Runtime prediction of parallel applications with workload-aware clustering.

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Bibliographic Details
Title:	Runtime prediction of parallel applications with workload-aware clustering.
Authors:	Park, Ju-Won¹ juwon.park@kisti.re.kr, Kim, Eunhye² eunhye@etri.re.kr
Source:	Journal of Supercomputing. Nov2017, Vol. 73 Issue 11, p4635-4651. 17p.
Subjects:	Workload of computers, Supercomputers, Workflow, Machine learning, Factor analysis
Abstract:	Traditionally, many science fields require great support for a massive workflow, which utilizes multiple cores simultaneously. In order to support such large-scale scientific workflows, high-capacity parallel systems such as supercomputers are widely used. To increase the utilization of these systems, most schedulers use backfilling policy based on user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, an efficient machine learning approach is present to predict the runtime of parallel application. The proposed method is divided into three phases. First is to analyze important feature of the history log data by factor analysis. Second is to carry out clustering for the parallel program based on the important features. Third is to build a prediction models by pattern similarity of parallel program log data and estimate runtime. In the experiments, we use workload logs on parallel systems (i.e., NASA-iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing root-mean-square error with other techniques, experimental results show that the proposed method improves the accuracy up to 69.56%. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Traditionally, many science fields require great support for a massive workflow, which utilizes multiple cores simultaneously. In order to support such large-scale scientific workflows, high-capacity parallel systems such as supercomputers are widely used. To increase the utilization of these systems, most schedulers use backfilling policy based on user's estimated runtime. However, it is found to be extremely inaccurate because users overestimate their jobs. Therefore, in this paper, an efficient machine learning approach is present to predict the runtime of parallel application. The proposed method is divided into three phases. First is to analyze important feature of the history log data by factor analysis. Second is to carry out clustering for the parallel program based on the important features. Third is to build a prediction models by pattern similarity of parallel program log data and estimate runtime. In the experiments, we use workload logs on parallel systems (i.e., NASA-iPSC, LANL-CM5, SDSC-Par95, SDSC-Par96, and CTC-SP2) to evaluate the effectiveness of our approach. Comparing root-mean-square error with other techniques, experimental results show that the proposed method improves the accuracy up to 69.56%. [ABSTRACT FROM AUTHOR]
ISSN:	09208542
DOI:	10.1007/s11227-017-2038-2