research-article

Autotuning and adaptivity in energy efficient HPC systems: the ANTAREX toolbox

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

Pages 270 - 275

https://doi.org/10.1145/3203217.3205338

Published: 08 May 2018 Publication History

Abstract

Designing and optimizing applications for energy-efficient High Performance Computing systems up to the Exascale era is an extremely challenging problem. This paper presents the toolbox developed in the ANTAREX European project for autotuning and adaptivity in energy efficient HPC systems. In particular, the modules of the ANTAREX toolbox are described as well as some preliminary results of the application to two target use cases. ¹

References

[1]

A. H. Ashouri, A. Bignoli, G. Palermo, C. Silvano, S. Kulkarni, and J. Cavazos. 2017. MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning. ACM TACO 14, 3 (2017), 29:1--29:28.

Digital Library

[2]

A. H. Ashouri, W. Killian, J. Cavazos, G. Palermo, and C. Silvano. 2018. A Survey on Compiler Autotuning using Machine Learning. Comput. Surveys (2018).

[3]

A. H. Ashouri, G. Mariani, G. Palermo, E. Park, J. Cavazos, and C. Silvano. 2016. COBAYN: Compiler Autotuning Framework Using Bayesian Networks. ACM TACO 13, 2, Article 21 (June 2016), 25 pages.

Digital Library

[4]

A. H. Ashouri, G. Palermo, J. Cavazos, and C. Silvano. 2018. Automatic Tuning of Compilers Using Machine Learning. Springer.

Digital Library

[5]

A. Bartolini, R. Diversi, D. Cesarini, and F. Beneventi. 2017. Self-Aware Thermal Management for High Performance Computing Processors. IEEE Design & Test (2017).

[6]

C. Beato, A. Beccari, C. Cavazzoni, S. Lorenzi, and G. Costantino. 2013. Use of experimental design to optimize docking performance: The case of ligendock, the docking module of ligen, a new de novo design program. Journal of Chemical Information and Modeling 53, 6 (2013), 1503--1517.

[7]

A. Beccari, C. Cavazzoni, C. Beato, and Gabriele G. Costantino. 2013. LiGen: a High Performance workflow for chemistry driven de novo design. Journal of Chemical Information and Modeling 53, 6 (2013), 1518--1527.

[8]

F. Beneventi, A. Bartolini, C. Cavazzoni, and L. Benini. 2017. Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools. In Proc. of DATE. 1038--1043.

Digital Library

[9]

J. Cardoso, J. Coutinho, T. Carvalho, P. Diniz, Z. Petrov, W. Luk, and F. Gonçalves. 2016. Performance-driven instrumentation and mapping strategies using the LARA aspect-oriented programming approach. Software: Practice and Experience 46, 2 (2016), 251--287.

Digital Library

[10]

D. Cesarini, A. Bartolini, and L. Benini. 2017. Benefits in Relaxing the Power Capping Constraint. In Proceedings of ANDARE Workshop.

Digital Library

[11]

D. Cesarini, A. Bartolini, and L. Benini. 2017. Prediction horizon vs. efficiency of optimal dynamic thermal control policies in HPC nodes. In 2017 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC).

[12]

S. Cherubin and G. Agosta. 2018. libVersioningCompiler: An easy-to-use library for dynamic generation and invocation of multiple code versions. SoftwareX 7 (2018), 95 -- 100.

[13]

C. Silvano et al. 2016. The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems. In Proc. of the ACM International Conference on Computing Frontiers, CF'16. 288--293.

Digital Library

[14]

C. Silvano et al. 2016. AutoTuning and Adaptivity appRoach for Energy efficient eXascale HPC systems: the ANTAREX Approach. In Proc. of DATE. 1518--1527.

Digital Library

[15]

C. Silvano et al. 2018. ANTAREX: A DSL-based Approach to Adaptively Optimizing and Enforcing Extra-functional Properties in High Performance Computing. In Euromicro Conference on Digital System Design - DSD.

[16]

D. Gadioli, R. Nobre, P. Pinto, E. Vitali, A.H. Ashouri, G. Palermo, C. Silvano, and J. Cardoso. 2018. SOCRATES - A Seamless Online Compiler and System Runtime AutoTuning Framework for Energy-Aware Applications. In Proc. of DATE. 1149--1152.

[17]

M. Golasowski, J. Bispo, J. Martinovič, K. Slaninová, and J. Cardoso. 2017. Expressing and Applying C++ Code Transformations for the HDF5 API Through a DSL. In IFIP International Conference on Computer Information Systems and Industrial Management. 303--314.

[18]

M. Golasowski, R. Tomis, J. Martinovič, K. Slaninová, and L. Rapant. 2016. Performance evaluation of probabilistic time-dependent travel time computation. In IFIP International Conference on Computer Information Systems and Industrial Management. 377--388.

[19]

M. Golasowski R. Cmar J. Cardoso J. Bispo G. Palermo D. Gadioli J. Martinovic, K. SlaninovÃą and C. Silvano. 2016. DSL and Autotuning Tools for Code Optimization on HPC Inspired by Navigation Use Case. In SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

[20]

Jeffrey O Kephart and David M Chess. 2003. The vision of autonomic computing. Computer 36, 1 (2003), 41--50.

Digital Library

[21]

R. Nobre, L. Martins, and J. Cardoso. 2016. A Graph-Based Iterative Compiler Pass Selection and Phase Ordering Approach. (2016), 21--30.

Digital Library

[22]

A. Suresh, E. Rohou, and A. Seznec. 2017. Compile-Time Function Memoization. In 26th International Conference on Compiler Construction. Austin, United States.

Digital Library

[23]

A. Suresh, B. Narasimha Swamy, E. Rohou, and A. Seznec. 2015. Intercepting Functions for Memoization: A Case Study Using Transcendental Functions. ACM TACO 12, 2 (2015), 23.

Digital Library

Cited By

Silva CVilaça RPereira ABessa R(2024)A review on the decarbonization of high-performance computing centersRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114019189(114019)Online publication date: Jan-2024
https://doi.org/10.1016/j.rser.2023.114019
Palermo GAccordi GGadioli DVitali ESilvano CGuindani BArdagna DBeccari ABonanni DTalarico CLughini FMartinovic JSilva PBohm ABeranek JKrenek JJansik BCosenza BCrisci LThoman PSalzmann PFahringer TAlexander LTauriello GSchwede TDurairaj JEmerson AFicarelli FWingbermühle SLindhal EGregori DSana EColetti SGschwandtner PBartolini ARietveld KSchuman CMoreira J(2023)Tunable and Portable Extreme-Scale Drug Discovery Platform at ExascaleProceedings of the 20th ACM International Conference on Computing Frontiers10.1145/3587135.3592172(272-278)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3587135.3592172
Wu XKruse MBalaprakash PFinkel HHovland PTaylor VHall M(2021)Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimizationConcurrency and Computation: Practice and Experience10.1002/cpe.668334:20Online publication date: 8-Nov-2021
https://doi.org/10.1002/cpe.6683
Show More Cited By

Autotuning and adaptivity in energy efficient HPC systems: the ANTAREX toolbox
1. Software and its engineering
  1. Software notations and tools

Recommendations

The ANTAREX approach to autotuning and adaptivity for energy efficient HPC systems
CF '16: Proceedings of the ACM International Conference on Computing Frontiers

The ANTAREX project aims at expressing the application self-adaptivity through a Domain Specific Language (DSL) and to runtime manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exascale. The DSL ...
Optimizing I/O Performance of HPC Applications with Autotuning

Parallel Input output is an essential component of modern high-performance computing (HPC). Obtaining good I/O performance for a broad range of applications on diverse HPC platforms is a major challenge, in part, because of complex inter dependencies ...
Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers
Highlights
- Generate parallel CUDA code from sequential C input code using a compiler-based tool for key operators in Geometric Multigrid.
Abstract
GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

May 2018

401 pages

ISBN:9781450357616

DOI:10.1145/3203217

General Chairs:
David Kaeli
Northeastern University
,
Miquel Pericàs
Chalmers University of Technology, SE

Copyright © 2018 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].

Sponsors

SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

H2020 Future and Emerging Technologies

Conference

CF '18

Sponsor:

SIGMICRO

CF '18: Computing Frontiers Conference

May 8 - 10, 2018

Ischia, Italy

Acceptance Rates

Overall Acceptance Rate 273 of 785 submissions, 35%

Upcoming Conference

CF '25

Sponsor:
sigmicro

22nd ACM International Conference on Computing Frontiers

May 28 - 30, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
163
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Silva CVilaça RPereira ABessa R(2024)A review on the decarbonization of high-performance computing centersRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114019189(114019)Online publication date: Jan-2024
https://doi.org/10.1016/j.rser.2023.114019
Palermo GAccordi GGadioli DVitali ESilvano CGuindani BArdagna DBeccari ABonanni DTalarico CLughini FMartinovic JSilva PBohm ABeranek JKrenek JJansik BCosenza BCrisci LThoman PSalzmann PFahringer TAlexander LTauriello GSchwede TDurairaj JEmerson AFicarelli FWingbermühle SLindhal EGregori DSana EColetti SGschwandtner PBartolini ARietveld KSchuman CMoreira J(2023)Tunable and Portable Extreme-Scale Drug Discovery Platform at ExascaleProceedings of the 20th ACM International Conference on Computing Frontiers10.1145/3587135.3592172(272-278)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3587135.3592172
Wu XKruse MBalaprakash PFinkel HHovland PTaylor VHall M(2021)Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimizationConcurrency and Computation: Practice and Experience10.1002/cpe.668334:20Online publication date: 8-Nov-2021
https://doi.org/10.1002/cpe.6683
Wu XKruse MBalaprakash PFinkel HHovland PTaylor VHall M(2020)Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS)10.1109/PMBS51919.2020.00012(61-70)Online publication date: Nov-2020
https://doi.org/10.1109/PMBS51919.2020.00012
Mosseri IAlon LHarel ROren G(2020)ComPar: Optimized Multi-compiler for Automatic OpenMP S2S ParallelizationOpenMP: Portable Multi-Level Parallelism on Modern Systems10.1007/978-3-030-58144-2_16(247-262)Online publication date: 1-Sep-2020
https://doi.org/10.1007/978-3-030-58144-2_16
Festa MGervasoni NCherubin SAgosta G(2019)Continuous Program Optimization via Advanced Dynamic Compilation TechniquesProceedings of the 10th and 8th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms10.1145/3310411.3310415(1-6)Online publication date: 21-Jan-2019
https://dl.acm.org/doi/10.1145/3310411.3310415
Cattaneo DBello AChiari MCherubin SAgosta GPalumbo FBecchi MSchulz MSato K(2019)Fixed point exploitation via compiler analyses and transformationsProceedings of the 16th ACM International Conference on Computing Frontiers10.1145/3310273.3323424(292-294)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3310273.3323424
Sreenivasan VJavali RHall MBalaprakash PScogland Tde Supinski B(2019)A Framework for Enabling OpenMP AutotuningOpenMP: Conquering the Full Hardware Spectrum10.1007/978-3-030-28596-8_4(50-60)Online publication date: 9-Aug-2019
https://doi.org/10.1007/978-3-030-28596-8_4
Anjum B(2018)An interview with Pamela WisniewskiUbiquity10.1145/33013232018:December(1-6)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1145/3301323
Smirnova ACudré-Mauroux P(2018)Relation Extraction Using Distant SupervisionACM Computing Surveys10.1145/324174151:5(1-35)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3241741
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents