Abstract
Efficiently programming parallel computers 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 parallel language designs that tackle this difficult issue: Cilk, Chapel, X10, Habanero-Java, OpenMP and OpenCL. Using the parallel computation of the Mandelbrot set as running example, this paper describes how the fundamentals of task parallel programming are dealt with in these languages. Our study suggests that, even though there are many keywords and notions introduced by these languages, they boil down, as far as control issues are concerned, to three key task concepts: creation, synchronization and atomicity. These languages adopt one of three memory models: shared, message passing and Partitioned Global Address Space. The paper is designed to give users and language and compiler designers an up-to-date comparative overview of current parallel languages.
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Khaldi, D., Jouvelot, P., Ancourt, C., Irigoin, F. (2013). Task Parallelism and Data Distribution: An Overview of Explicit Parallel Programming Languages. In: Kasahara, H., Kimura, K. (eds) Languages and Compilers for Parallel Computing. LCPC 2012. Lecture Notes in Computer Science, vol 7760. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37658-0_12
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DOI: https://doi.org/10.1007/978-3-642-37658-0_12
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