Prediction of high-performance computing input/output variability and its application to optimization for system configurations

Xu, Li; Lux, Thomas; Chang, Tyler H.; Li, Bo; Hong, Yili; Watson, Layne T.; Butt, Ali R.; Yao, Danfeng; Cameron, Kirk W.

doi:10.1080/08982112.2020.1866203

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…In extending this previous research, our innovation lies in auto-tuning those parameters on the basis of MPI-IO bandwidth prediction to the optimized values. Previous research shows significant benefits of ML based IO performance prediction and autotuning over different parameters settings at different environment experimental setups [19,20,21,23]. This has provided the motivation to apply similar approach to improve the MPI-IO application bandwidth performance, in this research scenario.…”

Section: Introductionmentioning

confidence: 87%

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Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system

2022

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Super-computing or HPC clusters are built to provide services to execute computationally complex applications. Generally, these HPC applications involve large scale IO (input/output) processing over the networked parallel file system disks. They are commonly developed on top of the C/C++ based MPI standard library. The HPC clusters MPI–IO performance significantly depends on the particular parameter value configurations, not generally considered when writing the algorithms or programs. Therefore, this leads to poor IO and overall program performance degradation. The IO is mostly left to individual practitioners to be optimised at code level. This usually leads to unexpected consequences due to IO bandwidth degradation which becomes inevitable as the file data scales in size to petabytes. To overcome the poor IO performance, this research paper presents an approach for auto-tuning of the configuration parameters by forecasting the MPI–IO bandwidth via artificial neural networks (ANNs), a machine learning (ML) technique. These parameters are related to MPI–IO library and lustre (parallel) file system. In addition to this, we have identified a number of common configurations out of numerous possibilities, selected in the auto-tuning process of READ/WRITE operations. These configurations caused an overall READ bandwidth improvement of 65.7% with almost 83% test cases improved. In addition, the overall WRITE bandwidth improved by 83% with number of test cases improved by almost 93%. This paper demonstrates that by using auto-tuning parameters via ANNs predictions, this can significantly impact overall IO bandwidth performance.

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Section: Introductionmentioning

confidence: 87%

“…In [19], the study demonstrates that parameters like the IO scheduler, number of IO threads and CPU frequency affects HPC-IO performance. The IO behaviour is predicted and determined upon these factors as parameters through extrapolation and interpolation techniques.…”

Section: Background and Related Researchmentioning

confidence: 99%

Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system

2022

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“…One further step after obtaining desirable designs can be determining the system configuration that optimizes the HPC performance. For example, in Xu et al (2021) work, the optimal system configuration is determined as the configuration that can minimize the HPC variability while maintaining the HPC performance (i.e., the computing speed). This can provide insights in choosing system configurations in real HPC applications.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Design Strategies and Approximation Methods for High-Performance Computing Variability Management

Wang¹,

Li²,

Hong³

et al. 2022

Preprint

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Problem: Performance variability management is an active research area in highperformance computing (HPC). In this paper, we focus on input/output (I/O) variability, which is a complicated function that is affected by many system factors. To study the performance variability, computer scientists often use grid-based designs (GBDs) which are equivalent to full factorial designs to collect I/O variability data, and use mathematical approximation methods to build a prediction model. Mathematical approximation models, as deterministic methods, could be biased particularly if extrapolations are needed. In statistics literature, space-filling designs (SFDs) and surrogate models such as Gaussian process (GP) are popular for data collection and building predictive models.The applicability of SFDs and surrogates in the HPC variability management setting, however, needs investigation. In this case study, we investigate their applicability in the HPC setting in terms of design efficiency, prediction accuracy, and scalability.Approach: We first customize the existing SFDs so that they can be applied in the HPC setting. We conduct a comprehensive investigation of design strategies and the prediction ability of approximation methods. We use both synthetic data simulated from three test functions and the real data from the HPC setting. We then compare different methods in terms of design efficiency, prediction accuracy, and scalability.Results: In our synthetic and real data analysis, GP with SFDs outperforms in most scenarios. With respect to the choice of approximation models, GP is recommended if

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“…Thus, we make comparisons with existing methods in predicting summary statistics in this section. Xu et al (2021) study the accuracy of predicting throughput standard deviations using multiple surrogates. For comparison, we have two baseline models which can incorporate both quantitative and qualitative factors.…”

Section: Predicting Summary Statistics and Comparisonsmentioning

confidence: 99%

Prediction for Distributional Outcomes in High-Performance Computing I/O Variability

Li¹,

Hong²,

Morris³

et al. 2022

Preprint

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Although high-performance computing (HPC) systems have been scaled to meet the exponentially-growing demand for scientific computing, HPC performance variability remains a major challenge and has become a critical research topic in computer science.Statistically, performance variability can be characterized by a distribution. Predicting performance variability is a critical step in HPC performance variability management and is nontrivial because one needs to predict a distribution function based on system factors. In this paper, we propose a new framework to predict performance distributions.The proposed model is a modified Gaussian process that can predict the distribution function of the input/output (I/O) throughput under a specific HPC system configuration. We also impose a monotonic constraint so that the predicted function is nondecreasing, which is a property of the cumulative distribution function. Additionally, the proposed model can incorporate both quantitative and qualitative input variables.We evaluate the performance of the proposed method by using the IOzone variability data based on various prediction tasks. Results show that the proposed method can generate accurate predictions, and outperform existing methods. We also show how the predicted functional output can be used to generate predictions for a scalar summary of the performance distribution, such as the mean, standard deviation, and quantiles. Our methods can be further used as a surrogate model for HPC system variability monitoring and optimization.

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Prediction of high-performance computing input/output variability and its application to optimization for system configurations

Cited by 12 publications

References 30 publications

Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system

Artificial neural networks based predictions towards the auto-tuning and optimization of parallel IO bandwidth in HPC system

Design Strategies and Approximation Methods for High-Performance Computing Variability Management

Prediction for Distributional Outcomes in High-Performance Computing I/O Variability

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