2015/09/30 by Justus A. Calvin, Cannada A. Lewis, Edward F. Valeev · 2 citations
Computer Science · #cs.DC
paper · pdf · doi:10.1145/2833179.2833186
8 pages, 6 figures, accepted to IA3 2015. arXiv admin note: text overlap with arXiv:1504.05046
arxiv created 2015/10/09 · arxiv updated 2015/10/13
A task-based formulation of Scalable Universal Matrix Multiplication Algorithm (SUMMA), a popular algorithm for matrix multiplication (MM), is applied to the multiplication of hierarchy-free, rank-structured matrices that appear in the domain of quantum chemistry (QC). The novel features of our formulation are: (1) concurrent scheduling of multiple SUMMA iterations, and (2) fine-grained task-based composition. These features make it tolerant of the load imbalance due to the irregular matrix structure and eliminate all artifactual sources of global synchronization.Scalability of iterative computation of square-root inverse of block-rank-sparse QC matrices is demonstrated; for full-rank (dense) matrices the performance of our SUMMA formulation usually exceeds that of the state-of-the-art dense MM implementations (ScaLAPACK and Cyclops Tensor Framework).