null
(Ed.)
SpMV, the product of a sparse matrix and a dense
vector, is emblematic of a new class of applications that are memory
bandwidth and communication, not flop, driven. Sparsity and
randomness in such computations play havoc with performance,
especially when strong, instead of weak, scaling is attempted. In
this study we develop and evaluate a hybrid implementation for strong scaling of the Compressed Vectorization-oriented sparse Row (CVR) approach to SpMV on a cluster of Intel Xeon Phi Knights Landing (KNL) processors. We show how our hybrid SpMV implementation achieves increased computational performance, yet does not address the dominant communication overhead factor at extreme scale. Issues with workload distribution, data placement, and remote reductions are assessed over a range of matrix characteristics. Our results indicate that as P ! 1 communication overhead is by far the dominant factor despite improved computational performance.
more »
« less