Open AccessEmpirical investigation: performance and power‐consumption based dual‐level model for exascale computing systems
Author(s) -
Ashraf Muhammad Usman,
Eassa Fathy Alboraei,
Ahmad Aiiad,
Algarni Abdullah
Publication year - 2020
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2018.5062
Subject(s) - computer science , parallel computing , speedup , exascale computing , supercomputer , petascale computing , programming paradigm , symmetric multiprocessor system , solver , parallel programming model , distributed computing , computer architecture , programming language
Exascale computing systems (ECS) are anticipated to perform at Exaflop speed (10 18 operations per second) using power consumption <20 MW. This ultrascale performance requires the speedup in the system by thousand‐fold enhancement in current Petascale. For future high‐performance computing (HPC), power consumption is one of the vital challenges faced to achieve Exaflops through the traditional way of increasing clock‐speed. One standard way to attain such significant performance is through massive parallelism. In the early stages, it is hard to decide the promising parallel programming approach that can provide massive parallelism to attain ExaFlops. This article commences with a short description and implementation of algorithms of various hybrid parallel programming models (PPMs) for homogeneous and heterogeneous cluster systems. Furthermore, the authors evaluated performance and power consumption in these hybrid models by implementing in two HPC benchmarking applications such as square matrix multiplication and Jacobi iterative solver for two‐dimensional Laplace equation. The results demonstrated that the hybrid of heterogeneous (MPI + X ) outperformed to homogeneous parallel programming (MPI + OpenMP) model. This empirical investigation of hybrid PPMs is a leading step for researchers and development communities to select a promising model for emerging ECS.