Adaptive-Compi: Enhancing Mpi-Based Applications’ Performance and Scalability by using Adaptive Compression

Author:

Filgueira Rosa1,Singh David E.2,Carretero Jesús2,Calderón Alejandro3,García Félix2

Affiliation:

1. Computer Science Department at the Carlos III University of Madrid, Spain,

2. University Carlos III of Madrid, Spain

3. Carlos III University of Madrid, Spain

Abstract

This paper presents an optimization of MPI communication, called Adaptive-CoMPI, based on runtime compression of MPI messages exchanged by applications. The technique developed can be used for any application, because its implementation is transparent for the user, and integrates different compression algorithms for both MPI collective and point-to-point primitives. Furthermore, compression is turned on and off and the most appropriate compression algorithms are selected at runtime, depending on the characteristics of each message, the network behavior, and compression algorithm behavior, following a runtime adaptive strategy. Our system can be optimized for a specific application, through a guided strategy, to reduce the runtime strategy overhead. Adaptive-CoMPI has been validated using several MPI benchmarks and real HPC applications. Results show that, in most cases, by using adaptive compression, communication time is reduced, enhancing application performance and scalability.

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

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1. Implications of Reduced Communication Precision in a Collocated Discontinuous Galerkin Finite Element Framework;2021 IEEE High Performance Extreme Computing Conference (HPEC);2021-09-20

2. Parallel I/O on Compressed Data Files: Semantics, Algorithms, and Performance Evaluation;2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID);2020-05

3. Compression Challenges in Large Scale Partial Differential Equation Solvers;Algorithms;2019-09-17

4. Lossy data compression reduces communication time in hybrid time-parallel integrators;Computing and Visualization in Science;2018-05-29

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