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U.S. Department of Energy
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Data parallelism

Conference ·
OSTI ID:10141735
Data locality is fundamental to performance on distributed memory parallel architectures. Application programmers know this well and go to great pains to arrange data for optimal performance. Data Parallelism, a model from the Single Instruction Multiple Data (SIMD) architecture, is finding a new home on the Multiple Instruction Multiple Data (MIMD) architectures. This style of programming, distinguished by taking the computation to the data, is what programmers have been doing by hand for a long time. Recent work in this area holds the promise of making the programmer`s task easier.
Research Organization:
Lawrence Livermore National Lab., CA (United States)
Sponsoring Organization:
USDOE, Washington, DC (United States)
DOE Contract Number:
W-7405-ENG-48
OSTI ID:
10141735
Report Number(s):
UCRL-JC--111827; CONF-930117--6; ON: DE93009300
Country of Publication:
United States
Language:
English

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