Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms
Professor Maryam Mehri Dehnavi is the PI on a new NSF grant entitled "Performance-in-Depth Sparse Solvers for Heterogeneous Parallel Platforms." This is a two year project totaling $175,000 and is supported under the Computer and Information Science and Engineering (CISE) Research Initiation Initiative (CRII).
The project conducts an in-depth investigation of performance bottlenecks in sparse solvers and reformulates their standard variants to deliver end-to-end performance. Cross-layer solutions are developed to improve data locality, reduce communication, and increase inherent parallelism in sparse linear solvers. The solutions involve multi-level algorithm restructuring and performance tuning to significantly improve the scalability and performance of sparse computations while preserving their numerical accuracy, convergence, and stability. The proposed methods and algorithms are implemented as domain-specific high-performance software and a benchmark suite to promote iterative improvements of the developed algorithms and codes.