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Energy Consumption Comparison of Parallel Linear Systems Solver Algorithms on HPC Infrastructure
SessionThe 1st International Workshop on the Environmental Sustainability of High-Performance Software
DescriptionHigh-Performance Computing (HPC) systems today are gradually increasing in size and complexity due to the correspondent demand for ever-increasing computing needs, requiring more complicated tasks and higher accuracy. The growing energy demands of HPC systems necessitate the urgent adoption of green HPC approaches to mitigate environmental impact and promote energy-efficient computing.
This paper explores a monitoring solution for the energy values detected during the execution of two parallel algorithms for the solution of linear systems: the Inhibition Method and Gaussian Elimination from ScaLAPACK library. The main goal is to profile their execution from the energy consumption perspective. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, which will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.
This paper explores a monitoring solution for the energy values detected during the execution of two parallel algorithms for the solution of linear systems: the Inhibition Method and Gaussian Elimination from ScaLAPACK library. The main goal is to profile their execution from the energy consumption perspective. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, which will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.
Event Type
Workshop
TimeSunday, 12 November 20232:30pm - 2:55pm MST
Location603
Artificial Intelligence/Machine Learning
Energy Efficiency
Green Computing
Performance Measurement, Modeling, and Tools
Sustainability
W