NDLI: Accelerating Haskell array codes with multicore GPUs

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Accelerating Haskell array codes with multicore GPUs

Content Provider	ACM Digital Library
Author	Chakravarty, Manuel M.T. Lee, Sean Grover, Vinod Keller, Gabriele McDonell, Trevor L.
Abstract	Current GPUs are massively parallel multicore processors optimised for workloads with a large degree of SIMD parallelism. Good performance requires highly idiomatic programs, whose development is work intensive and requires expert knowledge. To raise the level of abstraction, we propose a domain-specific high-level language of array computations that captures appropriate idioms in the form of collective array operations. We embed this purely functional array language in Haskell with an online code generator for NVIDIA's CUDA GPGPU programming environment. We regard the embedded language's collective array operations as algorithmic skeletons; our code generator instantiates CUDA implementations of those skeletons to execute embedded array programs. This paper outlines our embedding in Haskell, details the design and implementation of the dynamic code generator, and reports on initial benchmark results. These results suggest that we can compete with moderately optimised native CUDA code, while enabling much simpler source programs.
Starting Page	3
Ending Page	14
Page Count	12
File Format	PDF
ISBN	9781450304863
DOI	10.1145/1926354.1926358
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-01-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Data parallelism Dynamic compilation Skeletons Haskell Arrays Gpgpu
Content Type	Text
Resource Type	Article

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