The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrixvector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high performance computing architectures. The introduction of General Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem.With this paper we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.

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Coarray Fortran is a set of features of the Fortran 2008 standard that make Fortran a PGAS parallel programming language. Two commercial compilers currently support coarrays: Cray and Intel. Here we present two coarray transport layers provided by the new OpenCoarrays project: one library based on MPI and the other on GASNet. We link the GNU Fortran (GFortran) compiler to either of the two OpenCoarrays implementations and present performance comparisons between executables produced by GFortran and the Cray and Intel compilers. The comparison includes synthetic benchmarks, application prototypes, and an application kernel. In our tests, Intel outperforms GFortran only on intra-node small transfers (in particular, scalars). GFortran outperforms Intel on intra-node array transfers and in all settings that require inter-node transfers. The Cray comparisons are mixed, with either GFortran or Cray being faster depending on the chosen hardware platform, network, and transport layer.

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