Task-based parallel sparse matrix-vector multiplication (SpMVM) with GPI-2

We present a task-based implementation of SpMVM with the PGAS communication library GPI-2. This computational kernel is essential for the overall performance of the Krylov subspace solvers but its proper hybrid parallel design is nowadays still a challenge on hierarchical architectures consisting of multi- and many-core sockets and nodes. The GPI-2 library allows, by default and in a natural way, a task-based parallelization. Thus, our implementation is fully asynchronous and it considerably differs from the standard hybrid approaches combining MPI and threads/OpenMP. Here we briefly describe the GPI-2 library, our implementation of the SpMVM routine, and then we compare the performance of our Jacobi preconditioned Richardson solver against the PETSc-Richardson using Poisson BVP in a unit cube as a benchmark test. The comparison employs two types of domain decomposition and demonstrates the preemptive performance and better scalability of our task-based implementation.