Performance Analysis and Tuning of Automatically Parallelized OpenMP Applications

Mustafa, Dheya; Aurangzeb, Aurangzeb; Eigenmann, Rudolf

doi:10.1007/978-3-642-21487-5_12

Cited by 14 publications

(10 citation statements)

References 13 publications

(15 reference statements)

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“…Recall a key challenge in doing so: Because of the interactions of compiler optimizations within and across program sections, the sheer number of possible optimization combinations would make a brute-force approach infeasible -leading to extremely long tuning times. This paper pursues an approach that exploits locality of optimization interactions [20] -distant program sections do not influence each others significantly, allowing substantial pruning of the number of optimization variants that need to be explored.…”

Section: Portable Tuning Systemmentioning

confidence: 99%

Portable section-level tuning of compiler parallelized applications

Mustafa¹,

Eigenmann²

2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Automatic parallelization of sequential programs combined with tuning is an alternative to manual parallelization. This method has the potential to substantially increase productivity and is thus of critical importance for exploiting the increased computational power of today's multicores. A key difficulty is that parallelizing compilers are generally unable to estimate the performance impact of an optimization on a whole program or a program section at compile time; hence, the ultimate performance decision today rests with the developer. Building an autotuning system to remedy this situation is not a trivial task. This work presents a portable empirical autotuning system that operates at program-section granularity and partitions the compiler options into groups that can be tuned independently.To our knowledge, this is the first approach delivering an autoparallelization system that ensures performance improvements for nearly all programs, eliminating the users' need to experiment with such tools to strive for highest application performance.

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Section: Portable Tuning Systemmentioning

confidence: 99%

Portable section-level tuning of compiler parallelized applications

Mustafa¹,

Eigenmann²

2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

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“…Hyper-parameters Tuning: The concept of auto-tuning OpenMP code is well-established [21][22][23][24], and as one can assume, the choice of each environment variable can greatly affect the performance of the code [25]. Consider for example the dynamic scheduling option: If the chosen chunk_size is too small, the resulting numerous work segments cause high overhead.…”

Section: Related Workmentioning

confidence: 99%

ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization

Mosseri

Alon

Harel

et al. 2020

Preprint

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Parallelization schemes are essential in order to exploit the full benefits of multi-core architectures, which have become widespread in recent years. In shared-memory architectures, the most comprehensive parallelization API is OpenMP. However, the introduction of correct and optimal OpenMP parallelization to applications is not always a simple task, due to common parallel shared-memory management pitfalls, architecture heterogeneity and the current necessity for human expertise in order to comprehend many fine details and abstract correlations. To ease this process, many automatic parallelization compilers were created over the last decade. [1] tested several source-to-source compilers and concluded that each has its advantages and disadvantages and no compiler is superior to all other compilers in all tests. This indicates that a fusion of the compilersâĂŹ best outputs under the best hyper-parameters for the current hardware setups can yield greater speedups. To create such a fusion, one should execute a computationally intensive hyper-parameter sweep, in which the performance of each option is estimated and the best option is chosen. We created a novel parallelization source-to-source multi-compiler named ComPar, which uses code segmentation-and-fusion with hyper-parameters tuning to achieve the best parallel code possible without any human intervention while maintaining the program's validity. In this paper we present ComPar and analyze its results on NAS and PolyBench benchmarks. We conclude that although the resources ComPar requires to produce parallel code are greater than other source-to-source parallelization compilers -as it depends on the number of parameters the user wishes to consider, and their combinations -ComPar achieves superior performance overall compared to the serial code version and other tested parallelization compilers. ComPar is publicly available at: https: //github.com/Scientific-Computing-Lab-NRCN/compar.

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“…The significance of loop transformations is described in earlier works . Loop transformation enhances coarse‐grain parallelism.…”

Section: Related Workmentioning

confidence: 99%

“…Imai claims that without any loop transformation, a program does not have much coarse‐grain parallelism between different loops. Recent research has therefore proposed automatic tuning techniques to be essential parts of the auto‐parallelization tools with the aim to overcome performance degradation. The studies mentioned above do not evaluate the effect of loop transformation techniques, such as tiling, fission, fusion, unrolling, and peeling on parallelization.…”

Section: Introductionmentioning

confidence: 99%

A study on popular auto‐parallelization frameworks

Prema

Nasre

Jehadeesan

et al. 2019

Concurrency and Computation

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We study five popular auto-parallelization frameworks (Cetus, Par4all, Rose, ICC, and Pluto) andcompare them qualitatively as well as quantitatively. All the frameworks primarily deal with loop parallelization but differ in the techniques used to identify parallelization opportunities. Due to this variance, various aspects, such as certain loop transformations, are supported only in a few frameworks. The frameworks exhibit varying abilities in handling loop-carried dependence and, therefore, achieve different amounts of speedup on widely used PolyBench and NAS parallel benchmarks. In particular, Intel C Compiler (ICC) fares as an overall good parallelizer. Our study also highlights the need for more sophisticated analyses, user-driven parallelization, and meta-auto-parallelizer that provides combined benefits of various frameworks. KEYWORDS loop-carried dependence, loop transformations, privatization, vectorization INTRODUCTIONQuest for performance has made multicore processors mainstream. It becomes vital for the programmers and algorithm developers to exploit these ubiquitous advanced architectures with parallel programming. Exploiting the potential of multicore processors through parallel programming is a significant challenge. Amid several approaches to tame this challenge, a promising and programmer-friendly approach is automatic parallelization. 1-3 Auto-parallelizers eliminate the need for a programmer to transform a sequential code into a parallel code, which is quite attractive.Source-to-source transformations with insertion of parallel directives are performed by auto-parallelizers, such as Cetus, 4-7 Par4all, 8,9 Pluto,10,11 Parallware, 12,13 Rose, 14,15 Intel C Compiler, 16 LLVM (Low Level Virtual Machine) Polly, 17 ParaWise, 18 ParaGraph, 19 SUIF (Stanford University Intermediate Format), 20-22 and Polaris. 23,24 Although these existing parallelizers offer considerable benefits, they still fall short of fully replacing the manual transformations. Several parallelizers do not utilize all the static information available, whereas several others fall short of modeling precision. It leads to either missed parallelism opportunities or unwanted parallelization of sequential codes. As a consequence, compared to the original sequential version, the auto-parallelized code may lead to parallelization overheads and may exhibit poorer performance. 25Earlier studies 26,27 of the parallelizing compilers using Perfect benchmarks performed a detailed analysis of the code restructuring techniques.The techniques include induction variable elimination, scalar expansion, forward substitution, strip mining, and loop interchange. The studies found that some of the programs showed improvements and that scalar expansion led to positive results. An effectiveness study on the Polaris compiler with OpenMP (Open Multi-Processing) 28 parallel code using Perfect benchmarks proclaimed a performance lag in small parallel loops. It also illustrated the importance of reduction operation, which resulted in a moderate (10%) performance...

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Performance Analysis and Tuning of Automatically Parallelized OpenMP Applications

Cited by 14 publications

References 13 publications

Portable section-level tuning of compiler parallelized applications

Portable section-level tuning of compiler parallelized applications

ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization

A study on popular auto‐parallelization frameworks

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