Design of a task-parallel version of ILUPACK for graphics processors

Abstract

In many scientific and engineering applications, the solution of large sparse systems of equations is one of the most important stages. For this reason, many libraries have been developed among which ILUPACK stands out due to its efficient inverse-based multilevel preconditioner. Several parallel versions of ILUPACK have been proposed in the past. In particular, two task-parallel versions, for shared and distributed memory platforms, and a GPU accelerated data-parallel variant have been developed to solve symmetric positive definite linear systems. In this work we evaluate the combination of both previously covered approaches. Specifically, we leverage the computational power of one GPU (associated with the data-level parallelism) to accelerate each computation of the multicore (task-parallel) variant of ILUPACK. The performed experimental evaluation shows that our proposal can accelerate the multicore variant when the leaf tasks of the parallel solver offer an acceptable dimension.