cfaed Publications

Domain specific languages to tame heterogeneous and emerging computing systems

Reference

Jeronimo Castrillon, "Domain specific languages to tame heterogeneous and emerging computing systems", In ACM SIGHPC conference Platform for Advanced Scientific Computing PASC'21 (keynote), Jul 2021.

Abstract

Programming heterogeneous computing systems is still a daunting task that will become even more challenging with the advent of emerging computer architectures. This complexity will make it harder to democratize high-performance computing, which already today highly relies on expert programmers to write efficient parallel code. This talk discusses domain specific languages (DSLs) as a promising avenue to tame heterogeneity for non-expert programmers. The high-level semantics in DSLs improves productivity while enabling coarser-grained optimization and safer code generation. Examples are provided from the domains of big-data, physics simulations and machine learning. The talk closes with insights on how compilers can leverage the high-level semantics of DSLs to optimize for emerging memory technologies.

Bibtex

@Misc{castrillon_pasc21,
author = {Castrillon, Jeronimo},
title = {Domain specific languages to tame heterogeneous and emerging computing systems},
howpublished = {ACM SIGHPC conference Platform for Advanced Scientific Computing PASC'21 (keynote)},
location = {Geneva (virtual), Switzerland},
abstract = {Programming heterogeneous computing systems is still a daunting task that will become even more challenging with the advent of emerging computer architectures. This complexity will make it harder to democratize high-performance computing, which already today highly relies on expert programmers to write efficient parallel code. This talk discusses domain specific languages (DSLs) as a promising avenue to tame heterogeneity for non-expert programmers. The high-level semantics in DSLs improves productivity while enabling coarser-grained optimization and safer code generation. Examples are provided from the domains of big-data, physics simulations and machine learning. The talk closes with insights on how compilers can leverage the high-level semantics of DSLs to optimize for emerging memory technologies.},
month = jul,
year = {2021},
}

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210709_castrillon_PASC-sent [PDF]

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https://esim-project.eu/publications?pubId=3170


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