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.
Recommended Citation
D. Tao et al., "Improving Performance of Iterative Methods by Lossy Checkponting," Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing (2018, Tempe, AZ), pp. 52 - 65, Association for Computing Machinery (ACM), Jun 2018.
The definitive version is available at https://doi.org/10.1145/3208040.3208050
Meeting Name
27th International Symposium on High-Performance Parallel and Distributed Computing, HPDC '18 (2018: Jun. 11-15, Tempe, AZ)
Department(s)
Computer Science
Keywords and Phrases
Checkpoint/Restart; Iterative Methods; Lossy Compression; Numerical Linear Algebra; Performance Optimization; Resilience
International Standard Book Number (ISBN)
978-145035785-2
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2018 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
11 Jun 2018
Comments
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC.