An Efficient In-Memory Checkpoint Method and its Practice on Fault-Tolerant HPL

• Published in: IEEE transactions on parallel and distributed systems Vol. 29; no. 4; pp. 758 - 771
• Main Authors: Tang, Xiongchao, Zhai, Jidong, Yu, Bowen, Chen, Wenguang, Zheng, Weimin, Li, Keqin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer memory; Encoding; Fault tolerance; Fault tolerant systems; fault-tolerant HPL; in-memory checkpoint; Large-scale systems; memory consumption; Memory management; Methods; Random access memory; Servers; State of the art; System reliability
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2017.2781257

Fault tolerance is increasingly important in high-performance computing due to the substantial growth of system scale and decreasing system reliability. In-memory/diskless checkpoint has gained extensive attention as a solution to avoid the IO bottleneck of traditional disk-based checkpoint methods. However, applications using previous in-memory checkpoint suffer from little available memory space. To provide high reliability, previous in-memory checkpoint methods either need to keep two copies of checkpoints to tolerate failures while updating old checkpoints or trade performance for space by flushing in-memory checkpoints into disk. In this paper, we propose a novel in-memory checkpoint method, called self-checkpoint, which can not only achieve the same reliability of previous in-memory checkpoint methods, but also increase the available memory space for applications by almost 50 percent. To validate our method, we apply self-checkpoint method to an important problem: High-Performance Linpack (HPL) with fault tolerance. We implement a scalable and fault tolerant HPL based on this new method, called SKT-HPL, and validate it on two large-scale systems. Experimental results with 24,576 processes show that SKT-HPL achieves over 95 percent of the performance of the original HPL. Compared to the state-of-the-art in-memory checkpoint method, it improves the available memory size by 47 percent and the performance by 5 percent.