Identifying pitfalls in automatic parallelization of NAS parallel benchmarks

Prema, S.; Jehadeesan, R.; Panigrahi, B. K.

doi:10.1109/parcomptech.2017.8068329

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…However, the emergence of the open-source Par4All automatic parallelization platform has improved the practicality of automatic parallelization to a new level. [18][19][20] Although Par4All implements the automatic parallelization of an algorithm, it has been found through experiments that Par4All's automatic conversion process takes a long time; meanwhile, according to our experiments in this study, some generated RS image-enhancement automatic parallel algorithms were only minimally accelerated. In order to resolve these problems, it is particularly important to study automatic parallel optimization models.…”

mentioning

confidence: 79%

Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi

Huang

Yang

Jian

et al. 2021

Concurrency and Computation

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In unmanned aerial vehicle (UAV) image-processing applications, one needs to implement different parallel image-enhancement algorithms on several high-performance computing platforms utilizing various programming models. To speed up the parallelization procedure and improve its efficiency, the automatic parallel software package, Par4All, is applied in this work. We find that the performance of the original automatic parallelization algorithm produced with Par4All is inefficient. To resolve this problem, we propose different optimization approaches for Par4All based on Intel®'s Xeon Phi high-performance computing platform that are based on the structural features of the image-enhancement algorithms, which can further optimize the original parallel algorithm. These approaches mainly include: (1) Par4All automatic parallel search module optimization, (2) dynamic thread setting optimization, and (3) the collaborative parallelization of both CPU and many integrated core (MIC) processors.According to the results of the comparison experiments involving different algorithms, it is shown that the proposed optimization approaches for these kinds of algorithms can significantly improve the performance of automatic parallel algorithms. The acceleration ratio increases approximately by 30%, 70%, and 80% for the multiscale Retinex, Gaussian-filtering and median-filtering algorithms, respectively. As continuation and deepening of our previous research work, this research has the potential to be beneficial for other researchers in image-processing applications with image-enhancement algorithms. K E Y W O R D Sautomatic parallelism, image-enhancement algorithms, Par4All, Intel® Xeon Phi, unmanned aerial vehicles INTRODUCTIONAs mentioned by References 1, it is known that some image-enhancement algorithms, for example, median filtering, Gaussian filtering, wavelet transform, and the multiscale Retinex (MSR) algorithm, are used when preprocessing large amounts of unmanned aerial vehicle (UAV) remote-sensing (RS) images to solve the problems of poor clarity, insufficient contrast and low adaptability. In addition, many-cores computing platforms, e.g., graphics processing units (GPUs) and many integrate cores (MICs), have been involved in the development of some parallel algorithms to speed up the processing times 2 ; such approaches are expected to become mainstream in the near future. Experimental results have shown that parallelized algorithms can significantly improve computation speed and provide better acceleration efficiency. [3][4][5] Usually, there are different kinds of serial

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confidence: 79%

Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi

Huang

Yang

Jian

et al. 2021

Concurrency and Computation

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“…For shared memory applications, compiler capability such as OpenMP is not efficient to parallelize complex applications automatically due to dependencies either in data or in tasks. For distributed memory applications, there are no tools yet developed for auto-parallelization [7]. Furthermore, combining both parallel processing paradigms (shared-memory and distributed-memory) is extremely challenging for auto parallel tools.…”

Section: Cloud Computing Encompasses Computer Network and Virtualizat...mentioning

confidence: 99%

Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

et al. 2020

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The main goal of oil reservoir management is to provide more efficient, price-effective and environmentally more secure oil production. Oil production management includes an accurate characterization of the reservoir and strategies that involve interactions between reservoir data and human assessment. Hence, it is important to graphically visualize and handle massive data sets of oil and gas pressure / saturation levels to help decision makers in statistical analysis, history matching and recovery of hydrocarbons of the reservoir. In this article, we experimentally study the parallelization of intensive computation for a 3-D (three dimensional) oil reservoir data visualization tool. For this tool, we develop and implement a transformation and lighting model to visualize and react with the grid. Herein, we propose a hybrid (shared memory and distributed memory) parallelization technique to adapt with the data processing scalability. We tested these implementations on OpenStack Cloud Virtual Cluster. Our results indicate that although the virtual platform adds overhead for running parallel implementations, utilizing knowledge of the VM location on the compute host and network traffic among VMs to deploy the virtual environment can achieve significant performance enhancements. Hybrid parallel implementation using large data size can achieve 70× speedup over serial execution without owning a costly HPC infrastructure as the conventional parallel processing deployment model.INDEX TERMS Cloud computing, data visualization tool, HPC, hybrid (distributed/shared)-memoryparallel programming, MPI, multi-threading, OpenStack.

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“…S2S Automatic Parallelization Compilers: S. Prema et al [9] compared several automatic parallelization compilers (not necessarily S2S) including Cetus [10], Par4All [11], Pluto [12], Parallware [13,14], ROSE [15,16], and ICC [17]. They discussed the different aspects of the compilers' work fashions and showed their speedups and points of failure on ten NAS Parallel Benchmarks [18] using the Gprof performance analysis tool [19].…”

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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Identifying pitfalls in automatic parallelization of NAS parallel benchmarks

Cited by 12 publications

References 18 publications

Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi

Preliminary study on the automatic parallelism optimization model for image enhancement algorithms based on Intel's® Xeon Phi

Deployment and Analysis of a Hybrid Shared/Distributed-Memory Parallel Visualization Tool for 3-D Oil Reservoir Grid on OpenStack Cloud Computing

ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization

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