Research topic and goals
Prior work has shown the effectiveness of using a multigrid hierarchy to compress the iterative solution to reduce checkpoint size. Recently specially designed floating-point lossy compression algorithms have gained increased popularity due to their ability to significantly reduce floating-point data size and bound the error at each data point. The focus of this collaboration is to explore how to use compression techniques to improve checkpoint-based recovery routines for multigrid methods used either as a stand alone linear system solver or as a preconditioner for other methods such as GMRES or CG. In particular, we explore hybrid compression techniques that switch between the lossy compressor SZ developed at ANL and multi-level multigrid compression in order to minimize the resilience overhead. When using SZ we explore various methods of setting the compressor’s error bounds such as a static tolerance and one that varies the tolerance based on the current accuracy of the solver. To improve the quality of the compressed checkpoint we create and solve a local problem based on the patch of the solution that we are recovering. Finally, we plan to create performance models to explore theoretical possibilities for new compressor designs and system architectures.
The project involves JLESC Fellow Jon Calhoun, JSC affiliated graduate student Micro Altenbernd, Robert Speck from JSC, and Franck Cappello from Argonne. This project was formed though collaborative discussions at the 2018 JLESC workshop in Barcelona.
Results for 2019/2020
Visits and meetings
Planned: Visit by Mirco Altenbernd to Clemson, Spring 2019 for 2 months. Planned: Present talk at JLESC Meeting in Knoxville, April 2019.
Impact and publications
Create performance models for using lossy compression inside running HPC applications. Integrate lossy compression algorithm into applications and measure the impact on performance / accuracy.