Validation of Loop Parallelization and Loop Vectorization Transformations

Dutta, Sudakshina; Sarkar, Dipankar; Rawat, Arvind; Singh, Kulwant

doi:10.5220/0005869501950202

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article is an extension of our earlier work in the study of Dutta et al that has been accepted, but yet to be published. The current paper contains a more detailed description of the entire procedure of construction of DGs and checking equivalence of two programs with a more extensive experimental results section.…”

Section: Introductionmentioning

confidence: 76%

Validation of parallelizing transformations of sequential programs

Dutta

2017

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary Transformations for high‐performance superscalar, vector, and parallel processors maximize parallelism and memory locality. Often parallelizing compilers apply transformations, such as loop parallelization and loop vectorization, to convert a sequential array‐handling program into a parallel program. Validation of such transformations is extremely useful in the prevalent high‐performance computing environment. This paper proposes a novel algorithm for construction of the dependence graph of the generated parallel programs. These transformations are validated by checking equivalence of the dependence graphs of the original sequential program and the transformed parallel program using a standard algorithm reported in the literature. The above equivalence checker works even when the above parallelizing transformations are preceded by various enabling transformations except for loop collapsing transformation that changes the dimensions of the arrays. In the present paper, the scope of the equivalence checker has been expanded to handle this special case by informing it of the correspondence between the index spaces of the corresponding of input and output arrays in the sequential and the parallel programs. The proposed methods are implemented and tested against a set of available benchmark programs that are parallelized by the polyhedral auto‐parallelizer LooPo and the auto‐vectorizer Scout.

show abstract

Section: Introductionmentioning

confidence: 76%

Validation of parallelizing transformations of sequential programs

Dutta

2017

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, it would be an interesting future work to apply the normalization techniques proposed in [69], [70] on the DG based method proposed in [67], [68] to handle both recurrence and arithmetic transformations elegantly. In addition, the work presented in [79], [80] can be used prior to this method to handle loop vectorization and loop parallelization transformations. We further envision that the verification techniques discussed here are also effective in verifying transformations applied by compilers targeting deep learning applications.…”

Section: Discussionmentioning

confidence: 99%

A Quick Introduction to Functional Verification of Array-Intensive Programs

Banerjee,

Karfa

2019

Preprint

View full text Add to dashboard Cite

Array-intensive programs are often amenable to parallelization across many cores on a single machine as well as scaling across multiple machines and hence are well explored, especially in the domain of high-performance computing. These programs typically undergo loop transformations and arithmetic transformations in addition to parallelizing transformations. Although a lot of effort has been invested in improving parallelizing compilers, experienced programmers still resort to handoptimized transformations which is typically followed by careful tuning of the transformed program to finally obtain the optimized program. Therefore, it is critical to verify that the functional correctness of an original sequential program is not sacrificed during the process of optimization. In this paper, we cover important literature on functional verification of array-intensive programs which we believe can be a good starting point for one interested in this field.

show abstract

“…Translation validation techniques, [9,17,20,22,27,28,32,35,39], relate the source programs with their compiler outputs to check equivalence. However, it is usually the case that the compiler provides the manner of composition.…”

Section: Related Workmentioning

confidence: 99%

Lockstep Composition for Unbalanced Loops

Hamza

Fedyukovich

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Equivalence checking of two programs is often reduced to the safety verification of a so-called product program that aligns the programs in lockstep. However, this strategy is not applicable when programs have arbitrary loop structures, e.g., the numbers of loops vary. We introduce an automatic iterative abstraction-refinement-based technique for checking equivalence of a single-loop program and a program which has a series of consecutive loops. Our approach decomposes the single loop into a sequence of separate loops thus reducing the main problem to a series of equivalence-checking problems for pairs of loops. Since due to the decomposition, these problems become abstract, our approach iteratively refines the decomposed loops and lifts useful information across them. Our second contribution is a procedure for the alignment of loops with counters and explicit bounds that cannot be composed in lockstep. We have implemented the approach and successfully evaluated it on two suites, one with benchmarks containing different numbers of loops and the other containing benchmarks that need alignment.

show abstract

Validation of Loop Parallelization and Loop Vectorization Transformations

Cited by 6 publications

References 0 publications

Validation of parallelizing transformations of sequential programs

Validation of parallelizing transformations of sequential programs

A Quick Introduction to Functional Verification of Array-Intensive Programs

Lockstep Composition for Unbalanced Loops

Contact Info

Product

Resources

About