HiCMA: Hierarchical Computations on Manycore Architectures. 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.

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  1. Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun: ExaGeoStatR: A Package for Large-Scale Geostatistics in R (2019) arXiv