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

Abstract

The article presents a comparative analysis of the effectiveness of using parallelism of varying granularity degrees in modern multicore computer systems using the most popular programming languages and libraries (such as C#, Java, C++, and OpenMP). Based on the performed comparison, the possibilities of increasing the efficiency of computations in multicore computer systems by using combinations of medium- and fine-grained parallelism were also investigated. The results demonstrate the high potential efficiency of fine-grained parallelism when organizing intensive parallel computations. Based on these results, it can be argued that, in comparison with more traditional parallelization methods that use medium-grain parallelism, the use of separately fine-grained parallelism can reduce the computation time of a large mathematical problem by an average of 4%. The use of combined parallelism can reduce the computation time of such a problem to 5,5%. This reduction in execution time can be significant when performing very large computations.

Keywords

• multicore computer system
• core
• thread
• tasks
• parallelism
• granularity
• fork-join
• speedup coefficient
• fine-grained parallelism
• nested parallelism
• combined parallelism