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Meta-programming and auto-tuning in the search for high performance GPU code

Vollmer, Michael, Svensson, B.J., Holk, Eric, Newton, Ryan R. (2015) Meta-programming and auto-tuning in the search for high performance GPU code. In: FHPC 2015: Proceedings of the 4th ACM SIGPLAN Workshop on Functional High-Performance Computing. . pp. 1-11. ACM ISBN 978-1-4503-3807-3. (doi:10.1145/2808091.2808092) (KAR id:95046)

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Writing high performance GPGPU code is often difficult and time-consuming, potentially requiring laborious manual tuning of low-level details. Despite these challenges, the cost in ignoring GPUs in high performance computing is increasingly large. Auto-tuning is a potential solution to the problem of tedious manual tuning. We present a framework for auto-tuning GPU kernels which are expressed in an embedded DSL, and which expose compile-time parameters for tuning. Our framework allows for kernels to be polymorphic over what search strategy will tune them, and allows search strategies to be implemented in the same meta-language as the kernel-generation code (Haskell). Further, we show how to use functional programming abstractions to enforce regular (hyper-rectangular) search spaces. We also evaluate several common search strategies on a variety of kernels, and demonstrate that the framework can tune both EDSL and ordinary CUDA code.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1145/2808091.2808092
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Michael Vollmer
Date Deposited: 06 Dec 2022 16:56 UTC
Last Modified: 09 Dec 2022 10:21 UTC
Resource URI: (The current URI for this page, for reference purposes)
Vollmer, Michael:
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