A survey of CPU-GPU heterogeneous computing techniques
Abstract
As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and application level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). Furthermore, we believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Georgia Inst. of Technology, Atlanta, GA (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1265534
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- ACM Computing Surveys
- Additional Journal Information:
- Journal Volume: 47; Journal Issue: 4; Journal ID: ISSN 0360-0300
- Publisher:
- Association for Computing Machinery (ACM)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; experimentation; management; measurement; performance; analysis; CPU-GPU heterogeneous/hybrid/collaborative computing; workload division/partitioning; dynamic/static load-balancing; pipelining; programming frameworks; fused CPU-GPU chip
Citation Formats
Mittal, Sparsh, and Vetter, Jeffrey S. A survey of CPU-GPU heterogeneous computing techniques. United States: N. p., 2015.
Web. doi:10.1145/2788396.
Mittal, Sparsh, & Vetter, Jeffrey S. A survey of CPU-GPU heterogeneous computing techniques. United States. https://doi.org/10.1145/2788396
Mittal, Sparsh, and Vetter, Jeffrey S. Sat .
"A survey of CPU-GPU heterogeneous computing techniques". United States. https://doi.org/10.1145/2788396. https://www.osti.gov/servlets/purl/1265534.
@article{osti_1265534,
title = {A survey of CPU-GPU heterogeneous computing techniques},
author = {Mittal, Sparsh and Vetter, Jeffrey S.},
abstractNote = {As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and application level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). Furthermore, we believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.},
doi = {10.1145/2788396},
journal = {ACM Computing Surveys},
number = 4,
volume = 47,
place = {United States},
year = {Sat Jul 04 00:00:00 EDT 2015},
month = {Sat Jul 04 00:00:00 EDT 2015}
}
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Evaluating application performance and energy consumption on hybrid CPU+GPU architecture
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journal, March 2012
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conference, January 2011
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conference, June 2010
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conference, December 2013
- Korwar, Sai Kiran; Vadhiyar, Sathish; Nanjundiah, Ravi S.
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journal, April 2011
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A hybrid shared memory heterogeneous execution platform for PCIe-based GPGPUs
conference, December 2013
- Shukla, Sambit K.; Bhuyan, Laxmi N.
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Optimization Techniques for 3D-FWT on Systems with Manycore GPUs and Multicore CPUs
journal, January 2013
- Bernabé, Gregorio; Cuenca, Javier; Giménez, Domingo
- Procedia Computer Science, Vol. 18
OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems
journal, May 2010
- Stone, John E.; Gohara, David; Shi, Guochun
- Computing in Science & Engineering, Vol. 12, Issue 3, p. 66-73
A 22nm IA multi-CPU and GPU System-on-Chip
conference, February 2012
- Damaraju, Satish; George, Varghese; Jahagirdar, Sanjeev
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Characterizing and evaluating a key-value store application on heterogeneous CPU-GPU systems
conference, April 2012
- Hetherington, Tayler H.; Rogers, Timothy G.; Hsu, Lisa
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Scaling Hierarchical N-body Simulations on GPU Clusters
conference, November 2010
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conference, January 2013
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journal, October 2011
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conference, December 2010
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A Fast Parallel Implementation of Molecular Dynamics with the Morse Potential on a Heterogeneous Petascale Supercomputer
conference, May 2012
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X-device query processing by bitwise distribution
conference, January 2012
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journal, April 2012
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conference, May 2012
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conference, October 2009
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journal, May 2012
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conference, January 2012
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journal, September 2011
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book, January 2012
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journal, January 2012
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- Benner, Peter; Ezzatti, Pablo; Quintana-Ortí, Enrique S.
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journal, March 2013
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book, January 2012
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journal, April 2013
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conference, October 2009
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