately dispatched to an available thread from a pool of threads and the client is put in a blocking state. Upon the completion of the task, the server is notified by a callback. The server unblocks the client and transmits the response back to the client. In case of thread starvation, clients are blocked waiting for threads to become available.

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