poster

The boat hull model: adapting the roofline model to enable performance prediction for parallel computing

Authors:

Cedric Nugteren,

Henk CorporaalAuthors Info & Claims

ACM SIGPLAN Notices, Volume 47, Issue 8

Pages 291 - 292

https://doi.org/10.1145/2370036.2145859

Published: 25 February 2012 Publication History

Get Access

Abstract

Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application.

In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the boat hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.

References

[1]

S. W. Keckler, W. J. Dally, B. Khailany, M. Garland, and D. Glasco. GPUs and the Future of Parallel Computing. IEEE Micro, 31:7--17, September 2011.

Digital Library

Google Scholar

[2]

C. Nugteren and H. Corporaal. A Modular and Parameterisable Classification of Algorithms. Technical Report No. ESR-2011-02, Eindhoven University of Technology, 2011.

Google Scholar

[3]

S. Williams, A. Waterman, and D. Patterson. Roofline: an Insightful Visual Performance Model for Multicore Architectures. Communications of the ACM, 52:65--76, April 2009.

Digital Library

Google Scholar

Cited By

View all

Trümper LBen-Nun TSchaad PCalotoiu AHoefler TGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance OptimizationProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593714(50-62)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593714
Saussard RBouzid BVasiliu MReynaud R(2018)A novel global methodology to analyze the embeddability of real-time image processing algorithmsJournal of Real-Time Image Processing10.1007/s11554-017-0686-314:3(565-583)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1007/s11554-017-0686-3
Moolchandani DGupta SKumar ASarangi S(2020)Performance Prediction for Multi-Application Concurrency on GPUs2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS48437.2020.00050(306-315)Online publication date: Aug-2020
https://doi.org/10.1109/ISPASS48437.2020.00050
Show More Cited By

Index Terms

The boat hull model: adapting the roofline model to enable performance prediction for parallel computing
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Recommendations

The boat hull model: adapting the roofline model to enable performance prediction for parallel computing
PPoPP '12: Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming

Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given ...
The boat hull model: enabling performance prediction for parallel computing prior to code development
CF '12: Proceedings of the 9th conference on Computing Frontiers

Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given ...
Roofline-aware DVFS for GPUs
ADAPT '14: Proceedings of International Workshop on Adaptive Self-tuning Computing Systems

Graphics processing units (GPUs) are becoming increasingly popular for compute workloads, mainly because of their large number of processing elements and high-bandwidth to off-chip memory. The roofline model captures the ratio between the two (the ...

Comments

Information & Contributors

Information

Published In

ACM SIGPLAN Notices Volume 47, Issue 8

PPOPP '12

August 2012

334 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2370036

Issue’s Table of Contents

PPoPP '12: Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
February 2012
352 pages
ISBN:9781450311601
DOI:10.1145/2145816
General Chair:
J. Ramanujam
Louisiana State University, USA
,
Program Chair:
P. Sadayappan
The Ohio State University, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2012

Published in SIGPLAN Volume 47, Issue 8

Check for updates

Author Tags

Qualifiers

Poster

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
494
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Trümper LBen-Nun TSchaad PCalotoiu AHoefler TGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance OptimizationProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593714(50-62)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593714
Saussard RBouzid BVasiliu MReynaud R(2018)A novel global methodology to analyze the embeddability of real-time image processing algorithmsJournal of Real-Time Image Processing10.1007/s11554-017-0686-314:3(565-583)Online publication date: 20-Dec-2018
https://dl.acm.org/doi/10.1007/s11554-017-0686-3
Moolchandani DGupta SKumar ASarangi S(2020)Performance Prediction for Multi-Application Concurrency on GPUs2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS48437.2020.00050(306-315)Online publication date: Aug-2020
https://doi.org/10.1109/ISPASS48437.2020.00050
Cardwell DSong F(2019)An Extended Roofline Model with Communication-Awareness for Distributed-Memory HPC SystemsProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3293320.3293321(26-35)Online publication date: 14-Jan-2019
https://dl.acm.org/doi/10.1145/3293320.3293321
Tang LBarrett RCook JHu X(2017)PeaPawACM Transactions on Design Automation of Electronic Systems10.1145/299954022:3(1-26)Online publication date: 10-Mar-2017
https://dl.acm.org/doi/10.1145/2999540
Nowatzki TSankaralingam K(2016)Analyzing Behavior Specialized AccelerationACM SIGARCH Computer Architecture News10.1145/2980024.287241244:2(697-711)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2980024.2872412
Nowatzki TSankaralingam K(2016)Analyzing Behavior Specialized AccelerationACM SIGOPS Operating Systems Review10.1145/2954680.287241250:2(697-711)Online publication date: 25-Mar-2016
https://doi.org/10.1145/2954680.2872412
Nowatzki TSankaralingam K(2016)Analyzing Behavior Specialized AccelerationACM SIGPLAN Notices10.1145/2954679.287241251:4(697-711)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2954679.2872412
Nowatzki TSankaralingam KConte TZhou Y(2016)Analyzing Behavior Specialized AccelerationProceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/2872362.2872412(697-711)Online publication date: 25-Mar-2016
https://dl.acm.org/doi/10.1145/2872362.2872412
Lemeire JCornelis JSegers L(2016)Microbenchmarks for GPU Characteristics: The Occupancy Roofline and the Pipeline Model2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)10.1109/PDP.2016.120(456-463)Online publication date: Feb-2016
https://doi.org/10.1109/PDP.2016.120
Show More Cited By

View Options

Login options

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

Cited By

Index Terms

Recommendations

The boat hull model: adapting the roofline model to enable performance prediction for parallel computing

The boat hull model: enabling performance prediction for parallel computing prior to code development

Roofline-aware DVFS for GPUs