Adaptive incremental checkpointing for massively parallel systems

Abstract

Given the scale of massively parallel systems, occurrence of faults is no longer an exception but a regular event. Periodic checkpointing is becoming increasingly important in these systems. However, huge memory footprints of parallel applications place severe limitations on scalability of normal checkpointing techniques. Incremental checkpointing is a well researched technique that addresses scalability concerns, but most of the implementations require paging support from hardware and the underlying operating system, which may not be always available. In this paper, we propose a software based adaptive incremental checkpoint technique which uses a secure hash function to uniquely identify changed blocks in memory. Our algorithm is the first self-optimizing algorithm that dynamically computes the optimal block boundaries, based on the history of changed blocks. This provides better opportunities for minimizing checkpoint file size. Since the hash is computed in software, we do not need any system support for this. We have implemented and tested this mechanism on the BlueGene/L system. Our results on several well-known benchmarks are encouraging, both in terms of reduction in average checkpoint file size and adaptivity towards application's memory access patterns.