Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems

Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems

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 challeng...

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Bibliographic Details
Main Authors: Donghyeon Kim, Seokwon Kang, Junsu Lim, Sunwook Jung, Woosung Kim, Yongjun Park
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Electronics
Subjects:
device abstraction
dynamic resource management
GPGPUs
heterogeneous system architecture
multitasking
OpenCL
Online Access:https://www.mdpi.com/2079-9292/9/11/1825
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spelling doaj-f5cdc95c70d2496bbae7e8282a05d7312020-11-25T04:04:21ZengMDPI AGElectronics2079-92922020-11-0191825182510.3390/electronics9111825Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous SystemsDonghyeon Kim0Seokwon Kang1Junsu Lim2Sunwook Jung3Woosung Kim4Yongjun Park5Department of Computer Science, Hanyang University, Seoul 04763, KoreaDepartment of Computer Science, Hanyang University, Seoul 04763, KoreaDepartment of Computer Science, Hanyang University, Seoul 04763, KoreaDepartment of Computer Science, Hanyang University, Seoul 04763, KoreaLG Electronics CTO Software Platform Laboratory, Seoul 06772, KoreaDepartment of Computer Science, Hanyang University, Seoul 04763, KoreaAs 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 1.85× over the round-robin policy in the non-shared-memory system with small data set.https://www.mdpi.com/2079-9292/9/11/1825device abstractiondynamic resource managementGPGPUsheterogeneous system architecturemultitaskingOpenCL
collection DOAJ
language English
format Article
sources DOAJ
author Donghyeon Kim
Seokwon Kang
Junsu Lim
Sunwook Jung
Woosung Kim
Yongjun Park
spellingShingle Donghyeon Kim
Seokwon Kang
Junsu Lim
Sunwook Jung
Woosung Kim
Yongjun Park
Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
Electronics
device abstraction
dynamic resource management
GPGPUs
heterogeneous system architecture
multitasking
OpenCL
author_facet Donghyeon Kim
Seokwon Kang
Junsu Lim
Sunwook Jung
Woosung Kim
Yongjun Park
author_sort Donghyeon Kim
title Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
title_short Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
title_full Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
title_fullStr Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
title_full_unstemmed Resource-Aware Device Allocation of Data-Parallel Applications on Heterogeneous Systems
title_sort resource-aware device allocation of data-parallel applications on heterogeneous systems
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2020-11-01
description 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 1.85× over the round-robin policy in the non-shared-memory system with small data set.
topic device abstraction
dynamic resource management
GPGPUs
heterogeneous system architecture
multitasking
OpenCL
url https://www.mdpi.com/2079-9292/9/11/1825
work_keys_str_mv AT donghyeonkim resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
AT seokwonkang resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
AT junsulim resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
AT sunwookjung resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
AT woosungkim resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
AT yongjunpark resourceawaredeviceallocationofdataparallelapplicationsonheterogeneoussystems
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