Assessment of OpenMP Master–Slave Implementations for Selected Irregular Parallel Applications

Abstract

The paper investigates various implementations of a master–slave paradigm using the popular OpenMP API and relative performance of the former using modern multi-core workstation CPUs. It is assumed that a master partitions available input into a batch of predefined number of data chunks which are then processed in parallel by a set of slaves and the procedure is repeated until all input data has been processed. The paper experimentally assesses performance of six implementations using OpenMP locks, the tasking construct, dynamically partitioned for loop, without and with overlapping merging results and data generation, using the gcc compiler. Two distinct parallel applications are tested, each using the six aforementioned implementations, on two systems representing desktop and worstation environments: one with Intel i7-7700 3.60GHz Kaby Lake CPU and eight logical processors and the other with two Intel Xeon E5-2620 v4 2.10GHz Broadwell CPUs and 32 logical processors. From the application point of view, irregular adaptive quadrature numerical integration, as well as finding a region of interest within an irregular image is tested. Various compute intensities are investigated through setting various computing accuracy per subrange and number of image passes, respectively. Results allow programmers to assess which solution and configuration settings such as the numbers of threads and thread affinities shall be preferred.

Performance assessment of OpenMP constructs and benchmarks using modern compilers and multi-core CPUs

“…In paper [5] All in all, for integration the best results were obtained for tasking and dynamic for with or without overlapping (for systems 2 and 1) while for image recognition for system 1 dynamic for and using locks while for system 2 dynamic for (both versions) and tasking with overlapping. Scalability and overheads during execution of parallel code is studied in more detail in [6] where authors distinguished 4 overhead categories such as: need for synchronization among threads, imbalance, limited parallelism i.e.…”

Comparative analysis of the effectiveness of using fine-grained and nested parallelism to increase the speedup of parallel computing in multicore computer systems

“…Among the most popular languages and libraries currently used in high-load computing, fine-grained parallelism tools are implemented in the OpenMP library (which is usually used in combination with C++), Java and C# languages [11][12][13][14].…”