The Hierarchical Computations on Manycore Architectures (HiCMA) library aims to redesign existing dense linear algebra libraries to exploit the data sparsity of the matrix operator. Data sparse matrices arise in many scientific problems (e.g., in statistics-based weather forecasting, seismic imaging, and materials science applications) and are characterized by low-rank off-diagonal tile structure. Numerical low-rank approximations have demonstrated attractive theoretical bounds, both in memory footprint and arithmetic complexity. The core idea of HiCMA is to develop fast linear algebra computations operating on the underlying tile low-rank data format, while satisfying a specified numerical accuracy and leveraging performance from massively parallel hardware architectures.
- Matrix-Matrix Multiplication
- Cholesky Factorization/Solve
- Double Precision
- Task-based Programming Models
- Shared and Distributed-Memory Environments
- Support for StarPU Dynamic Runtime Systems
- Testing Suite and Examples
- LU Factorization/Solve
- Matrix Inversion
- Schur Complements
- Preconditioners
- Hardware Accelerators
- Support for Multiple Precisions
- Autotuning: Tile Size, Fixed Accuracy and Fixed Ranks
- Support for OpenMP, PaRSEC and Kokkos
- Support for HODLR, H, HSS and H2
HiCMA depends on the following libraries:
- Chameleon
- STARS-H
- hwloc
- StarPU
- MPI
- K. Akbudak, H. Ltaief, A. Mikhalev, and D. E. Keyes, Tile Low Rank Cholesky Factorization for Climate/Weather Modeling Applications on Manycore Architectures, International Supercomputing Conference (ISC17), June 18-22, 2017, Frankfurt, Germany.
