ComPar: Optimized Multi-compiler for Automatic OpenMP S2S Parallelization

Mosseri, Idan; Alon, Lee-or; Harel, Re'em; Oren, Gal

doi:10.1007/978-3-030-58144-2_16

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Cetus outperformed the other parallelizers in five out of the seven programs evaluated, with minimal user intervention. Similar experiments were performed by Mosseri et al [33] on a set of five benchmarks from the PolyBench benchmark [6] suite. The Cetus parallel code for each of the benchmarks could achieve substantial speedup over the serial versions, improving the performance by as much as 754%, on average.…”

Section: Evaluation Of Optimization Techniques In Cetusmentioning

confidence: 92%

“…BT, SP, LU and two benchmarks for unstructured computation -UA, DC. We used serial 350 versions of the benchmarks written in C [33] for our experiments and measured the 351 performance of the applications for input Class B, having a problem size that is neither for Doitgen and (d) Performance results for Gramschmidt benchmarks from the PolyBench benchmark suite v4.2. The selected base case is the best tuned version (all on).…”

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confidence: 99%

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Automatic and Interactive Program Parallelization Using the Cetus Source to Source Compiler Infrastructure v2.0

et al. 2022

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This paper presents an overview and evaluation of the existing and newly added analysis and transformation techniques in the Cetus source-to-source compiler infrastructure. Cetus is used for research on compiler optimizations for multi-cores with an emphasis on automatic parallelization. The compiler has gone through several iterations of benchmark studies and implementations of those techniques that could improve the parallel performance of these programs. This work seeks to measure the impact of the existing Cetus techniques on the newer versions of some of these benchmarks. In addition, we describe and evaluate the recent advances made in Cetus, which are the capability of analyzing subscripted subscripts and a feature for interactive parallelization. Cetus started as a class project in the 1990s and grew with support from Purdue University and from the National Science Foundation (NSF), as well as through countless volunteer projects by enthusiastic students. While many Version-1 releases were distributed via the Purdue download site, Version 2 is being readied for release from the University of Delaware.

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Section: Evaluation Of Optimization Techniques In Cetusmentioning

confidence: 92%

mentioning

confidence: 99%

Automatic and Interactive Program Parallelization Using the Cetus Source to Source Compiler Infrastructure v2.0

et al. 2022

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“…The second system we evaluate against is the state-of-the-art S2S compiler named ComPar [12]. ComPar is the most suitable for this task, as it incorporates several S2S compilers under the hood -Par4All [14], AutoPar [59] and Cetus [60] -and combines their results to produce the best OpenMP directive.…”

Section: Parallel Work-sharing Loop Construct Classificationmentioning

confidence: 99%

“…More importantly, they open up the possibility of using any standard tool -a standard compiler, a vendor-specific tool -to subsequently compile the parallelized code. Some examples of S2S tools are ComPar [12], Par4all [13,14], and Cetus [15]. Although automatic parallelization tools provide a solution for code parallelization, they have many pitfalls.…”

Section: Introductionmentioning

confidence: 99%

PragFormer: Data-driven Parallel Source Code Classification with Transformers

Harel,

Kadosh,

Hasabnis

et al. 2023

Preprint

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Multi-core shared memory architectures have become ubiquitous in computing hardware nowadays. As a result, there is a growing need to fully utilize these architectures by introducing appropriate parallelization schemes, such as OpenMP worksharing-loop constructs, to applications. However, most developers find introducing OpenMP directives to their code hard due to pervasive pitfalls in managing parallel shared memory. To assist developers in this process, many compilers, as well as source-to-source (S2S) translation tools, have been developed over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. Recently, many data-driven AI-based code completion (CC) tools, such as GitHub CoPilot, have been developed to ease and improve programming productivity. Leveraging the insights from existing AI-based programming-assistance tools, this work presents a novel AI model that can serve as a parallel-programming assistant∗. Specifically, our model, named PragFormer, is tasked with identifying for loops that can benefit from conversion to parallel worksharing-loop construct (OpenMP directive) and even predict the need for specific data-sharing attributes clauses on the fly. We created a unique database, named Open-OMP, specifically for this goal. Open-OMP contains over 32,000 unique code snippets from different domains, half of which contain OpenMP directives, while the other half do not. We experimented with different model design parameters for these tasks and showed that our best-performing model outperforms a statistically-trained baseline as well as a state-of-the-art S2S compiler. In fact, it even outperforms the popular generative AI model of ChatGPT. In the spirit of advancing research on this topic, we have already released source code for PragFormer as well as Open-OMP dataset to public†. Moreover, an interactive demo of our tool, as well as a Hugging Face webpage ‡to experiment with our tool, are already available.

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Learning from Automatically Versus Manually Parallelized NAS Benchmarks

Barakhshan

Eigenmann

2023

Lecture Notes in Computer Science

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ComPar: Optimized Multi-compiler for Automatic OpenMP S2S Parallelization

Cited by 9 publications

References 21 publications

Automatic and Interactive Program Parallelization Using the Cetus Source to Source Compiler Infrastructure v2.0

Automatic and Interactive Program Parallelization Using the Cetus Source to Source Compiler Infrastructure v2.0

PragFormer: Data-driven Parallel Source Code Classification with Transformers

Learning from Automatically Versus Manually Parallelized NAS Benchmarks

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