NDLI: Optimization of scan algorithms on multi- and many-core processors

Content Provider	IEEE Xplore Digital Library
Author	Qiao Sun Chao Yang
Copyright Year	2014
Description	Author affiliation: Inst. of Software, Beijing, China (Qiao Sun; Chao Yang)
Abstract	Scan is a basic building block widely utilized in many applications. With the emergence of multi-core and many-core processors, the study of highly scalable parallel scan algorithms becomes increasingly important. In this paper, we first propose a novel parallel scan algorithm based on the fine grain dynamic task scheduling in QUARK, and then derive a cache-friendly framework for any parallel scan kernel. The QUARK-scan is superior to the fastest available counterpart proposed by Zhang in 2012 and many other parallel scans in several aspects, including the greatly improved load balance and the substantially reduced number of global barriers. On the other hand, the cache-friendly framework helps in improving the cache line usage and is flexible to apply to any parallel scan kernel. A variety of optimization techniques such as SIMD vectorization, loop unrolling, adjacent synchronization and thread affinity are exploited in QUARKscan and the cache-friendly versions of both QUARK-scan and Zhang's scan. Experiments done on three typical multi- and many-core platforms indicate that the proposed QUARK-scan and the cache-friendly Zhang's scan are superior in different scenarios.
Starting Page	1
Ending Page	10
File Size	893254
Page Count	10
File Format	PDF
e-ISBN	9781479959761
DOI	10.1109/HiPC.2014.7116883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-17
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Synchronization Arrays Dynamic scheduling Program processors Kernel Optimization Heuristic algorithms Cache-friendly Parallel scan Multi-core Many-core QUARKscan Dynamic task scheduling
Content Type	Text
Resource Type	Article

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