NDLI: Optimizing techniques for saturated arithmetic with first-order linear recurrence

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Optimizing techniques for saturated arithmetic with first-order linear recurrence

Content Provider	ACM Digital Library
Author	Zang, Binyu Liu, Lili Zhang, Hongjiong Zhu, Chuanqi Zhang, Weihua Zhang, Chen
Abstract	Saturated arithmetic is a typical operation in multimedia applications, most multimedia extensions in the instruction set architecture (ISA) of modern processors provide saturation instructions for such operation. Therefore, extensive researches have focused on how to utilize saturation instructions to optimize programs. Previous algorithms mainly focus on purely saturated arithmetic, however saturated arithmetic is often mingled with first-order linear recurrence (FOLR) in real life applications. When FLOR pattern appears in the program, previous algorithms can not identify the saturated arithmetic as well. In fact, the saturated arithmetic with FOLR (SAWF) is a new and significant pattern, especially, SAWF with one as coefficient is frequently used in multimedia applications. Hence, it is necessary to explore a method with which such pattern can be efficiently vectorized. This paper discusses how to vectorize SAWF, explores the efficient method to vectorize SAWF with one as coefficient and gives its evaluation and implement a library for the optimizing technique. Such an implementation manner can make compilers are able to exploit it more easily. The experimental results shows the optimizing technique can achieve a speedup of 1.19 to 1.46 on Pentium IV processor. At the same time, the optimizing techniques in this paper can also be used to develop a library for SAWF so a programmer can benefit even without changing the compiler.
Starting Page	1883
Ending Page	1889
Page Count	7
File Format	PDF
ISBN	9781605581668
DOI	10.1145/1529282.1529704
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-03-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sawf Simd Folr Optimization
Content Type	Text
Resource Type	Article

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