By Thomas Fahringer (auth.), Christoph W. Keßler (eds.)
Distributed-memory multiprocessing structures (DMS), equivalent to Intel's hypercubes, the Paragon, considering Machine's CM-5, and the Meiko Computing floor, have quickly won consumer reputation and promise to carry the computing energy required to unravel the grand problem difficulties of technological know-how and Engineering. those machines are quite low-cost to construct, and are almost certainly scalable to massive numbers of processors. even though, they're tricky to software: the non-uniformity of the reminiscence which makes neighborhood accesses a lot speedier than the move of non-local info through message-passing operations signifies that the locality of algorithms has to be exploited with a view to in attaining appropriate functionality. The administration of knowledge, with the dual ambitions of either spreading the computational workload and minimizing the delays prompted whilst a processor has to attend for non-local information, turns into of paramount value. while a code is parallelized through hand, the programmer needs to distribute the program's paintings and knowledge to the processors for you to execute it. one of many universal ways to take action uses the regularity of such a lot numerical computations. this can be the so-called unmarried software a number of information (SPMD) or facts parallel version of computation. With this system, the knowledge arrays within the unique software are each one disbursed to the processors, setting up an possession relation, and computations defining a knowledge merchandise are played through the processors possessing the data.
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Additional info for Automatic Parallelization: New Approaches to Code Generation, Data Distribution, and Performance prediction
Sipkova. P. Zima. 0 - User's Guide. Jan. 1993.  B. Chapman. T. Fahringer. and H. Zima. Automatic Support for Data Distribution. In Proc. of the Sixth Annual Workshop on Languages and Compilers for Parallel Computing. Portland. Oregon. Aug. 1993. [6) B. Chapman. P. Mehrotra. and H. Zima. Vienna Fortran - A Fortran Language Extension for Distributed-Memory Multiprocessors. Elsevier Science Publishers. Amsterdam. 1991. Also: ICASE Report No. 91-72. Contract No. NASI-18605. NASA. Langley Research Center.
Zima and B. Chapman. Compiling fOT Distributed-Memory Systems. Proceedings of the IEEE Special Section on Languages and Compilers for Parallel Machines. February 1993. to appear. (27) H. Zima. P. Brezany. B. Chapman. P. Mehrotra. and A. Schwald. Vienna FortTan - a language specification. Technical report. ICASE. VA. 1992. ICASE Internal Report 21. (28) H. Zima and B. Chapman. Supercompilersfor Parallel and Vector Computers. ACM Press Frontier Series. Addison-Wesley. 1990. te of the', IS,'. ber of Iter.
Existing Fortran codes are generally too large to analyze fully in depth with respect to all different program transformation sequences. In this paper the Weight Finder, an advanced pro filer for Fortran77 programs based on a von Neumann architecture, is described. It locates the run-time intensive program parts by profiling the underlying program. This allows the user to concentrate program optimization and parallelization efforts on those smaller program sections. g. frequencies, true-ratios, loop iteration counts, etc.
Automatic Parallelization: New Approaches to Code Generation, Data Distribution, and Performance prediction by Thomas Fahringer (auth.), Christoph W. Keßler (eds.)