NDLI: Profile guided optimization for dataflow predication

Content Provider	IEEE Xplore Digital Library
Author	Li Wang Hong An Yongqing Ren Yaobin Wang
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei (Li Wang; Hong An; Yongqing Ren; Yaobin Wang)
Abstract	Dataflow predication provides a lightweight full support for predicated execution in dataflow-like architectures. One of its major overhead is the large amounts of fanout trees for distributing predicates to all dependant instructions. Conventional optimizations are predicating only the heads or tails of dataflow chains. Predicating tails offers more speculation but leads to resource contentions and power consumption increasing. Predicating heads is power efficient but reduces speculation and instruction level parallelism. This paper introduces a profile guided technique to combine these optimizations. It uses profiling feedback to guide the compiler in deciding to predicate at the head or tail. By predicating tails on hot paths and predicating heads on infrequent paths, this technique can get performance, power and resource efficiency. Performance evaluation result shows that profile guided optimization performs better in removing fanout trees. It has 10.6% speedup over always predicating heads and 2.5% speedup over always predicating tails in performance.
Starting Page	1
Ending Page	8
File Size	619380
Page Count	8
File Format	PDF
ISBN	9781424426829
DOI	10.1109/APCSAC.2008.4625471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article

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