An Out-Of-Core Dataflow Middleware to Reduce the Cost of Large Scale Iterative Solvers

Abstract—The emergence of high performance computing (HPC) platforms equipped with solid state drives (SSD) presents an opportunity to dramatically increase the efficiency of outof-core numerical linear algebra computations. In this paper, we explore the advantages and challenges associated with performing sparse matrix vector multiplications (SpMV) on a small SSD testbed. Such an endeavor requires programming abstractions that ease implementation, while enabling an efficient usage of the resources in the testbed. For this purpose, we adopt a task-based out-of-core programming model on top of a dataflow middleware based on the filter stream programming model. We compare the performance of the resulting out-of-core iterated SpMV procedure running on the SSD testbed to the performance of an in-core implementation on a multi-core cluster for solving largescale eigenvalue problems. Preliminary experiments indicate that the out-of-core implementation on the SSD testbed can compete with an in-core implementation in terms of the total CPU-hour cost. We conclude with some architectural design suggestions that can enable numerical linear algebra computations in general to be carried out with high efficiency on SSD-equipped platforms. I