high performance computing on graphics processing units: hgpu.org

Programming parallel machines as effectively as sequential ones would ideally require a language that provides high-level programming constructs in order to avoid the programming errors frequent when expressing parallelism. Since task parallelism is often considered more error-prone than data parallelism, we survey six popular and efficient parallel programming languages that tackle this difficult issue: Cilk, Chapel, X10, Habanero-Java, OpenMP and OpenCL. Using as single running example a parallel implementation of the computation of the Mandelbrot set, this paper describes how the fundamentals of task parallel programming, namely collective and point-to-point synchronization and mutual exclusion, are dealt with in these languages. Our study suggests that, even though there is a wealth of various names and notions introduced by these languages, they all boil down to three key task concepts: creation, synchronization and atomicity. The paper is designed to give users and language and compiler designers an overview of current parallel languages.