Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Rojek, Krzysztof

doi:10.1002/cpe.4644

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…The influence of vectorization combining with multi-threading on the energy efficiency of program executions is investigated in [24]. In [25], Rojek proposed a method that leverages the mixed precision arithmetic to reduce the energy consumption for applications executed in supercomputing centers.…”

Section: Related Workmentioning

confidence: 99%

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

Szustak

Wyrzykowski

Olas

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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This article provides a comprehensive study of the impact of performance optimizations on the energy efficiency of a real-world CFD application called MPDATA, as well as an insightful analysis of performance-energy interaction of these optimizations with the underlying hardware that represents the first generation of Intel Xeon Scalable processors. Considering the MPDATA iterative application as a use case, we explore the fundamentals of energy and performance analysis for a memory-bound application when exposed to a set of optimization steps that increase the application performance, by improving the operational intensity of code and utilizing resources more efficiently. It is shown that for memory-bound applications, optimizing toward high performance could be a powerful strategy for improving the energy efficiency as well. In fact, for the considered performance optimizations, the energy gain is correlated with the performance gain but with varying degrees. As a result, these optimizations allow improving both performance and energy consumption radically, up to about 10.9 and 8.8 times, respectively. The impact of the Intel AVX-512 SIMD extension on the energy consumption and performance is demonstrated. Also, we discover limitations on the usability of CPU frequency scaling as a tool for balancing energy savings with admissible performance losses.

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Section: Related Workmentioning

confidence: 99%

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

Szustak

Wyrzykowski

Olas

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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“…The second kernel is responsible for computing the psuedovelocity for the second pass of the upwind algorithm. It returns an approximation of the relative velocity and is required to provide the second-order-accurate advection (Rojek andWyrzykowski, 2017, Smolarkiewicz et al, 2014). The data dependency graph of this algorithm is shown in Figure 2.…”

Section: Pseudovelocitymentioning

confidence: 99%

CFD code adaptation to the FPGA architecture

Rojek¹,

Halbiniak²,

Kuczynski

2020

The International Journal of High Performance Computing Applica

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For the last years, we observe the intensive development of accelerated computing platforms. Although current trends indicate a well-established position of GPU devices in the HPC environment, FPGA (Field-Programmable Gate Array) aspires to be an alternative solution to offload the CPU computation. This paper presents a systematic adaptation of four various CFD (Computational Fluids Dynamic) kernels to the Xilinx Alveo U250 FPGA. The goal of this paper is to investigate the potential of the FPGA architecture as the future infrastructure able to provide the most complex numerical simulations in the area of fluid flow modeling. The selected kernels are customized to a real-scientific scenario, compatible with the EULAG (Eulerian/semi-Lagrangian) fluid solver. The solver is used to simulate thermo-fluid flows across a wide range of scales and is extensively used in numerical weather prediction. The proposed adaptation is focused on the analysis of the strengths and weaknesses of the FPGA accelerator, considering performance and energy efficiency. The proposed adaptation is compared with a CPU implementation that was strongly optimized to provide realistic and objective benchmarks. The performance results are compared with a set of server CPUs containing various Intel generations, including Intel SkyLake-based CPUs as Xeon Gold 6148 and Xeon Platinum 8168, as well as Intel Xeon E5-2695 CPU based on the IvyBridge architecture. Since all the kernels belong to the group of memory-bound algorithms, our main challenge is to saturate global memory bandwidth and provide data locality with the intensive BRAM (Block RAM) reusing. Our adaptation allows us to reduce the performance per watt up to 80% compared to the CPUs.

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“…20,21 Considering the existing problems for CMB detection. Furthermore, the above computer-aided CMB detection method though made some progress.…”

mentioning

confidence: 99%

“…However, accuracy, sensitivity, specificity, precision, and computation time still need to be improved, as traditional machine learning methods extracted the hand-crafted features, which were lack of robustness. 20,21 Considering the existing problems for CMB detection. In this paper, we proposed using CNN with stochastic pooling for the CMB detection as CNN provided a very effective way for image processing via sharing weights among the layer.…”

mentioning

confidence: 99%

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Wang

Sun

Mehmood

et al. 2019

Concurrency and Computation

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Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. KEYWORDScerebral micro-bleeding, convolution neural network, detection, intra-cerebral hemorrhage, stochastic pooling, stroke INTRODUCTIONCerebral micro-bleedings (CMBs) are small chronic brain hemorrhages, which can be caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke and memory clinics that is initially set up for research purposes. 1 CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. 2 Previous studies show that, in a range of 18% to 68%, patients with ischemic stroke/transient ischemic attack have a high prevalence of CMBs. There are 23% of prevalence in patients with first-ever ischemic stroke and 44% in patients with recurrent ischemic stroke reported by a previous systematic review. 3 The strong relationship with CMBs and increased risk of ICH suggests that the antithrombotic drugs Concurrency Computat Pract Exper. 2020;32:e5130. wileyonlinelibrary.com/journal/cpe to test the performance of the proposed structure of CNN based on the stochastic pooling, we compared different pooling methods, including average pooling, max pooling, and stochastic pooling. Meanwhile, we compared CNN with different convolution number and the state-of-the-art method for CMB detection. The experiment result shows that our designed structure of CNN based on the stochastic pooling achieved the best performance in sensitivity as 97.22%, specificity as 97.35%, accuracy as 97.28%, and precision as 97.35%.The limitation of our work is that we may need to move the window from top to bottom and from left to right. This is time-consuming. It is better to use concurrent convolution in the future studies, and we may need to replace the fully connected layers with conv layers. Another limitation is that we have two labels (CMB and ...

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Machine learning method for energy reduction by utilizing dynamic mixed precision on GPU‐based supercomputers

Cited by 11 publications

References 32 publications

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

Correlation of Performance Optimizations and Energy Consumption for Stencil-Based Application on Intel Xeon Scalable Processors

CFD code adaptation to the FPGA architecture

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

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