|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
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.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsEconomic Epidemiology Seminar Series (supported by CReMic) POLIS events and lectures Moral Psychology Research Group
Other talksBalancing forces during mitosis: from molecular motors up to tissue mechanics Art speak Novel mechanisms connecting innate immunity, microbiota and lipid metabolism: potential therapeutic approaches for atherosclerosis? The future of fMRI in cognitive neuroscience The UK and Cambridge Housing crises: an Open Dialogue Cambridge Particle Meeting 2016