NDLI: High-Performance Graph Analytics on Manycore Processors

Content Provider	IEEE Xplore Digital Library
Author	Slota, G.M. Rajamanickam, S. Madduri, K.
Copyright Year	2015
Description	Author affiliation: Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA (Slota, G.M.; Madduri, K.) \|\| Scalable Algorithms Dept., Sandia Nat. Labs., Albuquerque, NM, USA (Rajamanickam, S.)
Abstract	The divergence in the computer architecture landscape has resulted in different architectures being considered mainstream at the same time. For application and algorithm developers, a dilemma arises when one must focus on using underlying architectural features to extract the best performance on each of these architectures, while writing portable code at the same time. We focus on this problem with graph analytics as our target application domain. In this paper, we present an abstraction-based methodology for performance-portable graph algorithm design on manicure architectures. We demonstrate our approach by systematically optimizing algorithms for the problems of breadth-first search, color propagation, and strongly connected components. We use Kokkos, a manicure library and programming model, for prototyping our algorithms. Our portable implementation of the strongly connected components algorithm on the NVIDIA Tesla K40M is up to 3.25× faster than a state-of-the-art parallel CPU implementation on a dual-socket Sandy Bridge compute node.
Starting Page	17
Ending Page	27
File Size	258586
Page Count	11
File Format	PDF
ISBN	9781479986491
ISSN	15302075
DOI	10.1109/IPDPS.2015.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruction sets Silicon Optimization Synchronization Color Arrays Parallel processing portability graph computations BFS color propagation GPU parallel performance
Content Type	Text
Resource Type	Article

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