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Return to Parallel processing, Parallelism

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Leveraging the principle that large problems can often be divided into smaller ones, which are then solved concurrently (“in parallel”), it significantly reduces computation time for tasks with large datasets or complex algorithms. This approach contrasts with traditional serial computing, where tasks are performed sequentially. Parallel computing utilizes multiple processing elements simultaneously, ranging from multicore processors within a single computer to a large number of computers in a cluster computing environment or even across distributed systems. It is essential for high-performance computing (HPC), enabling scientists and engineers to perform large-scale simulations, complex data analyses, and advanced scientific research that would be impractical, if not impossible, with serial computing. Key challenges in parallel computing include data decomposition, balancing computational loads, and managing data communication among processors to minimize performance bottlenecks.

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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