Explicit parallelism

In computer programming, explicit parallelism is the representation of concurrent computations using primitives in the form of operators, function calls or special-purpose directives.^[1] Most parallel primitives are related to process synchronization, communication and process partitioning.^[2] As they seldom contribute to actually carry out the intended computation of the program but, rather, structure it, their computational cost is often considered as overhead.

The advantage of explicit parallel programming is increased programmer control over the computation. A skilled parallel programmer may take advantage of explicit parallelism to produce efficient code for a given target computation environment. However, programming with explicit parallelism is often difficult, especially for non-computing specialists, because of the extra work and skill involved in developing it.

In some instances, explicit parallelism may be avoided with the use of an optimizing compiler or runtime that automatically deduces the parallelism inherent to computations, known as implicit parallelism.

Programming languages that support explicit parallelism

Some of the programming languages that support explicit parallelism are:

References

^ von Praun, Christoph (June 2011). Parallel programming: design of an overview class. Proceedings of the 2011 ACM SIGPLAN X10 Workshop. pp. 1–6. doi:10.1145/2212736.2212738.
^ Dijkstra, Edsger W. (May 1, 1968). "The structure of the "THE"-multiprogramming system". Communications of the ACM. 11 (5): 341–346. doi:10.1145/363095.363143.

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[pra11-1] von Praun, Christoph (June 2011). Parallel programming: design of an overview class. Proceedings of the 2011 ACM SIGPLAN X10 Workshop. pp. 1–6. doi:10.1145/2212736.2212738.

[dij68-2] Dijkstra, Edsger W. (May 1, 1968). "The structure of the "THE"-multiprogramming system". Communications of the ACM. 11 (5): 341–346. doi:10.1145/363095.363143.

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v t e Parallel computing
General	Distributed computing Parallel computing Massively parallel Cloud computing High-performance computing Multiprocessing Manycore processor GPGPU Computer network Systolic array
Levels	Bit Instruction Thread Task Data Memory Loop Pipeline
Multithreading	Temporal Simultaneous (SMT) Simultaneous and heterogenous Speculative (SpMT) Preemptive Cooperative Clustered multi-thread (CMT) Hardware scout
Theory	PRAM model PEM model Analysis of parallel algorithms Amdahl's law Gustafson's law Cost efficiency Karp–Flatt metric Slowdown Speedup
Elements	Process Thread Fiber Instruction window Array
Coordination	Multiprocessing Memory coherence Cache coherence Cache invalidation Barrier Synchronization Application checkpointing
Programming	Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm
Hardware	Flynn's taxonomy SISD SIMD Array processing (SIMT) Pipelined processing Associative processing MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Beowulf cluster Grid computer Hardware acceleration
APIs	Ateji PX Boost Chapel HPX Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays GPUOpen MPI OpenMP OpenCL OpenHMPP OpenACC Parallel Extensions PVM pthreads RaftLib ROCm UPC TBB ZPL
Problems	Automatic parallelization Deadlock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation
Category: Parallel computing