Comparison of speedups for computing π using .NET TPL and OpenMP parallelization techonologies
Martina Vistica, Hana Haseljić, Amna Maksumic, Novica Nosović
Abstract
Martina Vistica, Hana Haseljić, Amna Maksumic, Novica Nosović
Abstract
Paper presents speedup achieved through parallelization of code for computing π. Codes are implemented in C# with .NET framework and in C with OpenMP, on machine with i7 processor. Parallelization of code, more precisely embarassingly parallel problem, should show linear speedup, but as as shown in the following paper the same was not proven to be right. The differences in speedup between OpenMP and Task Parallel Library, hereinafter referenced as TPL are demonstrated by calculating speedup in different scenarios. Problem remains the same through the scenarios, but the number of iterations and the number of cores activated are changed. Finally, results are presented comparing the time needed for execution of serial and parallel computing. Ultimately, the results show that OpenMP is parallelization tool that is adviced to use while solving problems similar to the problem that is considered in this paper.
OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Paper presents speedup achieved through parallelization of code for computing π. Codes are implemented in C# with .NET framework and in C with OpenMP, on machine with i7 processor. Parallelization of code, more precisely embarassingly parallel problem, should show linear speedup, but as as shown in the following paper the same was not proven to be right. The differences in speedup between OpenMP and Task Parallel Library, hereinafter referenced as TPL are demonstrated by calculating speedup in different scenarios. Problem remains the same through the scenarios, but the number of iterations and the number of cores activated are changed. Finally, results are presented comparing the time needed for execution of serial and parallel computing. Ultimately, the results show that OpenMP is parallelization tool that is adviced to use while solving problems similar to the problem that is considered in this paper.
Key concepts: Speedup, Parallel computing, Computer science, Code (set theory), Task (project management), Automatic parallelization, Parallel algorithm, Programming language