Abstract
This paper proposes a deep Q network (DQN)-based method for the workload partition problem in OpenCL. The DQN, a reinforcement learning algorithm, optimizes the workload partition for each processing unit by the self-training, based on the accumulated performance data on the computing environment. Our experiments reveal that the DQN-based partition provides the performance improvement by up to 62.2% and 6.9% in JPEG decoding, compared to the LuxMark-based and target-based partitions, respectively. The DQN is able to capture the low-level contention in slave devices such as caches and memory, and the communication bottleneck between devices, and reflect it to the workload partition ratio.
| Original language | English |
|---|---|
| Journal | Journal of Supercomputing |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Keywords
- DQN
- OpenCL
- Workload partition
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture
Cite this
DQN-based OpenCL workload partition for performance optimization. / Park, Sanghyun; Suh, Taeweon.
In: Journal of Supercomputing, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - DQN-based OpenCL workload partition for performance optimization
AU - Park, Sanghyun
AU - Suh, Taeweon
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper proposes a deep Q network (DQN)-based method for the workload partition problem in OpenCL. The DQN, a reinforcement learning algorithm, optimizes the workload partition for each processing unit by the self-training, based on the accumulated performance data on the computing environment. Our experiments reveal that the DQN-based partition provides the performance improvement by up to 62.2% and 6.9% in JPEG decoding, compared to the LuxMark-based and target-based partitions, respectively. The DQN is able to capture the low-level contention in slave devices such as caches and memory, and the communication bottleneck between devices, and reflect it to the workload partition ratio.
AB - This paper proposes a deep Q network (DQN)-based method for the workload partition problem in OpenCL. The DQN, a reinforcement learning algorithm, optimizes the workload partition for each processing unit by the self-training, based on the accumulated performance data on the computing environment. Our experiments reveal that the DQN-based partition provides the performance improvement by up to 62.2% and 6.9% in JPEG decoding, compared to the LuxMark-based and target-based partitions, respectively. The DQN is able to capture the low-level contention in slave devices such as caches and memory, and the communication bottleneck between devices, and reflect it to the workload partition ratio.
KW - DQN
KW - OpenCL
KW - Workload partition
UR - http://www.scopus.com/inward/record.url?scp=85061327493&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061327493&partnerID=8YFLogxK
U2 - 10.1007/s11227-019-02766-0
DO - 10.1007/s11227-019-02766-0
M3 - Article
AN - SCOPUS:85061327493
JO - The Journal of Supercomputing
JF - The Journal of Supercomputing
SN - 0920-8542
ER -