research-article

Affinity-aware work-stealing for integrated CPU-GPU processors

Authors:

Karsten SchwanAuthors Info & Claims

PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Article No.: 30, Pages 1 - 2

https://doi.org/10.1145/2851141.2851194

Published: 27 February 2016 Publication History

Get Access

Abstract

Recent integrated CPU-GPU processors like Intel's Broadwell and AMD's Kaveri support hardware CPU-GPU shared virtual memory, atomic operations, and memory coherency. This enables fine-grained CPU-GPU work-stealing, but architectural differences between the CPU and GPU hurt the performance of traditionally-implemented work-stealing on such processors. These architectural differences include different clock frequencies, atomic operation costs, and cache and shared memory latencies. This paper describes a preliminary implementation of our work-stealing scheduler, Libra, which includes techniques to deal with these architectural differences in integrated CPU-GPU processors. Libra's affinity-aware techniques achieve significant performance gains over classically-implemented work-stealing. We show preliminary results using a diverse set of nine regular and irregular workloads running on an Intel Broadwell Core-M processor. Libra currently achieves up to a 2× performance improvement over classical work-stealing, with a 20% average improvement.

References

[1]

Intel thread building blocks. URL www.threadbuildingblocks.org.

Google Scholar

[2]

C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier. Starpu: a unified platform for task scheduling on heterogeneous multicore architectures. Concurrency and Computation: Practice and Experience, 23 (2):187--198, 2011.

Digital Library

Google Scholar

[3]

R. D. Blumofe and C. E. Leiserson. Scheduling multithreaded computations by work stealing. J. ACM, 46(5):720--748, Sept. 1999. ISSN 0004-5411.

Digital Library

Google Scholar

[4]

Y. Guo, J. Zhao, V. Cave, and V. Sarkar. Slaw: A scalable locality-aware adaptive work-stealing scheduler for multi-core systems. In Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '10, pages 341--342, New York, NY, USA, 2010. ACM. ISBN 978-1-60558-877-3. URL http://doi.acm.org/10.1145/1693453.1693504.

Digital Library

Google Scholar

[5]

S. jai Min, C. Iancu, and K. Yelick. Hierarchical work stealing on manycore clusters. In In Fifth Conference on Partitioned Global Address Space Programming Models, 2011.

Google Scholar

[6]

R. Kaleem, R. Barik, T. Shpeisman, B. T. Lewis, C. Hu, and K. Pingali. Adaptive heterogeneous scheduling for integrated gpus. In Proceedings of the 23rd International Conference on Parallel Architectures and Compilation, PACT '14, pages 151--162, New York, NY, USA, 2014. ACM. ISBN 978-1-4503-2809-8. URL http://doi.acm.org/10.1145/2628071.2628088.

Digital Library

Google Scholar

[7]

J. Lee, M. Samadi, Y. Park, and S. Mahlke. Transparent CPU-GPU collaboration for data-parallel kernels on heterogeneous systems. In Proceedings of the 22nd international conference on Parallel architectures and compilation techniques, PACT, 2013.

Digital Library

Google Scholar

[8]

C.-K. Luk, S. Hong, and H. Kim. Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 42, pages 45--55, NY, USA, 2009. ACM. ISBN 978-1-60558-798-1. URL http://doi.acm.org/10.1145/1669112.1669121.

Digital Library

Google Scholar

Cited By

View all

Klinkenberg JKraus JTerboven CMüller M(2024)Towards Locality-Aware Host-to-Device Offloading in OpenMPAdvancing OpenMP for Future Accelerators10.1007/978-3-031-72567-8_1(3-15)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72567-8_1
Iorga DDonaldson ASorensen TWickerson J(2021)The semantics of shared memory in Intel CPU/FPGA systemsProceedings of the ACM on Programming Languages10.1145/34854975:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485497
Kulkarni AAnnappa B(2021)GPU-aware resource management in heterogeneous cloud data centersThe Journal of Supercomputing10.1007/s11227-021-03779-4Online publication date: 8-Apr-2021
https://doi.org/10.1007/s11227-021-03779-4
Show More Cited By

Recommendations

Affinity-aware work-stealing for integrated CPU-GPU processors
PPoPP '16

Recent integrated CPU-GPU processors like Intel's Broadwell and AMD's Kaveri support hardware CPU-GPU shared virtual memory, atomic operations, and memory coherency. This enables fine-grained CPU-GPU work-stealing, but architectural differences between ...
Understanding Co-Running Behaviors on Integrated CPU/GPU Architectures

Architecture designers tend to integrate both CPUs and GPUs on the same chip to deliver energy-efficient designs. It is still an open problem to effectively leverage the advantages of both CPUs and GPUs on integrated architectures. In this work, we port ...
Optimized HPL for AMD GPU and multi-core CPU usage

The installation of the LOEWE-CSC ( http://csc.uni-frankfurt.de/csc/__ __51 ) supercomputer at the Goethe University in Frankfurt lead to the development of a Linpack which can fully utilize the installed AMD Cypress GPUs. At its core, a fast DGEMM for ...

Comments

Information & Contributors

Information

Published In

PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 2016

420 pages

ISBN:9781450340922

DOI:10.1145/2851141

General Chair:
Rafael Asenjo
University of Málaga, Spain
,
Program Chair:
Tim Harris
Oracle Labs, Cambridge, UK

ACM SIGPLAN Notices Volume 51, Issue 8
PPoPP '16
August 2016
405 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3016078
Editor:
Matthew Fluet
Issue’s Table of Contents

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 ACM 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 February 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

PPoPP '16

Sponsor:

PPoPP '16: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

March 12 - 16, 2016

Barcelona, Spain

Acceptance Rates

Overall Acceptance Rate 230 of 1,014 submissions, 23%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
484
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)0

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Klinkenberg JKraus JTerboven CMüller M(2024)Towards Locality-Aware Host-to-Device Offloading in OpenMPAdvancing OpenMP for Future Accelerators10.1007/978-3-031-72567-8_1(3-15)Online publication date: 23-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72567-8_1
Iorga DDonaldson ASorensen TWickerson J(2021)The semantics of shared memory in Intel CPU/FPGA systemsProceedings of the ACM on Programming Languages10.1145/34854975:OOPSLA(1-28)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485497
Kulkarni AAnnappa B(2021)GPU-aware resource management in heterogeneous cloud data centersThe Journal of Supercomputing10.1007/s11227-021-03779-4Online publication date: 8-Apr-2021
https://doi.org/10.1007/s11227-021-03779-4
Hong CSpence INikolopoulos D(2017)GPU Virtualization and Scheduling MethodsACM Computing Surveys10.1145/306828150:3(1-37)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3068281
Farooqui NKaeli DCavazos J(2016)A systems perspective on GPU computingProceedings of the 9th Annual Workshop on General Purpose Processing using Graphics Processing Unit10.1145/2884045.2884057(72-81)Online publication date: 12-Mar-2016
https://dl.acm.org/doi/10.1145/2884045.2884057
Iorga DDonaldson ASorensen TWickerson J(2021)The semantics of shared memory in Intel CPU/FPGA systemsProceedings of the ACM on Programming Languages10.1145/34854975:OOPSLA(1-28)Online publication date: 20-Oct-2021
https://doi.org/10.1145/3485497

View Options

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations