We introduce a new parallelization framework for scientific computing based on BDSC, an efficient automatic scheduling algorithm for parallel programs in the presence of resource constraints on the number of processors and their local memory size. BDSC extends Yang and Gerasoulis's Dominant Sequence Clustering (DSC) algorithm; it uses sophisticated cost models and addresses both shared and distributed parallel memory architectures. We describe BDSC, its integration within the PIPS compiler infrastructure and its application to the parallelization of four well-known scientific applications: Harris, ABF, equake and IS. Our experiments suggest that BDSC's focus on efficient resource management leads to significant parallelization speedups on both shared and distributed memory systems, improving upon DSC results, as shown by the comparison of the sequential and parallelized versions of these four applications running on both OpenMP and MPI frameworks.
Abstract. Programming parallel machines as effectively as sequential ones would ideally require a language that provides high-level programming constructs to avoid the programming errors frequent when expressing parallelism. Since task parallelism is considered more error-prone than data parallelism, we survey six popular and efficient parallel language designs 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, i.e., collective and point-to-point synchronization and mutual exclusion, are dealt with in these languages. We discuss how these languages allocate and distribute data over memory. Our study suggests that, even though there are many keywords and notions introduced by these languages, they all boil down, as far as control issues are concerned, to three key task concepts: creation, synchronization and atomicity. Regarding memory models, these languages adopt one of three approaches: shared memory, message passing and PGAS (Partitioned Global Address Space). The paper is designed to give users and language and compiler designers an upto-date comparative overview of current parallel languages.
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