Program and data transformations for efficient execution on distributed memory architectures
Abstract: "This report is concerned with the efficient execution of array computation on Distributed Memory Architectures by applying compiler-directed program and data transformations. By translating a sub- set of a single-assignment language, Sisal, into a linear algebraic framework it is possible to transform a program so as to reduce load imbalance and non-local memory access. A new test is presented which allows the construction of transformations to reduce load imbalance. By a new expression of data alignment, transformations to reduce non-local access are derived. A new pre-fetching procedure, which prevents redundant non-local accesses, is presented and forms the basis of a new data partitioning methodology.
"Abstract: "This report is concerned with the efficient execution of array computation on Distributed Memory Architectures by applying compiler-directed program and data transformations. By translating a sub- set of a single-assignment language, Sisal, into a linear algebraic framework it is possible to transform a program so as to reduce load imbalance and non-local memory access. A new test is presented which allows the construction of transformations to reduce load imbalance. By a new expression of data alignment, transformations to reduce non-local access are derived. A new pre-fetching procedure, which prevents redundant non-local accesses, is presented and forms the basis of a new data partitioning methodology."@en
"By applying these transformations in a straightforward manner to some well known scientific programs, it is shown that this approach is competitive with hand-crafted methods.""@en
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