research-article

Improving GPGPU concurrency with elastic kernels

Authors:

Matthew J. Thazhuthaveetil,

R. GovindarajanAuthors Info & Claims

ASPLOS '13: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems

Pages 407 - 418

https://doi.org/10.1145/2451116.2451160

Published: 16 March 2013 Publication History

Abstract

Each new generation of GPUs vastly increases the resources available to GPGPU programs. GPU programming models (like CUDA) were designed to scale to use these resources. However, we find that CUDA programs actually do not scale to utilize all available resources, with over 30% of resources going unused on average for programs of the Parboil2 suite that we used in our work. Current GPUs therefore allow concurrent execution of kernels to improve utilization. In this work, we study concurrent execution of GPU kernels using multiprogram workloads on current NVIDIA Fermi GPUs. On two-program workloads from the Parboil2 benchmark suite we find concurrent execution is often no better than serialized execution. We identify that the lack of control over resource allocation to kernels is a major serialization bottleneck. We propose transformations that convert CUDA kernels into elastic kernels which permit fine-grained control over their resource usage. We then propose several elastic-kernel aware concurrency policies that offer significantly better performance and concurrency compared to the current CUDA policy. We evaluate our proposals on real hardware using multiprogrammed workloads constructed from benchmarks in the Parboil 2 suite. On average, our proposals increase system throughput (STP) by 1.21x and improve the average normalized turnaround time (ANTT) by 3.73x for two-program workloads when compared to the current CUDA concurrency implementation.

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Cited By

Alexopoulos GMitropoulos DRoychoudhury APaiva AAbreu RStorey M(2024)nvshare: Practical GPU Sharing without Memory Size ConstraintsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3640034(16-20)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3640034
Andriotis NSerrano-Cases AAlcaide SAbella JCazorla FPeng YBaldovin APaulitsch MTsymbal VHong JLanperne MPark JCerny TShahriar H(2023)A Software-Only Approach to Enable Diverse Redundancy on Intel GPUs for Safety-Related KernelsProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577610(451-460)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577610
Chen HLin EChou YChou J(2023)Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst EstimationIEEE Transactions on Cloud Computing10.1109/TCC.2021.311920511:1(854-867)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TCC.2021.3119205
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Index Terms

Improving GPGPU concurrency with elastic kernels
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Multiple instruction, multiple data
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features

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cover image ACM Conferences

ASPLOS '13: Proceedings of the eighteenth international conference on Architectural support for programming languages and operating systems

March 2013

574 pages

ISBN:9781450318709

DOI:10.1145/2451116

General Chair:
Vivek Sarkar
Rice University, USA
,
Program Chair:
Rastislav Bodik
University of California, Berkeley, USA

ACM SIGPLAN Notices Volume 48, Issue 4
ASPLOS '13
April 2013
540 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2499368
Issue’s Table of Contents
ACM SIGARCH Computer Architecture News Volume 41, Issue 1
ASPLOS '13
March 2013
540 pages
ISSN:0163-5964
DOI:10.1145/2490301
Issue’s Table of Contents

Copyright © 2013 ACM.

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Published: 16 March 2013

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ASPLOS '13: Architectural Support for Programming Languages and Operating Systems

March 16 - 20, 2013

Texas, Houston, USA

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Cited By

Alexopoulos GMitropoulos DRoychoudhury APaiva AAbreu RStorey M(2024)nvshare: Practical GPU Sharing without Memory Size ConstraintsProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3640034(16-20)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3640034
Andriotis NSerrano-Cases AAlcaide SAbella JCazorla FPeng YBaldovin APaulitsch MTsymbal VHong JLanperne MPark JCerny TShahriar H(2023)A Software-Only Approach to Enable Diverse Redundancy on Intel GPUs for Safety-Related KernelsProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing10.1145/3555776.3577610(451-460)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3555776.3577610
Chen HLin EChou YChou J(2023)Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst EstimationIEEE Transactions on Cloud Computing10.1109/TCC.2021.311920511:1(854-867)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TCC.2021.3119205
Zhao HCui WChen QGuo M(2023)ISPA: Exploiting Intra-SM Parallelism in GPUs via Fine-Grained Resource ManagementIEEE Transactions on Computers10.1109/TC.2022.321408872:5(1473-1487)Online publication date: 1-May-2023
https://doi.org/10.1109/TC.2022.3214088
Lin ZMo ZHuang XZhang XLu Y(2023)KeSCo: Compiler-based Kernel Scheduling for Multi-task GPU Applications2023 IEEE 41st International Conference on Computer Design (ICCD)10.1109/ICCD58817.2023.00046(247-254)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICCD58817.2023.00046
Saroliya UArima ELiu DSchulz M(2023)Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach2023 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER52292.2023.00023(185-196)Online publication date: 31-Oct-2023
https://doi.org/10.1109/CLUSTER52292.2023.00023
Guo KXu YQi ZGuan H(2023) OptimumJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2023.102901141:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.sysarc.2023.102901
Masola ACapodieci NCavicchioli ROlmedo IRouxel B(2023)Memory-Aware Latency Prediction Model for Concurrent Kernels in Partitionable GPUs: Simulations and ExperimentsJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_3(46-73)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_3
Zhang XTang ZZhang XLi K(2022)Co-Concurrency Mechanism for Multi-GPUs in Distributed Heterogeneous EnvironmentsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.320808233:12(4935-4947)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TPDS.2022.3208082
Zhao CGao WNie FZhou H(2022)A Survey of GPU Multitasking Methods Supported by Hardware ArchitectureIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.311563033:6(1451-1463)Online publication date: 1-Jun-2022
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