By Thomas Fahringer (auth.), Christoph W. Keßler (eds.)
Distributed-memory multiprocessing platforms (DMS), reminiscent of Intel's hypercubes, the Paragon, considering Machine's CM-5, and the Meiko Computing floor, have speedily won consumer attractiveness and promise to convey the computing strength required to unravel the grand problem difficulties of technology and Engineering. those machines are fairly reasonably cheap to construct, and are in all likelihood scalable to giant numbers of processors. notwithstanding, they're tough to application: the non-uniformity of the reminiscence which makes neighborhood accesses a lot quicker than the move of non-local info through message-passing operations signifies that the locality of algorithms needs to be exploited as a way to in achieving appropriate functionality. The administration of information, with the dual objectives of either spreading the computational workload and minimizing the delays triggered whilst a processor has to attend for non-local information, turns into of paramount significance. whilst a code is parallelized through hand, the programmer needs to distribute the program's paintings and information to the processors in an effort to execute it. one of many universal methods to take action uses the regularity of so much numerical computations. this can be the so-called unmarried application a number of facts (SPMD) or facts parallel version of computation. With this technique, the information arrays within the unique application are each one dispensed to the processors, developing an possession relation, and computations defining a knowledge merchandise are played by means of the processors possessing the data.
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Extra info for Automatic Parallelization: New Approaches to Code Generation, Data Distribution, and Performance prediction
11 mlou .. enting unit 1011 .. Ii na 1 eheels .. reconstruc ting .. starting COIp llation .. starting execution . . Instru' entation dont. point .. , reading result Illes .. updating syntill< trees .. Allribute units do"... 3 Visualizing the sequential program parameters DO- 32 3 Predicting Execution Times of Sequential Scientific Kernels N. B. MacDonald DEPT. ac . uk Abstract: Parallel computer systems are typically employed in order to obtain higher performance or cost-perfonnance levels than can be achieved by a conventional system.
The time model used in this paper has a very naive view of the memory hierarchy: variables referenced several times in a statement will incur the corresponding number of load costs. This can clearly lead to overestimation of execution time. Significantly, Fragment D, by far the least-well modelled of the example codes, has the highest number of repeated references to objects. Furthermore, the best results overall were obtained for the T800, which probably makes significantly less use of registers than the other platforms.
M. Gemdt. Parallelizationfor Distributed-Memory Multiprocessing Systems. PhD thesis. University of Bonn. December 1989. L. Graham. B. Kessler. K. McKusick. gprof: A Call Graph Execution Pro filer. In Proceedings of the SIGPLAN 82 Symposium on Compiler Construction. pages 120 - 126. June 1982. SIGPLAN Notices. VoU7. 6. E. Knuth. An empirical study of FORTRAN programs. Software - Practice and Experience. 1971. [15) P. Lenzi and G. Serazzi. ParMon: Parallel Monitor. Technical Report N3/95. Dipartimento di Elettronica.