Kremlin

Garcia, Saturnino; Jeon, Dong‐Hwan; Louie, Christopher; Taylor, Michael

doi:10.1145/1993498.1993553

Cited by 61 publications

(4 citation statements)

References 41 publications

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“…While Hayashi et al [2008] use meta-patterns to infer the sequential patterns underlying the design of a program, Tsantalis et al [2006] use matrix similarity algorithms on adjacency matrices. However, Dong et al proved that the approach of Tsantalis et al is unable to distinguish between multiple design-pattern instances in the program's design.…”

Section: Related Workmentioning

confidence: 99%

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Using Template Matching to Infer Parallel Design Patterns

Huda

Jannesari

Wolf

2015

ACM Trans. Archit. Code Optim.

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The triumphant spread of multicore processors over the past decade increases the pressure on software developers to exploit the growing amount of parallelism available in the hardware. However, writing parallel programs is generally challenging. For sequential programs, the formulation of design patterns marked a turning point in software development, boosting programmer productivity and leading to more reusable and maintainable code. While the literature is now also reporting a rising number of parallel design patterns, programmers confronted with the task of parallelizing an existing sequential program still struggle with the question of which parallel pattern to apply where in their code. In this article, we show how template matching, a technique traditionally used in the discovery of sequential design patterns, can also be used to support parallelization decisions. After looking for matches in a previously extracted dynamic dependence graph, we classify code blocks of the input program according to the structure of the parallel patterns we find. Based on this information, the programmer can easily implement the detected pattern and create a parallel version of his or her program. We tested our approach with six programs, in which we successfully detected pipeline and do-all patterns.

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Section: Related Workmentioning

confidence: 99%

“…Many useful techniques have been proposed to infer the design patterns underlying already existing sequential applications [Dong et al 2008;Hayashi et al 2008;Gupta et al 2011]. One of them, template matching [Dong et al 2008], identifies design patterns by mapping a UML diagram of the pattern onto the UML diagram of the target software.…”

Section: Introductionmentioning

confidence: 99%

Using Template Matching to Infer Parallel Design Patterns

Huda

Jannesari

Wolf

2015

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

show abstract

“…3 Therefore, most of program analysis tools have been constructed based on data-dependence analysis. There are three kinds of tools: data dependence profilers, 4,5 semi-automatic parallelism discovery tools 6,7 and automatic parallelization tools. [8][9][10] However, current parallelism discovery tools suffers from issues such as high time consumption and conservativeness.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10] However, current parallelism discovery tools suffers from issues such as high time consumption and conservativeness. For example, some tools 6,9 perform program analysis based on purely static methods, which makes their analysis results conservative. To avoid the problem of conservativeness caused by purely static methods, data-dependence profilers 5 and semi-automatic tools 7,11 that combine static and dynamic methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Multigraph learning for parallelism discovery in sequential programs

Shen

Peng

et al. 2023

Concurrency and Computation

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Summary Parallelization is an optimization technique that is playing an increasingly vital role in software applications. Discovering the potential parallelism in sequential programs is of primary importance for parallelization. To this end, various tools have been created to obtain parallelizable targets by analyzing the dependencies in sequential programs. However, the majority are constructed based on manually designed dependence analysis rules. Consequently, they must follow some constraints, which limit the scope of their program analysis capability. Furthermore, constructing a dependency analysis rule is a complicated and highly sophisticated endeavor. In this study, we exploit the fact that neural network models can learn the potential features of programs to tackle the parallelism discovery task in an end‐to‐end manner. Specifically, we developed a multigraph learning architecture on top of multiple abstract code representations in a complementary manner. In the architecture, we equip code representations with different neural network models in a targeted manner, including a deep convolutional neural network for analyzing both control and data flows and a novel neural network for learning the abstract syntax trees. Experimental results obtained on a common parallelism discovery dataset indicate that the developed multigraph learning architecture can learn potential parallelism patterns in sequential programs with high accuracy and efficiency.

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Dissecting sequential programs for parallelization—An approach based on computational units

Atre

Ul‐Huda

Wolf

et al. 2018

Concurrency and Computation

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Summary When trying to parallelize a sequential program, programmers routinely struggle during the first step: finding out which code sections can be made to run in parallel. While identifying such code sections, most of the current parallelism discovery techniques focus on specific language constructs. In contrast, we propose to concentrate on the computations performed by a program. In our approach, a program is treated as a collection of computations communicating with one another using a number of variables. Each computation is represented as a computational unit (CU). A CU contains the inputs and outputs of a computation, and the three phases of a computation are read, compute, and write. Based on the notion of CU, which ensures that the read phase executes before the write phase, we present a unified framework to identify both loop parallelism and task parallelism in sequential programs. We conducted a range of experiments on 23 applications from four different benchmark suites. Our approach accurately identified the parallelization opportunities in benchmark applications based on comparison with their parallel versions. We have also parallelized the opportunities identified by our approach that were not implemented in the parallel versions of the benchmarks and reported the speedup.

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Kremlin

Cited by 61 publications

References 41 publications

Using Template Matching to Infer Parallel Design Patterns

Using Template Matching to Infer Parallel Design Patterns

Multigraph learning for parallelism discovery in sequential programs

Dissecting sequential programs for parallelization—An approach based on computational units

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