We leverage performance of HPC scientific applications using tile low-rank matrix computations. The idea consists in revisiting tile algorithms using low-rank matrix approximations by exploiting the data sparsity of the dense operator coming from computational astronomy, seismic imaging, and climate/weather prediction applications. We rely on the HiCMA software library for providing sequential numerical kernels and oneAPI runtime system for orchestrating the resulting computational tasks onto parallel systems. We demonstrate performance superiority against state-of-the-art numerical libraries with high productivity in mind.


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