Automatic Parallel Pattern Detection in the Algorithm Structure Design Space

Huda, Zia Ul; Atre, Rohit; Jannesari, Ali; Wolf, Felix

doi:10.1109/ipdps.2016.60

Cited by 15 publications

(13 citation statements)

References 22 publications

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“…Selection of parallel patterns depends upon problem, dependency, parallel programming environment and architecture. In [19] Automatic parallel pattern detection for algorithmic space is proposed. These types of tools are very limited in their functionality.…”

Section: Master Workermentioning

confidence: 99%

Applying Parallel Design Patterns on Molecular Dynamics Simulation

Maltare¹,

Vithal²

2019

IJCA

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Molecular dynamics simulates behavior of atoms and molecules. Molecular dynamics Simulation demonstrates and derives macroscopic properties by atomic interactions.In this experiment we have redesigned Molecular Dynamics (MD) Simulation by applying different parallel design pattern. Redesigned MD Simulation focuses on adaptability to different architectures and scalability to use with large number of atoms and long duration simulation. The paper demonstrates performance of MD on different architectures.

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Section: Master Workermentioning

confidence: 99%

Applying Parallel Design Patterns on Molecular Dynamics Simulation

Maltare¹,

Vithal²

2019

IJCA

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“…The output is a prioritized list of parallelization opportunities [6]. In a next step, we identify suitable parallel design patterns to support the parallel algorithm structure [7,8].…”

Section: Contribution Summarymentioning

confidence: 99%

“…We propose an approach that automatically finds parallel patterns in the algorithm structure design space of sequential applications using template matching [7,8]. The approach generates a pattern vector, which plays the role of the template to be matched to the program.…”

Section: Parallel Pattern Identificationmentioning

confidence: 99%

Advances in Engineering Software for Multicore Systems

Jannesari¹

2018

Dependability Engineering

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The vast amounts of data to be processed by today's applications demand higher computational power. To meet application requirements and achieve reasonable application performance, it becomes increasingly profitable, or even necessary, to exploit any available hardware parallelism. For both new and legacy applications, successful parallelization is often subject to high cost and price. This chapter proposes a set of methods that employ an optimistic semi-automatic approach, which enables programmers to exploit parallelism on modern hardware architectures. It provides a set of methods, including an LLVM-based tool, to help programmers identify the most promising parallelization targets and understand the key types of parallelism. The approach reduces the manual effort needed for parallelization. A contribution of this work is an efficient profiling method to determine the control and data dependences for performing parallelism discovery or other types of code analysis. Another contribution is a method for detecting code sections where parallel design patterns might be applicable and suggesting relevant code transformations. Our approach efficiently reports detailed runtime data dependences. It accurately identifies opportunities for parallelism and the appropriate type of parallelism to use as task-based or loop-based.

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“…In our previous work, an overview of our toolset DiscoPoP is provided, which includes the evaluation of the dynamic profiler with respect to its performance and memory consumption. In addition, our previous work also discusses the use of CUs to identify parallel patterns in existing sequential code that is discussed in greater detail in our other works . In our previous works, we focus on task parallelism within larger computations and functions, and identification of CUs using a different method with Use‐Definition Chain (UD Chain).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, our previous work 15 also discusses the use of CUs to identify parallel patterns in existing sequential code that is discussed in greater detail in our other works. 18,19 In our previous works, 20,21 we focus on task parallelism within larger computations and functions, and identification of CUs using a different method with Use-Definition Chain (UD Chain).…”

mentioning

confidence: 99%

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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Automatic Parallel Pattern Detection in the Algorithm Structure Design Space

Cited by 15 publications

References 22 publications

Applying Parallel Design Patterns on Molecular Dynamics Simulation

Applying Parallel Design Patterns on Molecular Dynamics Simulation

Advances in Engineering Software for Multicore Systems

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

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