Self-Checkpoint: An In-Memory Checkpoint Method Using Less Space and Its Practice on Fault-Tolerant HPL
Abstract
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%. To validate our method, we apply the self-checkpoint to an important problem, fault tolerant HPL. 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% 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% and the performance by 5%.
Jidong Zhai
Associate Professor
(特别研究员、博士生导师)
