1999
DOI: 10.1016/s0167-8191(99)00074-5
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Compiling High Performance Fortran for distributed-memory architectures

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Cited by 15 publications
(7 citation statements)
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“…Unlike ZPL, HPF does not restrict operations between arrays with different data distributions. Thus, a statement like C = A + B may or may not require communication depending on the distributions of A, B, and C. A great deal of research in the HPF community has focused on generating efficient and minimal communication for HPF programs [13,5]. Some of these efforts apply strength reduction to communication, replacing general all-to-all communications with reductions or nearest neighbor exchanges.…”
Section: Scheduling Communicationmentioning
confidence: 99%
“…Unlike ZPL, HPF does not restrict operations between arrays with different data distributions. Thus, a statement like C = A + B may or may not require communication depending on the distributions of A, B, and C. A great deal of research in the HPF community has focused on generating efficient and minimal communication for HPF programs [13,5]. Some of these efforts apply strength reduction to communication, replacing general all-to-all communications with reductions or nearest neighbor exchanges.…”
Section: Scheduling Communicationmentioning
confidence: 99%
“…We mention only a few among the many relevant references. In particular, languages such as Kali, Vienna Fortran, Fortran D, HPF, and Opus [26,9,14,21,10], and compilation systems such as SUPERB, the Fortran D compiler, Paradigm, and the Vienna Fortran Compiler [35,14,4,5] as well as the huge amount of work on irregular problems have strongly influenced the macroserver design. Macroservers address some weaknesses of existing systems (in particular with respect to performance), support a higher-level view of data structures and distribution than existing in most present systems, and address a number of specific features of PIM-based architectures.…”
Section: • Dealing With the Tradeoff Between Locality And Load Balancingmentioning
confidence: 99%
“…The elements whose value is 0 are represented by empty boxes; the non-zero elements are explicitly specified and numbered in row major order (for simplicity, we have chosen as the value its position in this numbering scheme). The distribution is irregular, based on (2, 7), (3,8), (4, 6), (7,7); and λ(4) = [8 : 10, 5 : 8] with nonzero elements (8,5), (8,8), (9,5), (10,7).…”
Section: Example: Sparse Matrix Distributionmentioning
confidence: 99%
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