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Automatic SIMD vectorization for Haskell
If you have a question about this talk, please contact Dominic Orchard.
Expressing algorithms using immutable arrays greatly simplifies the challenges of automatic SIMD vectorization, since several important classes of dependency violations cannot occur. The Haskell programming language provides libraries for programming with immutable arrays, and compiler support for optimizing them to eliminate the overhead of intermediate temporary arrays. We describe an implementation of automatic SIMD vectorization in a Haskell compiler which gives significant vector speedups for a range of programs written in a natural programming style. We compare performance with that of programs compiled by the Glasgow Haskell Compiler.
This is joint work with Leaf Petersen and Neal Glew (both Intel). This is an ICFP practice talk.
This talk is part of the Computer Laboratory Programming Research Group Seminar series.
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Other listsNational Centre for Statistical Ecology (NCSE) Seminars Cambridge Interdisciplinary Performance Network ADF: Amsterdam Density Functional, Concepts and Applications
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