Parallel computing

"Parallelization" redirects here. For parallelization of manifolds, see Parallelization (mathematics).
Large supercomputers such as IBM's Blue Gene/P are designed to heavily exploit parallelism.

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously.[1] 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.[2] As power consumption (and consequently heat generation) by computers has become a concern in recent years,[3] parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.[4]

Parallelism vs concurrency

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.[5]

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,[6] 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.

  1. ^ Gottlieb, Allan; Almasi, George S. (1989). Highly parallel computing. Redwood City, Calif.: Benjamin/Cummings. ISBN 978-0-8053-0177-9.
  2. ^ S.V. Adve et al. (November 2008). "Parallel Computing Research at Illinois: The UPCRC Agenda" Archived 2018-01-11 at the Wayback Machine (PDF). Parallel@Illinois, University of Illinois at Urbana-Champaign. "The main techniques for these performance benefits—increased clock frequency and smarter but increasingly complex architectures—are now hitting the so-called power wall. The computer industry has accepted that future performance increases must largely come from increasing the number of processors (or cores) on a die, rather than making a single core go faster."
  3. ^ Asanovic et al. Old [conventional wisdom]: Power is free, but transistors are expensive. New [conventional wisdom] is [that] power is expensive, but transistors are "free".
  4. ^ Asanovic, Krste et al. (December 18, 2006). "The Landscape of Parallel Computing Research: A View from Berkeley" (PDF). University of California, Berkeley. Technical Report No. UCB/EECS-2006-183. "Old [conventional wisdom]: Increasing clock frequency is the primary method of improving processor performance. New [conventional wisdom]: Increasing parallelism is the primary method of improving processor performance… Even representatives from Intel, a company generally associated with the 'higher clock-speed is better' position, warned that traditional approaches to maximizing performance through maximizing clock speed have been pushed to their limits."
  5. ^ Parallel and Concurrent Programming in Haskell. O'Reilly Media. 2013. ISBN 9781449335922.
  6. ^ Hennessy, John L.; Patterson, David A.; Larus, James R. (1999). Computer organization and design: the hardware/software interface (2. ed., 3rd print. ed.). San Francisco: Kaufmann. ISBN 978-1-55860-428-5.