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Co-optimizing Dataflow Graphs and Actors with MLIR

Authors :
Pedro Ciambra
Mickacl Dardaillon
Maxime Pelcat
Herve Yviquel
Computer Systems Laboratory [Campinas] (LSC - UNICAMP)
Universidade Estadual de Campinas = University of Campinas (UNICAMP)
Institut d'Électronique et des Technologies du numéRique (IETR)
Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie
Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)
Source :
2022 IEEE Workshop on Signal Processing Systems (SiPS), 2022 IEEE Workshop on Signal Processing Systems (SiPS), Nov 2022, Rennes, France
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

International audience; Dataflow programming is considered a good solution for the implementation of parallel signal processing applications. However, the strict separation between kernel and coordination codes limits the variety of possible optimizations and the compatibility with state-of-the-art compiler frameworks. We present a prototype static dataflow compiler, built with the LLVM MLIR framework, that overcomes these limitations and enables a previously impossible combination of optimization strategies that leverages information from the dataflow topology. Initial results show 30% wall time improvement and 53% memory usage improvement on a video processing workload.

Details

Language :
English
Database :
OpenAIRE
Journal :
2022 IEEE Workshop on Signal Processing Systems (SiPS), 2022 IEEE Workshop on Signal Processing Systems (SiPS), Nov 2022, Rennes, France
Accession number :
edsair.doi.dedup.....206920b0e9cade7dcc780fadd81ea49a