Trojan Horse: Aggregate-and-Batch for Scaling Up Sparse Direct Solvers on GPU Clusters
Abstract
Sparse direct solvers are critical building blocks in a range of scientific applications on heterogeneous supercomputers. However, existing sparse direct solvers have not been able to well leverage the high bandwidth and floating-point performance of modern GPUs. The primary challenges are twofold: (1) the absence of a mechanism for aggregating small tasks to saturate the GPU, and (2) the lack of a mechanism for executing a diverse set of small tasks in batch mode on a single GPU.
Fields of Study & Subjects
Parallel Computing and Optimization Techniques
Physical Unclonable Functions (PUFs) and Hardware Security
Advanced Data Storage Technologies
Publication details
- Journal / source
- Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
- Publisher
- ACM
- Pages
- 369–383
- Publication date
- 28 January 2026