cfaed Publications

Problem Solving Environment and Compiler Optimizations for High-Performance Particle-Mesh Numerical Simulations

Reference

Nesrine Khouzami, Jeronimo Castrillon, "Problem Solving Environment and Compiler Optimizations for High-Performance Particle-Mesh Numerical Simulations", Supercomputing Conference (SC 2022) - Women in HPC Workshop (WHPC), Nov 2022.

Abstract

We present OpenPME (Open Particle-Mesh Environment), a Problem Solving Environment (PSE) which provides a Domain Specific Language (DSL) built atop a domain model general enough to write numerical simulations in scientific computing using particle-mesh abstractions. This helps to close the productivity gap in HPC applications and effectively lowers the programming barrier to enable the smooth implementation of scalable simulations. We also introduce a model-based autotuning approach of discretization methods for OpenPME compiler. We evaluate the autotuner in two diffusion test cases and the results show that we consistently find configurations that outperform those found by state-of-the-art general-purpose autotuners.

Bibtex

@InProceedings{khouzami_sc_whpc22,
author = {Nesrine Khouzami and Jeronimo Castrillon},
title = {Problem Solving Environment and Compiler Optimizations for High-Performance Particle-Mesh Numerical Simulations},
location = {Dallas, Texas},
publisher = {Supercomputing Conference (SC 2022) - Women in HPC Workshop (WHPC)},
abstract = {We present OpenPME (Open Particle-Mesh Environment), a Problem Solving Environment (PSE) which provides a Domain Specific Language (DSL) built atop a domain model general enough to write numerical simulations in scientific computing using particle-mesh abstractions. This helps to close the productivity gap in HPC applications and effectively lowers the programming barrier to enable the smooth implementation of scalable simulations. We also introduce a model-based autotuning approach of discretization methods for OpenPME compiler. We evaluate the autotuner in two diffusion test cases and the results show that we consistently find configurations that outperform those found by state-of-the-art general-purpose autotuners.},
month = nov,
numpages = {3},
year = {2022},
}

Downloads

2211_Khouzami_WHPC-SC [PDF]

Permalink

https://esim-project.eu/publications?pubId=3408


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