research-article

Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures

Authors:

Kallia Chronaki,

Alejandro Rico,

Eduard Ayguadé,

Jesús Labarta,

Mateo ValeroAuthors Info & Claims

ICS '15: Proceedings of the 29th ACM on International Conference on Supercomputing

Pages 329 - 338

https://doi.org/10.1145/2751205.2751235

Published: 08 June 2015 Publication History

Abstract

Current and future parallel programming models need to be portable and efficient when moving to heterogeneous multi-core systems. OmpSs is a task-based programming model with dependency tracking and dynamic scheduling. This paper describes the OmpSs approach on scheduling dependent tasks onto the asymmetric cores of a heterogeneous system. The proposed scheduling policy improves performance by prioritizing the newly-created tasks at runtime, detecting the longest path of the dynamic task dependency graph, and assigning critical tasks to fast cores. While previous works use profiling information and are static, this dynamic scheduling approach uses information that is discoverable at runtime which makes it implementable and functional without the need of an oracle or profiling. The evaluation results show that our proposal outperforms a dynamic implementation of Heterogeneous Earliest Finish Time by up to 1.15x, and the default breadth-first OmpSs scheduler by up to 1.3x in an 8-core heterogeneous platform and up to 2.7x in a simulated 128-core chip.

References

[1]

T. L. Adam, K. M. Chandy, and J. R. Dickson. A Comparison of List Schedules for Parallel Processing Systems. Commun. ACM, 17(12), 1974.

Digital Library

[2]

A. Agarwal and P. Kumar. Economical Duplication Based Task Scheduling for Heterogeneous and Homogeneous Computing Systems. IACC 2009, 2009.

[3]

C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier. StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. Concurr. Comput. : Pract. Exper., 23(2), 2011.

Digital Library

[4]

E. Ayguadé, R. Badia, P. Bellens, D. Cabrera, A. Duran, R. Ferrer, M. Gonzàlez, F. Igual, D. Jiménez-González, J. Labarta, L. Martinell, X. Martorell, R. Mayo, J. Pérez, J. Planas, and E. Quintana-Ortí. Extending OpenMP to Survive the Heterogeneous Multi-Core Era. International Journal of Parallel Programming, 38(5--6), 2010.

[5]

S. Bansal, P. Kumar, and K. Singh. An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems. Parallel and Distributed Systems, IEEE Transactions on, 14(6), 2003.

Digital Library

[6]

Barcelona Supercomputing Center. Barcelona Application Repository. Available online on April 18th, 2014.

[7]

P. Bellens, K. Palaniappan, R. Badia, G. Seetharaman, and J. Labarta. Parallel Implementation of the Integral Histogram. In Advanced Concepts for Intelligent Vision Systems, volume 6915 of Lecture Notes in Computer Science. 2011.

Digital Library

[8]

A. Buttari, J. Langou, J. Kurzak, and J. Dongarra. Parallel tiled QR factorization for multicore architectures. Technical report, 2007.

[9]

M. Daoud and N. Kharma. Efficient Compile-Time Task Scheduling for Heterogeneous Distributed Computing Systems. ICPADS 2006, 2006.

Digital Library

[10]

A. Duran, E. Ayguadé, R. M. Badia, J. Labarta, L. Martinell, X. Martorell, and J. Planas. Ompss: a Proposal for Programming Heterogeneous Multi-Core Architectures. Parallel Processing Letters, 21, 2011.

[11]

A. Duran, J. Corbalán, and E. Ayguadé. Evaluation of OpenMP Task Scheduling Strategies. IWOMP'08, 2008.

Digital Library

[12]

A. Duran, J. M. Perez, E. Ayguadé, R. M. Badia, and J. Labarta. Extending the OpenMP Tasking Model to Allow Dependent Tasks. IWOMP'08, 2008.

Digital Library

[13]

A. Fedorova, J. C. Saez, D. Shelepov, and M. Prieto. Communications of the ACM, (12).

[14]

P. Greenhalgh. big.LITTLE Processing with ARM Cortex-A15 & Cortex-A7. ARM White Paper, 2011.

[15]

M. Hakem and F. Butelle. Dynamic Critical Path Scheduling Parallel Programs onto Multiprocessors. IPDPS'05, 2005.

Digital Library

[16]

Intel Corporation. Reference Manual for Intel Math Kernel Library 11.1 .

[17]

M. A. Iverson, F. Özgüner, and G. J. Follen. Parallelizing Existing Applications in a Distributed Heterogeneous Environment. HCW'95, 1995.

[18]

P. Kogge, K. Bergman, S. Borkar, D. Campbell, W. Carson, W. Dally, M. Denneau, P. Franzon, W. Harrod, K. Hill, and Others. Exascale Computing Study: Technology Challenges in Achieving Exascale Systems. Technical report, University of Notre Dame, CSE Dept., 2008.

[19]

K. Li, Z. Zhang, Y. Xu, B. Gao, and L. He. Chemical Reaction Optimization for Heterogeneous Computing Environments. ISPA, 2012.

Digital Library

[20]

C.-H. Liu, C.-F. Li, K.-C. Lai, and C.-C. Wu. A dynamic Critical Path Duplication Task Scheduling Algorithm for Distributed Heterogeneous Computing Systems. volume 1 of ICPADS 2006, 2006.

Digital Library

[21]

A. Page and T. Naughton. Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing. In Parallel and Distributed Processing Symposium, 2005. Proceedings. 19th IEEE International, 2005.

Digital Library

[22]

J. A. Pienaar, S. Chakradhar, and A. Raghunathan. Automatic Generation of Software Pipelines for Heterogeneous Parallel Systems. SC '12, 2012.

Digital Library

[23]

J. Planas, R. Badia, E. Ayguade, and J. Labarta. Self-Adaptive OmpSs Tasks in Heterogeneous Environments. IPDPS, 2013.

Digital Library

[24]

A. Rico, F. Cabarcas, C. Villavieja, M. Pavlovic, A. Vega, Y. Etsion, A. Ramirez, and M. Valero. On the Simulation of Large-Scale Architectures Using Multiple Application Abstraction Levels. ACM Trans. Archit. Code Optim., 8(4).

Digital Library

[25]

H. Topcuoglu, S. Hariri, and M.-Y. Wu. Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 2002.

Digital Library

[26]

M.-Y. Wu and D. Gajski. Hypertool: a Programming Aid for Message-Passing Systems. Parallel and Distributed Systems, IEEE Transactions on, 1(3), 1990.

Digital Library

[27]

T. Yang and A. Gerasoulis. DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors. Parallel and Distributed Systems, IEEE Transactions on, 5(9), 1994.

Digital Library

[28]

H. Yu. A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments. SCIS'07, 2007.

[29]

Z. Zong, A. Manzanares, X. Ruan, and X. Qin. EAD and PEBD: Two Energy-Aware Duplication Scheduling Algorithms for Parallel Tasks on Homogeneous Clusters. Computers, IEEE Transactions on, 60(3), 2011.

Digital Library

Cited By

Tayeb HBramas BFaverge MGuermouche A(2024)Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous Computing Systems2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00014(31-40)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00014
Allaqband SNazish MAllaqband SBashir JBanday M(2024)An efficient machine learning based CPU scheduler for heterogeneous multicore processorsInternational Journal of Information Technology10.1007/s41870-024-01936-5Online publication date: 24-May-2024
https://doi.org/10.1007/s41870-024-01936-5
Nasiri HNasehi SDivband AGoudarzi M(2023)A scheduling algorithm to maximize storm throughput in heterogeneous clusterJournal of Big Data10.1186/s40537-023-00771-y10:1Online publication date: 17-Jun-2023
https://doi.org/10.1186/s40537-023-00771-y
Show More Cited By

Index Terms

Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Concurrent programming languages
        Distributed programming languages
        Parallel programming languages

Recommendations

Architecture-aware configuration and scheduling of matrix multiplication on asymmetric multicore processors

Asymmetric multicore processors have recently emerged as an appealing technology for severely energy-constrained environments, especially in mobile appliances where heterogeneity in applications is mainstream. In addition, given the growing interest for ...
StarPU: a unified platform for task scheduling on heterogeneous multicore architectures
Euro-Par 2009

In the field of HPC, the current hardware trend is to design multiprocessor architectures featuring heterogeneous technologies such as specialized coprocessors (e.g. Cell/BE) or data-parallel accelerators (e.g. GPUs). Approaching the theoretical ...
Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures

Performance of high-end supercomputers will reach the exascale through the advent of core counts in billions. However, in the upcoming exascale computing era it is important not only to focus on the performance, but also on scalability of fine-grained ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICS '15: Proceedings of the 29th ACM on International Conference on Supercomputing

June 2015

446 pages

ISBN:9781450335591

DOI:10.1145/2751205

General Chair:
Laxmi N. Bhuyan
University of California, Riverside
,
Program Chairs:
Fred Chong
University of California, Santa Barbara
,
Vivek Sarkar
Rice University

Copyright © 2015 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 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]

Sponsors

SIGARCH: ACM Special Interest Group on Computer Architecture

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

ICS'15

Sponsor:

SIGARCH

ICS'15: 2015 International Conference on Supercomputing

June 8 - 11, 2015

California, Newport Beach, USA

Acceptance Rates

ICS '15 Paper Acceptance Rate 40 of 160 submissions, 25%;

Overall Acceptance Rate 629 of 2,180 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

41
Total Citations
View Citations
692
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)8

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tayeb HBramas BFaverge MGuermouche A(2024)Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous Computing Systems2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00014(31-40)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00014
Allaqband SNazish MAllaqband SBashir JBanday M(2024)An efficient machine learning based CPU scheduler for heterogeneous multicore processorsInternational Journal of Information Technology10.1007/s41870-024-01936-5Online publication date: 24-May-2024
https://doi.org/10.1007/s41870-024-01936-5
Nasiri HNasehi SDivband AGoudarzi M(2023)A scheduling algorithm to maximize storm throughput in heterogeneous clusterJournal of Big Data10.1186/s40537-023-00771-y10:1Online publication date: 17-Jun-2023
https://doi.org/10.1186/s40537-023-00771-y
Basaklar TGoksoy AKrishnakumar AGumussoy SOgras U(2023)DTRL: Decision Tree-based Multi-Objective Reinforcement Learning for Runtime Task Scheduling in Domain-Specific System-on-ChipsACM Transactions on Embedded Computing Systems10.1145/360910822:5s(1-22)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1145/3609108
Yao YAmmar NShi W(2023)FLOW: A Scalable Multi-Model Federated Learning Framework on the Wheels2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)10.1109/MOST57249.2023.00010(11-22)Online publication date: May-2023
https://doi.org/10.1109/MOST57249.2023.00010
Lu TPeng L(2023)SCU: A Hardware Accelerator for Smart Contract Execution2023 IEEE International Conference on Blockchain (Blockchain)10.1109/Blockchain60715.2023.00061(356-364)Online publication date: 17-Dec-2023
https://doi.org/10.1109/Blockchain60715.2023.00061
Adnan (2023)Performance evaluation on work-stealing featured parallel programs on asymmetric performance multicore processorsArray10.1016/j.array.2023.10031119(100311)Online publication date: Sep-2023
https://doi.org/10.1016/j.array.2023.100311
Bilbao CSaez JPrieto-Matias M(2023)Rapid Development of OS Support with PMCSched for Scheduling on Asymmetric Multicore SystemsEuro-Par 2022: Parallel Processing Workshops10.1007/978-3-031-31209-0_14(184-196)Online publication date: 2-May-2023
https://doi.org/10.1007/978-3-031-31209-0_14
Bilbao CSaez JPrieto‐Matias M(2023)Flexible system software scheduling for asymmetric multicore systems with PMCSched: A case for Intel Alder LakeConcurrency and Computation: Practice and Experience10.1002/cpe.781435:25Online publication date: 6-Jun-2023
https://doi.org/10.1002/cpe.7814
Chen JManivannan MAbduljabbar MPericàs M(2022)ERASE: Energy Efficient Task Mapping and Resource Management for Work Stealing RuntimesACM Transactions on Architecture and Code Optimization10.1145/351042219:2(1-29)Online publication date: 7-Mar-2022
https://dl.acm.org/doi/10.1145/3510422
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