NDLI: High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs

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High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs

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High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs

Content Provider	ACM Digital Library
Author	Mesman, Bart Corporaal, Henk van den Braak, Gert-Jan Nugteren, Cedric
Abstract	Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image processing, due to their massive parallel processing power. Many image processing applications use the histogramming algorithm, which fills a set of bins according to the frequency of occurrence of pixel values taken from an input image. Histogramming has been mapped on a GPU prior to this work. Although significant research effort has been spent in optimizing the mapping, we show that the performance and performance predictability of existing methods can still be improved. In this paper, we present two novel histogramming methods, both achieving a higher performance and predictability than existing methods. We discuss performance limitations for both novel methods by exploring algorithm trade-offs. Both the novel and the existing histogramming methods are evaluated for performance. The first novel method gives an average performance increase of 33% over existing methods for non-synthetic benchmarks. The second novel method gives an average performance increase of 56% over existing methods and guarantees to be fully data independent. While the second method is specifically designed for newer GPU architectures, the first method is also suitable for older architectures.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450305693
DOI	10.1145/1964179.1964181
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Histogram Image processing Parallel processing Gpu Cuda
Content Type	Text
Resource Type	Article

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