research-article

Autotuning of MPI Applications Using PTF

Authors:

Eduardo César,

Isaías Comprés,

Michael GerndtAuthors Info & Claims

SEM4HPC '16: Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications

Pages 31 - 38

https://doi.org/10.1145/2916026.2916028

Published: 31 May 2016 Publication History

Abstract

The main problem when trying to optimize the parameters of libraries, such as MPI, is that there are many parameters that users can configure. Moreover, predicting the behavior of the library for each configuration is non-trivial. This makes it very difficult to select good values for these parameters. This paper proposes a model for autotuning MPI applications. The model is developed to analyze different parameter configurations and is expected to aid users to find the best performance for executing their applications. As part of the AutoTune project, our work is ultimately aiming at extending Periscope to apply automatic tuning to parallel applications. In particular, our objective is to provide a straightforward way of tuning MPI parallel codes. The output of the framework are tuning recommendations that can be integrated into the production version of the code. Experimental tests demonstrate that this methodology could lead to significant performance improvements.

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Cited By

Kruse DSchonbein WDosanjh M(2023)A Statistical Analysis of HPC Network TuningProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624114(458-465)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624114
Schulz MHermanns MKnobloch MMohror KHjelm NElis BKraljic KYang D(2021)The MPI Tool Interfaces: Past, Present, and Future—Capabilities and ProspectsTools for High Performance Computing 2018 / 201910.1007/978-3-030-66057-4_3(55-83)Online publication date: 23-May-2021
https://doi.org/10.1007/978-3-030-66057-4_3
Hunold SBhatele ABosilca GKnees P(2020)Predicting MPI Collective Communication Performance Using Machine Learning2020 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER49012.2020.00036(259-269)Online publication date: Sep-2020
https://doi.org/10.1109/CLUSTER49012.2020.00036
Show More Cited By

Index Terms

Autotuning of MPI Applications Using PTF
1. Computing methodologies
  1. Parallel computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Parallel algorithms

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Information & Contributors

Information

Published In

cover image ACM Conferences

SEM4HPC '16: Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications

May 2016

54 pages

ISBN:9781450343510

DOI:10.1145/2916026

Program Chairs:
Atul Kumar
IBM Research, India
,
Santonu Sarkar
BITS Pilani Goa Campus, India
,
Michael Gerndt
Technical University of Munich, Germany

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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University of Arizona: University of Arizona
SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2016

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Research-article

Funding Sources

EU Horizon 2020 project RADEX
MINECO-Spain
GenCat-DIUiE(GRR)
EU FP7 project AutoTune

Conference

HPDC'16

Sponsor:

University of Arizona
SIGARCH

HPDC'16: The 25th International Symposium on High-Performance Parallel and Distributed Computing

May 31, 2016

Kyoto, Japan

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SEM4HPC '16 Paper Acceptance Rate 5 of 11 submissions, 45%;

Overall Acceptance Rate 8 of 16 submissions, 50%

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Citations

Cited By

Kruse DSchonbein WDosanjh M(2023)A Statistical Analysis of HPC Network TuningProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624114(458-465)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624114
Schulz MHermanns MKnobloch MMohror KHjelm NElis BKraljic KYang D(2021)The MPI Tool Interfaces: Past, Present, and Future—Capabilities and ProspectsTools for High Performance Computing 2018 / 201910.1007/978-3-030-66057-4_3(55-83)Online publication date: 23-May-2021
https://doi.org/10.1007/978-3-030-66057-4_3
Hunold SBhatele ABosilca GKnees P(2020)Predicting MPI Collective Communication Performance Using Machine Learning2020 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER49012.2020.00036(259-269)Online publication date: Sep-2020
https://doi.org/10.1109/CLUSTER49012.2020.00036
Kalinnik NKiesel RRauber TRichter MRünger G(2020)A performance- and energy-oriented extended tuning process for time-step-based scientific applicationsThe Journal of Supercomputing10.1007/s11227-020-03402-yOnline publication date: 25-Aug-2020
https://doi.org/10.1007/s11227-020-03402-y
Czarnul PRosciszewski P(2019)Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge2019 International Conference on High Performance Computing & Simulation (HPCS)10.1109/HPCS48598.2019.9188060(551-558)Online publication date: Jul-2019
https://doi.org/10.1109/HPCS48598.2019.9188060
Chakraborty SBayatpour MHashmi JSubramoni HPanda D(2018)Cooperative rendezvous protocols for improved performance and overlapProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291694(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.5555/3291656.3291694
Alcaraz JSikora ACesar E(2018)Dynamic Tuning of OpenMP Memory Bound Applications in Multisocket Systems using MATEWorkshop Proceedings of the 47th International Conference on Parallel Processing10.1145/3229710.3229748(1-10)Online publication date: 13-Aug-2018
https://dl.acm.org/doi/10.1145/3229710.3229748
Hunold SCarpen-Amarie A(2018)Autotuning MPI Collectives using Performance GuidelinesProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3149457.3149461(64-74)Online publication date: 28-Jan-2018
https://dl.acm.org/doi/10.1145/3149457.3149461
Chakraborty SBayatpour MHashmi JSubramoni HPanda D(2018)Cooperative rendezvous protocols for improved performance and overlapProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00031(1-13)Online publication date: 11-Nov-2018
https://dl.acm.org/doi/10.1109/SC.2018.00031
Ramesh SMahéo AShende SMalony ASubramoni HPanda DPeña ABalaji PGropp WThakur R(2017)MPI performance engineering with the MPI tool interfaceProceedings of the 24th European MPI Users' Group Meeting10.1145/3127024.3127036(1-11)Online publication date: 25-Sep-2017
https://dl.acm.org/doi/10.1145/3127024.3127036
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