IEICE TRANSACTIONS on Information
GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems
Summary :
This paper presents a stream programming framework, named GPU-chariot, for accelerating stream applications running on graphics processing units (GPUs). The main contribution of our framework is that it realizes efficient software pipelines on multi-GPU systems by enabling out-of-order execution of CPU functions, kernels, and data transfers. To achieve this out-of-order execution, we apply a runtime scheduler that not only maximizes the utilization of system resources but also encapsulates the number of GPUs available in the system. In addition, we implement a load-balancing capability to flow data efficiently through multiple GPUs. Furthermore, a callback interface enables overlapping execution of functions in third-party libraries. By using kernels with different performance bottlenecks, we show that our out-of-order execution is up to 20% faster than in-order execution. Finally, we conduct several case studies on a 4-GPU system and demonstrate the advantages of GPU-chariot over a manually pipelined code. We conclude that GPU-chariot can be useful when developing stream applications with software pipelines on multiple GPUs and CPUs.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.12 pp.2604-2616
- Publication Date
- 2013/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.2604
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
- Category
Authors
Fumihiko INO
Osaka University
Shinta NAKAGAWA
NEC Corporation
Kenichi HAGIHARA
Osaka University
Keyword
Latest Issue
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Fumihiko INO, Shinta NAKAGAWA, Kenichi HAGIHARA, "GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 12, pp. 2604-2616, December 2013, doi: 10.1587/transinf.E96.D.2604.
Abstract: This paper presents a stream programming framework, named GPU-chariot, for accelerating stream applications running on graphics processing units (GPUs). The main contribution of our framework is that it realizes efficient software pipelines on multi-GPU systems by enabling out-of-order execution of CPU functions, kernels, and data transfers. To achieve this out-of-order execution, we apply a runtime scheduler that not only maximizes the utilization of system resources but also encapsulates the number of GPUs available in the system. In addition, we implement a load-balancing capability to flow data efficiently through multiple GPUs. Furthermore, a callback interface enables overlapping execution of functions in third-party libraries. By using kernels with different performance bottlenecks, we show that our out-of-order execution is up to 20% faster than in-order execution. Finally, we conduct several case studies on a 4-GPU system and demonstrate the advantages of GPU-chariot over a manually pipelined code. We conclude that GPU-chariot can be useful when developing stream applications with software pipelines on multiple GPUs and CPUs.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.2604/_p
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@ARTICLE{e96-d_12_2604,
author={Fumihiko INO, Shinta NAKAGAWA, Kenichi HAGIHARA, },
journal={IEICE TRANSACTIONS on Information},
title={GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems},
year={2013},
volume={E96-D},
number={12},
pages={2604-2616},
abstract={This paper presents a stream programming framework, named GPU-chariot, for accelerating stream applications running on graphics processing units (GPUs). The main contribution of our framework is that it realizes efficient software pipelines on multi-GPU systems by enabling out-of-order execution of CPU functions, kernels, and data transfers. To achieve this out-of-order execution, we apply a runtime scheduler that not only maximizes the utilization of system resources but also encapsulates the number of GPUs available in the system. In addition, we implement a load-balancing capability to flow data efficiently through multiple GPUs. Furthermore, a callback interface enables overlapping execution of functions in third-party libraries. By using kernels with different performance bottlenecks, we show that our out-of-order execution is up to 20% faster than in-order execution. Finally, we conduct several case studies on a 4-GPU system and demonstrate the advantages of GPU-chariot over a manually pipelined code. We conclude that GPU-chariot can be useful when developing stream applications with software pipelines on multiple GPUs and CPUs.},
keywords={},
doi={10.1587/transinf.E96.D.2604},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems
T2 - IEICE TRANSACTIONS on Information
SP - 2604
EP - 2616
AU - Fumihiko INO
AU - Shinta NAKAGAWA
AU - Kenichi HAGIHARA
PY - 2013
DO - 10.1587/transinf.E96.D.2604
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2013
AB - This paper presents a stream programming framework, named GPU-chariot, for accelerating stream applications running on graphics processing units (GPUs). The main contribution of our framework is that it realizes efficient software pipelines on multi-GPU systems by enabling out-of-order execution of CPU functions, kernels, and data transfers. To achieve this out-of-order execution, we apply a runtime scheduler that not only maximizes the utilization of system resources but also encapsulates the number of GPUs available in the system. In addition, we implement a load-balancing capability to flow data efficiently through multiple GPUs. Furthermore, a callback interface enables overlapping execution of functions in third-party libraries. By using kernels with different performance bottlenecks, we show that our out-of-order execution is up to 20% faster than in-order execution. Finally, we conduct several case studies on a 4-GPU system and demonstrate the advantages of GPU-chariot over a manually pipelined code. We conclude that GPU-chariot can be useful when developing stream applications with software pipelines on multiple GPUs and CPUs.
ER -
English
