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NUPAR: A Benchmark Suite for Modern GPU Architectures

Authors:

Fanny Nina Paravecino,

Zhongliang Chen,

Perhaad Mistry,

David KaeliAuthors Info & Claims

ICPE '15: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

Pages 253 - 264

https://doi.org/10.1145/2668930.2688046

Published: 31 January 2015 Publication History

Abstract

Heterogeneous systems consisting of multi-core CPUs, Graphics Processing Units (GPUs) and many-core accelerators have gained widespread use by application developers and data-center platform developers. Modern day heterogeneous systems have evolved to include advanced hardware and software features to support a spectrum of application patterns. Heterogeneous programming frameworks such as CUDA, OpenCL, and OpenACC have all introduced new interfaces to enable developers to utilize new features on these platforms. In emerging applications, performance optimization is not only limited to effectively exploiting data-level parallelism, but includes leveraging new degrees of concurrency and parallelism to accelerate the entire application.

To aid hardware architects and application developers in effectively tuning performance on GPUs, we have developed the NUPAR benchmark suite. The NUPAR applications belong to a number of different scientific and commercial computing domains. These benchmarks exhibit a range of GPU computing characteristics that consider memory-bandwidth limitations, device occupancy and resource utilization, synchronization latency and device-specific compute optimizations. The NUPAR applications are specifically designed to stress new hardware and software features that include: nested parallelism, concurrent kernel execution, shared host-device memory and new instructions for precise computation and data movement. In this paper, we focus our discussion on applications developed in CUDA and OpenCL, and focus on high-end server class GPUs. We describe these benchmarks and evaluate their interaction with different architectural features on a GPU. Our evaluation examines the behavior of the advanced hardware features on recently-released GPU architectures.

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Cited By

Friesel BLütke Dreimann MSpinczyk O(2023)A Full-System Perspective on UPMEM PerformanceProceedings of the 1st Workshop on Disruptive Memory Systems10.1145/3609308.3625266(1-7)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3609308.3625266
Yan KShi YYan YChen QHuang ZSi M(2023)Exploring OpenMP GPU Offloading for Implementing Convolutional Neural NetworksProceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3582514.3582523(60-69)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3582514.3582523
Swatman SVarbanescu APimentel ASalzburger AKrasznahorkay AVieira MCardellini VDi Marco ATuma P(2023)Systematically Exploring High-Performance Representations of Vector Fields Through Compile-Time CompositionProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583723(55-66)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578244.3583723
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Index Terms

NUPAR: A Benchmark Suite for Modern GPU Architectures
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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cover image ACM Conferences

ICPE '15: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

January 2015

366 pages

ISBN:9781450332484

DOI:10.1145/2668930

General Chairs:
Lizy K. John
UT Austin, USA
,
Connie U. Smith
L&S Computer Technology, Inc., USA
,
Program Chairs:
Kai Sachs
SAP SE, Germany
,
Catalina M. Lladó
University of the Balearic Islands, Spain

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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SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGSOFT: ACM Special Interest Group on Software Engineering
SPEC: SPEC Research Group

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Published: 31 January 2015

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ICPE'15

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ICPE'15: ACM/SPEC International Conference on Performance Engineering

January 28 - February 4, 2015

Texas, Austin, USA

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ICPE '15 Paper Acceptance Rate 23 of 74 submissions, 31%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Friesel BLütke Dreimann MSpinczyk O(2023)A Full-System Perspective on UPMEM PerformanceProceedings of the 1st Workshop on Disruptive Memory Systems10.1145/3609308.3625266(1-7)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3609308.3625266
Yan KShi YYan YChen QHuang ZSi M(2023)Exploring OpenMP GPU Offloading for Implementing Convolutional Neural NetworksProceedings of the 14th International Workshop on Programming Models and Applications for Multicores and Manycores10.1145/3582514.3582523(60-69)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3582514.3582523
Swatman SVarbanescu APimentel ASalzburger AKrasznahorkay AVieira MCardellini VDi Marco ATuma P(2023)Systematically Exploring High-Performance Representations of Vector Fields Through Compile-Time CompositionProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583723(55-66)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578244.3583723
dos Santos FMalde SCazzaniga CFrost CCarro LRech P(2022)Experimental Findings on the Sources of Detected Unrecoverable Errors in GPUsIEEE Transactions on Nuclear Science10.1109/TNS.2022.314134169:3(436-443)Online publication date: Mar-2022
https://doi.org/10.1109/TNS.2022.3141341
Olabi MLuna JMutlu OHwu WEl Hajj ILee J(2022)A compiler framework for optimizing dynamic parallelism on GPUsProceedings of the 20th IEEE/ACM International Symposium on Code Generation and Optimization10.1109/CGO53902.2022.9741284(1-13)Online publication date: 2-Apr-2022
https://dl.acm.org/doi/10.1109/CGO53902.2022.9741284
Svedin MChien SChikafa GJansson NPodobas A(2021)Benchmarking the Nvidia GPU LineageProceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies10.1145/3468044.3468053(1-6)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3468044.3468053
Baruah TShivdikar KDong SSun YMojumder SJung KAbellan JUkidave YJoshi AKim JKaeli D(2021)GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)10.1109/ISPASS51385.2021.00013(13-23)Online publication date: Mar-2021
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Kalra CPrevilon FRubin NKaeli D(2020)ArmorAllACM Transactions on Architecture and Code Optimization10.1145/338213217:2(1-24)Online publication date: 29-May-2020
https://dl.acm.org/doi/10.1145/3382132
Kalra CPrevilon FLi XRubin NKaeli D(2018)PRISMProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291748(1-14)Online publication date: 11-Nov-2018
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Kalra CPrevilon FLi XRubin NKaeli D(2018)PRISMProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00072(1-14)Online publication date: 11-Nov-2018
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