NDLI: A collection-oriented programming model for performance portability

Content Provider	ACM Digital Library
Author	Catanzaro, Bryan Garland, Michael Sidelnik, Albert Muralidharan, Saurav Hall, Mary
Abstract	This paper describes Surge, a collection-oriented programming model that enables programmers to compose parallel computations using nested high-level data collections and operators. Surge exposes a code generation interface, decoupled from the core computation, that enables programmers and autotuners to easily generate multiple implementations of the same computation on various parallel architectures such as multi-core CPUs and GPUs. By decoupling computations from architecture-specific implementation, programmers can target multiple architectures more easily, and generate a search space that facilitates optimization and customization for specific architectures. We express in Surge four real-world benchmarks from domains such as sparse linear-algebra and machine learning and from the same performance-portable specification, generate OpenMP and CUDA C++ implementations. Surge generates efficient, scalable code which achieves up to 1.32x speedup over handcrafted, well-optimized CUDA code. .
Starting Page	263
Ending Page	264
Page Count	2
File Format	PDF
ISBN	9781450332057
DOI	10.1145/2688500.2688537
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Performance-portability Nested-data-parallelism
Content Type	Text
Resource Type	Article

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A collection-oriented programming model for performance portability