Branch elimination by condition merging

Kreahling, William; Whalley, David; Bailey, M. W.; Yuan, Xin; Uh, Gang-Ryung; Engelen, Robert van

doi:10.1002/spe.627

Cited by 10 publications

(2 citation statements)

References 14 publications

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“…When merging multiple variable conditions, the branches executed were reduced by an average of 11.54%. For space considerations the techniques that merge single variable conditions are not shown here but are discussed, in detail, in a technical report [13]. The average number of branches executed when using both techniques was reduced by 15.81% while the average number of instructions executed was reduced by 5.74%.…”

Section: Resultsmentioning

confidence: 99%

Branch Elimination via Multi-variable Condition Merging

Kreahling

Whalley

Bailey

et al. 2003

Euro-Par 2003 Parallel Processing

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Conditional branches are expensive. Branches require a significant percentage of execution cycles since they occur frequently and cause pipeline flushes when mispredicted. In addition, branches result in forks in the control flow, which can prevent other code-improving transformations from being applied. In this paper we describe profile-based techniques for replacing the execution of a set of two or more branches with a single branch on a conventional scalar processor. First, we gather profile information to detect the frequently executed paths in a program. Second, we detect sets of conditions in frequently executed paths that can be merged into a single condition. Third, we estimate the benefit of merging each set of conditions. Finally, we restructure the control flow to merge the sets that are deemed beneficial. The results show that eliminating branches by merging conditions can significantly reduce the number of conditional branches performed in non-numerical applications.

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Section: Resultsmentioning

confidence: 99%

Branch Elimination via Multi-variable Condition Merging

Kreahling

Whalley

Bailey

et al. 2003

Euro-Par 2003 Parallel Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…We can also use compilation tool to collect profile data to reorder the sequences of conditional branches of program so that the program executes faster [10]. In real-time system, in order to avoid unnecessary computations that occurred at different locations in a program, those original computations will be computed once and the subsequent computations could be replaced by variables or registers [5].…”

Section: Introductionmentioning

confidence: 99%

On Partitioning the Domain for Test Case Reusability (Short Paper)

Yang

Lau

Lin

2008

2008 the Eighth International Conference on Quality Software

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During the life time of software or even within the development stage, it is inevitable that the software needs to be modified. Test cases used before for the previous version could be reused in order to reduce the cost of regression testing. This paper proposes a regression testing methodology, called Partitioning of Domain Testing (PDT), to analyze the input domains of the previous version and the modified version so that test cases can be maximally reused. As a result, software test engineers can spend less effort in generating test cases to test the modified software. This methodology is different from traditional domain-based testing strategies in the sense that the partitioning is achieved by two supplementary perspectives, namely the specifications of the program and its testing criterion. First, the input domain is partitioned according to the specification and testing criterion of the program under test. Then, further refining methods are introduced to obtain 100% reusable test cases. We also illustrate the idea of PDT using a case study with 3 different testing criteria.

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Image Pre-Processing

Krig

2014

Computer Vision Metrics

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This chapter describes the methods used to prepare images for further analysis, including interest point and feature extraction. Some of these methods are also useful for global and local feature description, particularly the metrics derived from transforms and basis spaces. The focus is on image pre-processing for computer vision, so we do not cover the entire range of image processing topics applied to areas such as computational photography and photo enhancements, so we refer the interested reader to various other standard resources in Digital Image Processing and Signal Processing as we go along [4,9,325,326], and we also point out interesting research papers that will enhance understanding of the topics. Note ■ Readers with a strong background in image processing may benefit from a light reading of this chapter. Perspectives on Image Processing Image processing is a vast field that cannot be covered in a single chapter. So why do we discuss image pre-processing in a book about computer vision? The reason is to advance the science of local and global feature description, as image pre-processing is typically ignored in discussions of feature description. Some general image processing topics are covered here in light of feature description, intended to illustrate rather than to proscribe, as applications and image data will guide the image pre-processing stage. Some will argue that image pre-processing is not a good idea, since it distorts or changes the true nature of the raw data. However, intelligent use of image pre-processing can provide benefits and solve problems that ultimately lead to better local and global feature detection. We survey common methods for image enhancements and corrections that will affect feature analysis downstream in the vision pipeline in both favorable and unfavorable ways, depending on how the methods are employed.

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Branch elimination by condition merging

Cited by 10 publications

References 14 publications

Branch Elimination via Multi-variable Condition Merging

Branch Elimination via Multi-variable Condition Merging

On Partitioning the Domain for Test Case Reusability (Short Paper)

Image Pre-Processing

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