research-article

Whippletree: task-based scheduling of dynamic workloads on the GPU

Authors:

Markus Steinberger,

Michael Kenzel,

Bernhard Kerbl,

Mark Dokter, and

Dieter SchmalstiegAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 33, Issue 6

Article No.: 228, Pages 1 - 11

https://doi.org/10.1145/2661229.2661250

Published: 19 November 2014 Publication History

Abstract

In this paper, we present Whippletree, a novel approach to scheduling dynamic, irregular workloads on the GPU. We introduce a new programming model which offers the simplicity and expressiveness of task-based parallelism while retaining all aspects of the multi-level execution hierarchy essential to unlocking the full potential of a modern GPU. At the same time, our programming model lends itself to efficient implementation on the SIMD-based architecture typical of a current GPU. We demonstrate the practical utility of our model by providing a reference implementation on top of current CUDA hardware. Furthermore, we show that our model compares favorably to traditional approaches in terms of both performance as well as the range of applications that can be covered. We demonstrate the benefits of our model for recursive Reyes rendering, procedural geometry generation and volume rendering with concurrent irradiance caching.

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Cited By

Turimbetov ISasongko MUnat D(2024)GPU-Initiated Resource Allocation for Irregular WorkloadsProceedings of the 3rd International Workshop on Extreme Heterogeneity Solutions10.1145/3642961.3643799(1-8)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3642961.3643799
Park SHong JSong JKim HKim YLee JLee IChabbi MSteuwer M(2024)AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read MappingProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638474(431-444)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638474
Ismayilov IBaydamirli JSağbili DWahib MUnat DGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Multi-GPU Communication Schemes for Iterative Solvers: When CPUs are Not in ChargeProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593713(192-202)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593713
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Index Terms

Whippletree: task-based scheduling of dynamic workloads on the GPU
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
  2. Parallel computing methodologies
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction techniques

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 33, Issue 6

November 2014

704 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2661229

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

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Publication History

Published: 19 November 2014

Published in TOG Volume 33, Issue 6

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Cited By

Turimbetov ISasongko MUnat D(2024)GPU-Initiated Resource Allocation for Irregular WorkloadsProceedings of the 3rd International Workshop on Extreme Heterogeneity Solutions10.1145/3642961.3643799(1-8)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3642961.3643799
Park SHong JSong JKim HKim YLee JLee IChabbi MSteuwer M(2024)AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read MappingProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638474(431-444)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638474
Ismayilov IBaydamirli JSağbili DWahib MUnat DGallivan KNikolopoulos DBeivide RGallopoulos E(2023)Multi-GPU Communication Schemes for Iterative Solvers: When CPUs are Not in ChargeProceedings of the 37th International Conference on Supercomputing10.1145/3577193.3593713(192-202)Online publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1145/3577193.3593713
Nourazar MBooth BGoossens B(2023)A GPU optimization workflow for real-time execution of ultra-high frame rate computer vision applicationsJournal of Real-Time Image Processing10.1007/s11554-023-01384-721:1Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1007/s11554-023-01384-7
Chen YBrock BPorumbescu SBuluc AYelick KOwens J(2022)Atos: A Task-Parallel GPU Scheduler for Graph AnalyticsProceedings of the 51st International Conference on Parallel Processing10.1145/3545008.3545056(1-11)Online publication date: 29-Aug-2022
https://dl.acm.org/doi/10.1145/3545008.3545056
Huang TLin DLin CLin Y(2022)Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing SystemIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.310425533:6(1303-1320)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TPDS.2021.3104255
Wang QRen BChen JEdwards R(2022)MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00022(135-145)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00022
Stavrinides GKaratza H(2021)Orchestrating Bag-of-Tasks Applications with Dynamically Spawned Tasks in a Distributed Environment2021 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS)10.23919/SPECTS52716.2021.9639275(1-8)Online publication date: 19-Jul-2021
https://doi.org/10.23919/SPECTS52716.2021.9639275
Mayr BWeinrauch AParger MSteinberger M(2021)Are van Emde Boas trees viable on the GPU?2021 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC49654.2021.9622837(1-7)Online publication date: 20-Sep-2021
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Oh CZheng ZShen XZhai JYi YSarkar VKim H(2020)GOPipeProceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques10.1145/3410463.3414656(43-54)Online publication date: 30-Sep-2020
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