Abstract
Thanks to the multi-threads implementation we are able to efficiently overlap the communication and the computation phases. The unnecessary waiting times that may occur when using a sequential process are avoided. The limited number of threads involved in our implementation prevents the system overhead that in many cases counterbalances the benefit of the parallelization. We think that these improvements will contribute to a better maturity for data parallel compilers and so automatic code generation for distributed memory parallel architectures will become a realistic means of programming these architectures for application users. More details can be found in [1].
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F. André.-A multi-threads runtime for the pandore data-parallel compiler.-Research Report 986, IRISA, France, February 1996.
F. André, M. Le Fur, Y. Mahéo, and J.-L. Pazat.-The pandore data parallel compiler and its portable runtime.-In HPCN' 95, Milan, Italy, LNCS 919, Springer Verlag., pages 176–183, May 1995.
C.W. Tseng.-An Optimizing Fortran D Compiler for MIMD Distributed-Memory Machines.-PhD thesis, Rice University, January 1993.
H. P. Zima and B. M. Chapman.-Compiling for Distributed-Memory Systems.-Research Report APCP/TR 92-17, Austrian Center for Parallel Computation, November 1992.
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© 1996 Springer-Verlag Berlin Heidelberg
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André, F., Pazat, JL. (1996). A multi-threads runtime for the pandore data-parallel compiler. In: Liddell, H., Colbrook, A., Hertzberger, B., Sloot, P. (eds) High-Performance Computing and Networking. HPCN-Europe 1996. Lecture Notes in Computer Science, vol 1067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61142-8_678
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DOI: https://doi.org/10.1007/3-540-61142-8_678
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