cfaed Publications

Domain-specific programming methodologies for domain-specific and emerging computing systems

Reference

Jeronimo Castrillon, "Domain-specific programming methodologies for domain-specific and emerging computing systems", In 8th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), co-located with the International Conference on High Performance Computing, Networking, Storage and Analysis (SC23) (invited talk), Nov 2023.

Abstract

Programming heterogeneous computing systems is a daunting task which is becoming even more challenging with the advent of emerging, non Von-Neumann computer architectures. Innovation in programming abstractions and compilers are thus badly needed to cope with the current golden age of computer architecture. This talk discusses domain-specific abstractions and languages as a promising avenue to hide the system complexity from non-expert programmers while passing richer information to compilers. 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, targeting modern reconfigurable hardware, for emerging memory technologies and for emerging in-memory computing.

Bibtex

@Misc{castrillon_espm2023,
author = {Castrillon, Jeronimo},
date = {2023-11},
title = {Domain-specific programming methodologies for domain-specific and emerging computing systems},
howpublished = {8th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), co-located with the International Conference on High Performance Computing, Networking, Storage and Analysis (SC23) (invited talk)},
location = {Denver, CA, USA},
abstract = {Programming heterogeneous computing systems is a daunting task which is becoming even more challenging with the advent of emerging, non Von-Neumann computer architectures. Innovation in programming abstractions and compilers are thus badly needed to cope with the current golden age of computer architecture. This talk discusses domain-specific abstractions and languages as a promising avenue to hide the system complexity from non-expert programmers while passing richer information to compilers. 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, targeting modern reconfigurable hardware, for emerging memory technologies and for emerging in-memory computing.},
month = nov,
year = {2023},
}

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