Improving Performance of Iterative Methods by Lossy Checkponting

TL;DR

This paper introduces a lossy checkpointing scheme for iterative methods that reduces overhead and improves performance in large-scale parallel scientific computations by leveraging lossy compression and theoretical analysis.

Contribution

It proposes a novel lossy checkpointing scheme, develops a performance model with bounds, analyzes its impact on various iterative methods, and demonstrates significant efficiency gains in HPC environments.

Findings

Reduces checkpointing overhead by up to 70%.

Achieves 20-58% performance improvement over lossless methods.

Validates effectiveness on a 2,048-core HPC system.

Abstract

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4)We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%~70% compared with traditional checkpointing and 20%~58% compared with lossless-compressed checkpointing, in the presence of system failures.