Degree of parallelism

The degree of parallelism (DOP) is a metric which indicates how many operations can be or are being simultaneously executed by a computer. It is used as an indicator of the complexity of algorithms, and is especially useful for describing the performance of parallel programs and multi-processor systems.^[1]

A program running on a parallel computer may utilize different numbers of processors at different times. For each time period, the number of processors used to execute a program is defined as the degree of parallelism. The plot of the DOP as a function of time for a given program is called the parallelism profile.^[2]

References

↑ Deprettere, Ed F.; Takala, Jarmo; Leupers, Rainer et al., eds (13 October 2018). Handbook of Signal Processing Systems. Springer International Publishing. p. 552. ISBN 9783319917344. https://books.google.com/books?id=soJyDwAAQBAJ. Retrieved 12 April 2023.
↑ Wagner, Thomas D.; Carlson, Brian M. (20 April 1994). An Algorithm for Off-Line Detection of Phases in Execution Profiles. ISBN 9783540580218. https://books.google.com/books?id=QzLj_BPUdO0C&dq=%22parallelism+profile%22+%22function+of+time%22&pg=PA253. Retrieved 12 April 2023.

0.00

(0 votes)

Original source: https://en.wikipedia.org/wiki/Degree of parallelism. Read more

[handbook-1] Deprettere, Ed F.; Takala, Jarmo; Leupers, Rainer et al., eds (13 October 2018). Handbook of Signal Processing Systems. Springer International Publishing. p. 552. ISBN 9783319917344. https://books.google.com/books?id=soJyDwAAQBAJ. Retrieved 12 April 2023.

[wagnercarlson-2] Wagner, Thomas D.; Carlson, Brian M. (20 April 1994). An Algorithm for Off-Line Detection of Phases in Execution Profiles. ISBN 9783540580218. https://books.google.com/books?id=QzLj_BPUdO0C&dq=%22parallelism+profile%22+%22function+of+time%22&pg=PA253. Retrieved 12 April 2023.

[1]

[2]

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) 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 data structure
Coordination	Multiprocessing Memory coherency Cache coherency 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 SIMT 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 Grid computer Hardware acceleration
APIs	Ateji PX Boost.Thread Chapel HPX Charm++ Cilk Coarray Fortran CUDA HIP Dryad C++ AMP Global Arrays MPI OpenMP OpenCL OpenHMPP OpenACC TPL PLINQ PVM POSIX Threads RaftLib UPC TBB ZPL
Problems	Automatic parallelization Deadlock Livelock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation
Category: parallel computing Media related to Parallel computing at Wikimedia Commons

Degree of parallelism

See also

References