Virtualized HPC: a contradiction in terms?
Software—Practice & Experience
Checkpointing Orchestration: Toward a Scalable HPC Fault-Tolerant Environment
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
McrEngine: a scalable checkpointing system using data-aware aggregation and compression
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
McrEngine: A scalable checkpointing system using data-aware aggregation and compression
Scientific Programming - Selected Papers from Super Computing 2012
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Clusters and applications continue to grow in size while their mean time between failure (MTBF) is getting smaller. Checkpoint/Restart is becoming increasingly important for large scale parallel jobs. However, the performance of the Checkpoint/Restart mechanism does not scale well with increasing job size due to constraints within the file system. Furthermore, with the advent of multi-core architecture, the situation is aggravated due to larger number of processes running on the same node, trying to checkpoint simultaneously. This results in increased number of file writes at the time of checkpointing which leads to performance degradation. As a result, deployment of Checkpoint/Restart mechanisms for large scale parallel applications is limited. In this work, we explore the Checkpoint/Restart mechanism in MVAPICH2, which uses BLCR as the checkpointing library. Our profiling of the checkpoints for the NAS parallel benchmarks revealed a large number of small file writes interspersed with large writes. Based on these observation we propose to optimize checkpoint creation by classifying checkpoint file writes into small writes, medium writes and large writes based on their size of data to write, and use write aggregation to optimize the small and medium writes. At the aggregation threshold of 512KB, the implementation of our design in BLCR shows improvements from 27% to 32% over the original BLCR in terms of time cost to checkpoint an MPI application.