Auto-vectorization through code generation for stream processing applications

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Auto-vectorization through code generation for stream processing applications

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International Conference on Supercomputing archive
Proceedings of the 23rd international conference on Supercomputing table of contents

Yorktown Heights, NY, USA

POSTER SESSION: Posters table of contents

Pages 495-496

Year of Publication: 2009

ISBN:978-1-60558-498-0

Authors

Huayong Wang	IBM China Research Lab, Beijing, China
Henrique Andrade	IBM T. J. Watson Research Center, Hawthorne, NY, USA
Bugra Gedik	IBM T. J. Watson Research Center, Hawthorne, NY, USA
Kun-Lung Wu	IBM T. J. Watson Research Center, Hawthorne, NY, USA

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ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture

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ACM New York, NY, USA

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ABSTRACT

We describe language- and code generation-based approaches to providing access to architecture-specific vectorization support for high-performance data stream processing applications. We provide an experimental performance evaluation of several stream operators, contrasting our code generation approach with the native auto-vectorization support available in the GNU gcc and Intel icc compilers.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Bugra Gedik , Henrique Andrade , Kun-Lung Wu , Philip S. Yu , Myungcheol Doo, SPADE: the system s declarative stream processing engine, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 09-12, 2008, Vancouver, Canada [doi>10.1145/1376616.1376729]
	2	Intel C++ compiler user and reference guides. Intel Document number: 304968-022US, 2008.
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