Cart (Loading....) | Create Account
Close category search window
 

Nonlinear Symbolic Analysis for Advanced Program Parallelization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kyriakopoulos, K. ; Dept. of Comput. Sci., Univ. of Texas at San Antonio, San Antonio, TX ; Psarris, K.

High-end parallel and multicore processors rely on compilers to perform the necessary optimizations and exploit concurrency in order to achieve higher performance. However, the source code for high-performance computers is extremely complex to analyze and optimize. In particular, program analysis techniques often do not take into account complex expressions during the data dependence analysis phase. Most data dependence tests are only able to analyze linear expressions, even though nonlinear expressions occur very often in practice. Therefore, considerable amounts of potential parallelism remain unexploited. In this paper, we propose new data dependence analysis techniques to handle such complex instances of the dependence problem and increase program parallelization. Our method is based on a set of polynomial-time techniques that can prove or disprove dependences in source codes with nonlinear and symbolic expressions, complex loop bounds, arrays with coupled subscripts, and if-statement constraints. In addition, our algorithm can produce accurate and complete direction vector information, enabling the compiler to apply further transformations. To validate our method, we performed an experimental evaluation and comparison against the I-Test, the Omega test, and the Range test in the Perfect and SPEC benchmarks. The experimental results indicate that our dependence analysis tool is accurate, efficient, and more effective in program parallelization than the other dependence tests. The improved parallelization results into higher speedups and better program execution performance in several benchmarks.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:20 ,  Issue: 5 )

Date of Publication:

May 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.