NDLI: Towards Composable GPU Programming: Programming GPUs with Eager Actions and Lazy Views

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Towards Composable GPU Programming: Programming GPUs with Eager Actions and Lazy Views

Content Provider	ACM Digital Library
Author	Humernbrum, Tim Steuwer, Michel Haidl, Michael Gorlatch, Sergei Dirks, Hendrik
Abstract	In this paper, we advocate a composable approach to programming systems with Graphics Processing Units (GPU): programs are developed as compositions of generic, reusable patterns. Current GPU programming approaches either rely on low-level, monolithic code without patterns (CUDA and OpenCL), which achieves high performance at the cost of cumbersome and error-prone programming, or they improve the programmability by using pattern-based abstractions (e.g., Thrust) but pay a performance penalty due to inefficient implementations of pattern composition. We develop an API for GPUs based programming on C++ with STL-style patterns and its compiler-based implementation. Our API gives the application developers the native C++ means (views and actions) to specify precisely which pattern compositions should be automatically fused during code generation into a single efficient GPU kernel, thereby ensuring a high target performance. We implement our approach by extending the range-v3 library which is currently being developed for the forthcoming C++ standards. The composable programming in our approach is done exclusively in the standard C++14, with STL algorithms used as patterns which we re-implemented in parallel for GPU. Our compiler implementation is based on the LLVM and Clang frameworks, and we use advanced multi-stage programming techniques for aggressive runtime optimizations. We experimentally evaluate our approach using a set of benchmark applications and a real-world case study from the area of image processing. Our codes achieve performance competitive with CUDA monolithic implementations, and we outperform pattern-based codes written using Nvidia's Thrust.
Starting Page	58
Ending Page	67
Page Count	10
File Format	PDF
ISBN	9781450348836
DOI	10.1145/3026937.3026942
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-04
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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