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Data Provenance for Experiment Management of Scientific Applications on GPU

Authors
Kim, SejinOh, JisunKim, Yoonhee
Issue Date
Sep-2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
data provenance; GPU; scientific workflow
Citation
2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019
Journal Title
2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2858
DOI
10.23919/APNOMS.2019.8892997
ISSN
2576-8565
Abstract
Graphics Processing Units (GPUs) are getting popularly utilized for multi-purpose applications in order to enhance highly performed parallelism of computation. As memory virtualization methods in GPU nodes are not efficiently provided to deal with diverse memory usage patterns for these applications, the success of their execution depends on exclusive and limited use of physical memory in GPU environments. Therefore, it is important to predict a pattern change of GPU memory usage during runtime execution of an application. Data provenance extracted from application characteristics, GPU runtime environments, input, and execution patterns from runtime monitoring, is defined for supporting application management to set runtime configuration and predict an experimental result, and utilize resource with co-located applications. In this paper, we define data provenance of an application on GPUs and manage data by profiling the execution of CUDA scientific applications. Data provenance management helps to predict execution patterns of other similar experiments and plan efficient resource configuration. © 2019 IEICE.
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공과대학 (소프트웨어학부(첨단))
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