- By:
- Monil, Mohammad Alaul Haque ; Miniskar, Narasinga Rao ; Teranishi, Keita ; Vetter, Jeffrey S; Valero Lara, Pedro
- Page Number:
- 1081-1092
- Book Title:
- SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
- Publication Date:
- March 12, 2026
- Publisher Location:
- Association for Computing Machinery, New York, New York, United States of America
- Conference Name:
- 2023 International Workshop on Performance, Portability & Productivity in HPC at SC23:The International Conference for High Performance Computing, Networking, Storage, and Analysis
- Conference Location:
- Denver, Colorado, United States of America
- Conference Sponsor:
- ACM, SIGHPC, IEEE, TCHPC
- View DOI Listing:
- https://doi.org/10.1145/3624062.362418
Abstract
Vendor libraries are tuned for a specific architecture and are not portable to others. Moreover, they lack support for heterogeneity and multi-device orchestration, which is required for efficient use of contemporary HPC and cloud resources. To address these challenges, we introduce MatRIS—a multilevel math library abstraction for scalable and performance-portable sparse/dense BLAS/LAPACK operations using IRIS runtime. The MatRIS-IRIS co-design introduces three levels of abstraction to make the implementation completely architecture agnostic and provide highly productive programming. We demonstrate that MatRIS is portable without any change in source code and can fully utilize multi-device heterogeneous systems by achieving high performance and scalability on Summit, Frontier, and a CADES cloud node equipped with four NVIDIA A100 GPUs and four AMD MI100 GPUs. A detailed performance study is presented in which MatRIS demonstrates multi-device scalability. When compared, MatRIS provides competitive and even better performance than libraries from vendors and other third parties.
