Efficient detection of silent data corruption in HPC applications with synchronization-free message verification
Efficient detection of silent data corruption in HPC applications with synchronization-free message verification
复制标题
通过免同步消息验证有效检测 HPC 应用程序中的静默数据损坏
DOI:
10.1007/s11227-021-03892-4
复制
发表时间:
2021-06
期刊:
影响因子:
--
通讯作者:
Depei Qian
中科院分区:
文献类型:
--
作者:
Guozhen Zhang;Yi Liu;Hailong Yang;Depei Qian
Nowadays, high-performance computing (HPC) is stepping forward to exascale era. However, silent data corruption (SDC) behaved as bit-flipping can cause disastrous consequences for scientific computation, which jeopardizes the reliability of HPC at large scale. The most commonly used methods to address SDC are based on modular redundancy, which usually requires keeping execution progress consistent between replicas by synchronization and performing additional message transmission and comparison during program execution. Although such methods can detect SDC with high recall, they can introduce significant performance overhead and even stall the execution progress at a large scale. To our knowledge, this paper proposes the first solution of SDC detection without requiring synchronization and additional message transmission between replicas. It combines message logging with an innovative asynchronous message comparison mechanism, which uses specialized service routines (Data-Analytic-Service, DAS) to perform progress comparison without interfering target program execution. Besides, our solution adopts a distributed parallel architecture to perform DAS and utilizes an innovative reference mechanism based on single non-deterministic event to guarantee the consistent execution of different replicas. We implemented a user-level prototype, termed as synchronization-free SDC detection (SFSD). The experimental results on the Tianhe-2 supercomputer show that SFSD is effective in detecting SDC, with low-performance overhead (within 10%) and an acceptable recall rate. Moreover, SFSD exhibits good scalability when applied to large-scale program executions.
登录
查看更多内容
DOI:
10.1145/2802658.2802665
发表时间:
2015-09
期刊:
Proceedings of the 22nd European MPI Users' Group Meeting
影响因子:
--
作者:
L. Bautista-Gomez;F. Cappello
通讯作者:
L. Bautista-Gomez;F. Cappello
DOI:
10.1109/cluster.2017.128
发表时间:
2017-09
期刊:
2017 IEEE International Conference on Cluster Computing (CLUSTER)
影响因子:
--
作者:
Omer Subasi;S. Di;Prasanna Balaprakash;O. Unsal;Jesús Labarta;A. Cristal;S. Krishnamoorthy;F. Cappello
通讯作者:
Omer Subasi;S. Di;Prasanna Balaprakash;O. Unsal;Jesús Labarta;A. Cristal;S. Krishnamoorthy;F. Cappello
DOI:
10.1109/sc.2012.13
发表时间:
2012-11
期刊:
2012 International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子:
--
作者:
Vilas Sridharan;Dean Liberty
通讯作者:
Vilas Sridharan;Dean Liberty
DOI:
10.1145/2063384
发表时间:
2011-11
期刊:
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子:
--
作者:
Scott A. Lathrop;J. Costa;W. Kramer
通讯作者:
Scott A. Lathrop;J. Costa;W. Kramer
DOI:
10.1145/2687651
发表时间:
2015-01
期刊:
ACM Transactions on Architecture and Code Optimization (TACO)
影响因子:
--
作者:
Leo Porter;M. Laurenzano;Ananta Tiwari;Adam Jundt;W. A. Ward;R. Campbell;L. Carrington
通讯作者:
Leo Porter;M. Laurenzano;Ananta Tiwari;Adam Jundt;W. A. Ward;R. Campbell;L. Carrington