Generating local addresses and communication sets for data-parallel programs
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Generating local addresses and communication sets for data-parallel programs

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Published by Research Institute for Advanced Computer Science, NASA Ames Research Center, National Technical Information Service, distributor in [Moffett Field, Calif.], [Springfield, Va .
Written in English

Subjects:

  • Electronic data processing -- Distributed processing.

Book details:

Edition Notes

StatementSiddhartha Chatterjee ... [et al.].
SeriesNASA contractor report -- NASA CR-194605., RIACS technical report -- 93.03., RIACS technical report -- TR 93-3.
ContributionsChatterjee, Siddhartha., Research Institute for Advanced Computer Science (U.S.)
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL17680105M

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Generating local addresses and communication sets is an important issue in distributed-memory implementations of data-parallel languages such as High Performance Fortran. OpenURL. Abstract. Generating local addresses and communication sets is an important issue in distributedmemory implementations of data-parallel languages such as High Performance Fortran. We demonstrate a storage scheme for an array A affinely aligned to a template that is distributed across p processors with a cyclic#k# distribution that does not waste any storage, and show that, under this storage scheme, the local memory access sequence of any processor for a computation . Generating local addresses and communication sets is an important issue in distributedmemory implementations of data-parallel languages such as High Performance by: Siddhartha Chatterjee, John R. Gilbert, Fred J. E. Long, Robert Schreiber, Shang-Hua Teng Generating Local Address and Communication Sets for Data-Parallel Programs PPoPP,

The book is a unique survey on the current status and future perspectives of the currently very promising and popular data parallel programming model. Much attention is paid to the style of writing and complementary coverage of the relevant issues throughout the 12 chapters. The normal Execution of CPU under all the same programs is as shown except for running the program is as shown below: Fig The CPU utilization when idle (ii) Case Study 2. (Using Data Parallel C Environment, DPCE) The execution of the data parallel C program which can control the data that is granted to each of the cores of the. © Copyright by Ian FosterIan Foster. Generating local addresses and communication sets for data-parallel programs Author: Siddhartha Chatterjee ; Research Institute for Advanced Computer Science (U.S.).

Fig. 2. Structure of the proposed data-parallel compiling system. global references of array elements can be translated into local addresses on processors, and so that communication sets can be generated, if needed, so that processors to access non-local data. According to this information, the compiler can then generate an SPMD code for execution. Generally, a data-parallel language compiler can be expected to generate reasonably efficient code when a program's communication structure is regular and local. Programs involving irregular and global communication patterns are less likely to be compiled efficiently. These and other performance issues are addressed in Section Collective Communication Operations They represent regular communication patterns that are performed by parallel algorithms. Collective: Involve groups of processors Used extensively in most data-parallel algorithms. The parallel efficiency of these algorithms depends on efficient implementation of these operations. Generate up to 10, rows at a time instead of the maximum Save your form configurations so you don't have to re-create your data sets every time you return to the site. Every $20 you donate adds a year to your account. You may return at a later date to add more time to your account - it will be added to the end of your current time.