We present a C++ header-only parallel sparse matrix library, based on sparse quadtree representation of matrices using the Chunks and Tasks programming model. The library implements a number of sparse matrix algorithms for distributed memory parallelization that are able to dynamically exploit data locality to avoid movement of data. This is demonstrated for the example of block-sparse matrix-matrix multiplication applied to three sequences of matrices with different nonzero structure, using the CHT-MPI 2.0 runtime library implementation of the Chunks and Tasks model. The runtime library succeeds to dynamically load balance the calculation regardless of the sparsity structure.
@article{arxiv.2011.11762,
title = {The Chunks and Tasks Matrix Library 2.0},
author = {Emanuel H. Rubensson and Elias Rudberg and Anastasia Kruchinina and Anton G. Artemov},
journal= {arXiv preprint arXiv:2011.11762},
year = {2020}
}