abstract

System-level power-performance efficiency modeling for emergent GPU architectures

Authors:

Shuaiwen Song,

Kirk CameronAuthors Info & Claims

PACT '12: Proceedings of the 21st international conference on Parallel architectures and compilation techniques

Pages 473 - 474

https://doi.org/10.1145/2370816.2370903

Published: 19 September 2012 Publication History

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Abstract

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Dharwadkar RBalakrishnan M(2024)SPEEDY: SystemC-Based Design Space Exploration Framework for Embedded SystemsVLSI for Embedded Intelligence10.1007/978-981-97-3756-7_10(117-130)Online publication date: 28-Oct-2024
https://doi.org/10.1007/978-981-97-3756-7_10
Ci QLi HYang SGao J(2020) Adaptive and transparent task scheduling of GPU ‐powered clusters Concurrency and Computation: Practice and Experience10.1002/cpe.579334:9Online publication date: 24-Apr-2020
https://doi.org/10.1002/cpe.5793
Huang YGuo BShen Y(2019)GPU Energy Consumption Optimization With a Global-Based Neural Network MethodIEEE Access10.1109/ACCESS.2019.29153807(64303-64314)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2915380
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Index Terms

System-level power-performance efficiency modeling for emergent GPU architectures
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Information

Published In

PACT '12: Proceedings of the 21st international conference on Parallel architectures and compilation techniques

September 2012

512 pages

ISBN:9781450311823

DOI:10.1145/2370816

General Chairs:
Pen-Chung Yew
University of Minnesota
,
Sangyeun Cho
University of Pittsburgh
,
Program Chairs:
Luiz DeRose
Cray, Inc.
,
David J. Lilja
University of Minnesota

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 September 2012

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PACT '12

Sponsor:

IFIP WG 10.3
SIGARCH
IEEE CS TCPP
IEEE CS TCAA

PACT '12: International Conference on Parallel Architectures and Compilation Techniques

September 19 - 23, 2012

Minnesota, Minneapolis, USA

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Overall Acceptance Rate 121 of 471 submissions, 26%

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Dharwadkar RBalakrishnan M(2024)SPEEDY: SystemC-Based Design Space Exploration Framework for Embedded SystemsVLSI for Embedded Intelligence10.1007/978-981-97-3756-7_10(117-130)Online publication date: 28-Oct-2024
https://doi.org/10.1007/978-981-97-3756-7_10
Ci QLi HYang SGao J(2020) Adaptive and transparent task scheduling of GPU ‐powered clusters Concurrency and Computation: Practice and Experience10.1002/cpe.579334:9Online publication date: 24-Apr-2020
https://doi.org/10.1002/cpe.5793
Huang YGuo BShen Y(2019)GPU Energy Consumption Optimization With a Global-Based Neural Network MethodIEEE Access10.1109/ACCESS.2019.29153807(64303-64314)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2915380
Xie XTan WFong LLiang YHuang HWeissman JIamnitchi AIosup A(2017)CuMF_SGDProceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3078597.3078602(79-92)Online publication date: 26-Jun-2017
https://dl.acm.org/doi/10.1145/3078597.3078602
Spafford KVetter J(2015)Automated design space exploration with AspenScientific Programming10.1155/2015/1573052015(7-7)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/157305
Sengupta DSong SAgarwal KSchwan KKern JVetter J(2015)GraphReduceProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/2807591.2807655(1-12)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1145/2807591.2807655
Li BSong SBezakova ICameron K(2013)EDR: An energy-aware runtime load distribution system for data-intensive applications in the cloud2013 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2013.6702674(1-8)Online publication date: Sep-2013
https://doi.org/10.1109/CLUSTER.2013.6702674
Li HFox Gvon Laszewski GChauhan A(2013)Co-processing SPMD computation on CPUs and GPUs cluster2013 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2013.6702632(1-10)Online publication date: Sep-2013
https://doi.org/10.1109/CLUSTER.2013.6702632

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An integrated GPU power and performance model

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Accelerating the discontinuous Galerkin method for seismic wave propagation simulations using the graphic processing unit (GPU)-single-GPU implementation

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An integrated GPU power and performance model

An integrated GPU power and performance model

Accelerating the discontinuous Galerkin method for seismic wave propagation simulations using the graphic processing unit (GPU)-single-GPU implementation

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