Dense Matrix Multiplication Algorithms and Performance Evaluation of HPCC in 81 Nodes IBM Power 8 Architecture

Estévez Ruiz, Eduardo Patricio; Caluña, Giovanny; Jiménez Patiño, Fabian Rodolfo; López, Joaquín; Thirumuruganandham, Saravana Prakash

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http://hdl.handle.net/2183/28859

Dense Matrix Multiplication Algorithms and Performance Evaluation of HPCC in 81 Nodes IBM Power 8 Architecture

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2021_Estevez_Ruiz_Dense_matrix_multiplication_algorithms_and_performance_evaluation.pdf (1.5 MB)

Identifiers

URI: http://hdl.handle.net/2183/28859

DOI: 10.3390/computation9080086

Publication date

2021-08

Authors

Estévez Ruiz, Eduardo Patricio

Caluña, Giovanny

Jiménez Patiño, Fabian Rodolfo

López, Joaquín

Thirumuruganandham, Saravana Prakash

Bibliographic citation

Estévez Ruiz, E.P.; Chicaiza, G.E.C.; Patiño, F.R.J.; López Lago, J.C.; Thirumuruganandham, S.P. Dense Matrix Multiplication Algorithms and Performance Evaluation of HPCC in 81 Nodes IBM Power 8 Architecture. Computation 2021, 9, 86. https://doi.org/10.3390/computation9080086

Abstract

[Abstract] Optimizing HPC systems based on performance factors and bottlenecks is essential for designing an HPC infrastructure with the best characteristics and at a reasonable cost. Such insight can only be achieved through a detailed analysis of existing HPC systems and the execution of their workloads. The “Quinde I” is the only and most powerful supercomputer in Ecuador and is currently listed third on the South America. It was built with the IBM Power 8 servers. In this work, we measured its performance using different parameters from High-Performance Computing (HPC) to compare it with theoretical values and values obtained from tests on similar models. To measure its performance, we compiled and ran different benchmarks with the specific optimization flags for Power 8 to get the maximum performance with the current configuration in the hardware installed by the vendor. The inputs of the benchmarks were varied to analyze their impact on the system performance. In addition, we compile and compare the performance of two algorithms for dense matrix multiplication SRUMMA and DGEMM.