A Scalable Runtime Fault Localization Framework for High-Performance Computing Systems
| Autor: | Kang Yu, Jian Gao, Peng Qing, Hong-Mei Wei |
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| Rok vydání: | 2017 |
| Předmět: |
Job scheduler
021110 strategic defence & security studies business.industry Computer science Distributed computing 0211 other engineering and technologies Context (language use) 02 engineering and technology computer.software_genre Supercomputer Fault (power engineering) Fault detection and isolation Theoretical Computer Science Tree (data structure) Middleware Embedded system Scalability business computer Software Information Systems |
| Zdroj: | International Journal of Parallel Programming. 46:749-761 |
| ISSN: | 1573-7640 0885-7458 |
| Popis: | Fault localization has become an increasingly challenging issue in high-performance computing (HPC) systems. Various techniques have been used for HPC systems. However, as the HPC systems scale out, resulting in the rapid deterioration of the existing techniques. In this context, we propose a message-passing based fault localization framework, namely MPFL, which provides a light-weight distributed service using tree-based fault detection (TFD) and fault analysis (TFA) algorithms. In essence, MPFL serves as a fault localization engine within message-passing libraries by enabling several system middleware such as job scheduler to provide abnormal information. We present details of the MPFL framework, including the implementation of TFD and TFA. Further, we develop the fault localization engine prototype within MVAPICH2. The experimental evaluation is performed on a typical HPC cluster with 10 computing nodes, which demonstrate the capability of MPFL and show that the MPFL service does not affect the performance of an application in practice. |
| Databáze: | OpenAIRE |
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