By Philip J. Hatcher

MIMD desktops are notoriously tricky to application. Data-Parallel Programming demonstrates that architecture-independent parallel programming is feasible by means of describing intimately how courses written in a high-level SIMD programming language can be compiled and successfully executed-on either shared-memory multiprocessors and distributed-memory multicomputers.The authors supply sufficient facts in order that the reader can make a decision the feasibility of architecture-independent programming in a data-parallel language. for every benchmark application they provide the resource code directory, absolute execution time on either a multiprocessor and a multicomputer, and a speedup relative to a sequential application. they usually frequently current a number of options to an analogous challenge, to raised illustrate the strengths and weaknesses of those compilers.The language offered is Dataparallel C, a variation of the unique C* language built via pondering Machines company for its Connection laptop processor array. Separate chapters describe the compilation of Dataparallel C courses for execution at the Sequent multiprocessor and the Intel and nCUBE hypercubes, respectively. The authors record the functionality of those compilers on various benchmark courses and current a number of case studies.Philip J. Hatcher is Assistant Professor within the division of machine technology on the collage of latest Hampshire. Michael J. Quinn is affiliate Professor of computing device technological know-how at Oregon country University.Contents: creation. Dataparallel c program languageperiod Description. layout of a Multicomputer Dataparallel C Compiler. layout of a Multiprocessor Dataparallel C Compiler. Writing effective courses. Benchmarking the Compilers. Case stories. Conclusions.

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There is scarcely enough time to develop production-quality systems and applications software to take full advantage of a particular machine before the next generation is announced. Given the dizzying pace at which new parallel computers are released, architecture­ independent programming environments are a promising option. Programmers are far more likely to develop parallel programs if they know that the next generation of hard­ ware will not make their software obsolete. The data-parallel model of computation offers programmers an abstract machine with a single locus of control, virtual proces­ sors, and global name space.

1 987). Their algorithm is closer to the data-parallel style and achieves much bet­ ter speedup. Beck and Olien have implemented Lansdowne 'S algorithm on the Sequent Balance (Beck and Olien, 1 989). Carriero and Gelernter have compared several C-Linda implementations of the S ieve of Eratosthenes (Carriero and Gelernter, 1 989a). 7 Summary The typical commercial parallel programming language is too low-level and machine­ dependent. Vendors have been slow to recognize that programming environments can be Copyrighted Material Chapter I 22 even more important than processing speed.

The wh ile loop has similar semantics. The MPC repeatedly visits the control expression and the body of a while loop until all the virtual processors that entered the loop have exited. As virtual processors evaluate the control expression to zero (or execute a break statement), they go to the point immediately following the loop and wait for the MPC. 10). 10. An anthropomorphic view of the Dataparallel C while loop. Nobody leaves the table until everyone has finished eating. The rules for if-else and while are examples of a general control-flow model--�me that even handles the goto st atemen t .

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