Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systemsopen access
- Authors
- Kim, Donghyeon; Kang, Seokwon; Lim, Junsu; Jung, Sunwook; Kim, Woosung; Park, Yongjun
- Issue Date
- Nov-2020
- Publisher
- MDPI
- Keywords
- device abstraction; dynamic resource management; GPGPUs; heterogeneous system architecture; multitasking; OpenCL
- Citation
- ELECTRONICS, v.9, no.11, pp.1 - 18
- Indexed
- SCIE
SCOPUS
- Journal Title
- ELECTRONICS
- Volume
- 9
- Number
- 11
- Start Page
- 1
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/144414
- DOI
- 10.3390/electronics9111825
- ISSN
- 2079-9292
- Abstract
- As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchestration of multiple applications is highly challenging because a detailed runtime status such as expected remaining time and available memory size of each computing device is hidden. To solve these problems, we propose a dynamic data-parallel application allocation framework called ADAMS. Evaluations show that our framework improves the average total execution device time by 1.85x over the round-robin policy in the non-shared-memory system with small data set.
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