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.
|Title of host publication||HPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing|
|Number of pages||14|
|State||Published - Jun 11 2018|
|Event||27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018 - Tempe, United States|
Duration: Jun 11 2018 → Jun 15 2018
|Name||HPDC 2018 - Proceedings of the 2018 International Symposium on High-Performance Parallel and Distributed Computing|
|Conference||27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018|
|Period||6/11/18 → 6/15/18|
Bibliographical noteFunding Information:
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations – the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant No. 1305624, No. 1513201, and No. 1619253. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory. We would like to thank Dr. Patrick Bridges for his helpful suggestions for the final paper.
© 2018 Association for Computing Machinery.
- Iterative Methods
- Lossy Compression
- Numerical Linear Algebra
- Performance Optimization
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications