Abstract
This paper presents batched iterative solvers for GPU architectures. We elaborate on the design of the batched functionality aiming for optimal performance while still giving the user some flexibility in terms of choosing a sparse matrix format, a preconditioner optimized for the distinct items of the batch, and an application-specific stopping criterion that is evaluated for each problem in the batch, individually. Performance results for benchmark problems coming from PeleLM simulations reveal the potential of the batched iterative solvers for computational chemistry simulations, and their advantage compared to the current vendor-provided batched solutions.
Original language | English |
---|---|
Title of host publication | Proceedings of ScalA 2021 |
Subtitle of host publication | 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 35-43 |
Number of pages | 9 |
ISBN (Electronic) | 9781665411288 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA 2021 - St. Louis, United States Duration: Nov 19 2021 → … |
Publication series
Name | Proceedings of ScalA 2021: 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis |
---|
Conference
Conference | 12th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, ScalA 2021 |
---|---|
Country/Territory | United States |
City | St. Louis |
Period | 11/19/21 → … |
Funding
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. Isha Aggarwal, Aditya Kashi, Pratik Nayak, and Hartwig Anzt were also supported by the “Impuls und Vernetzungsfond” of the Helmholtz Association under grant VH-NG-1241. Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL-PROC-826165.
Keywords
- GPU
- Ginkgo
- Sparse linear systems
- batched solvers