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.

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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

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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]

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SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

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Published: 08 June 2015

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ICS'15

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SIGARCH

ICS'15: 2015 International Conference on Supercomputing

June 8 - 11, 2015

California, Newport Beach, USA

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

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

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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
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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
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