Performance and Energy Efficiency Evaluation for HPC Applications in Heterogeneous Architectures

Klôh, Vinícius; Yokoyama, Daniel; Yokoyama, André; Silva, Gabrieli; Ferro, Mariza; Schulze, Bruno

doi:10.1109/wscad.2018.00034

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The results show that Jetson TX1 has worse time-cost performance compared to Jetson TK1 systems, this is because that Jetson TX1 has lower operating core clock frequency and lower instructions-per-cycle of Jetson TX1's GPU on some compute-intensive applications compared with Jetson TK1. This work [25] also studied the performance analysis with considering energy efficiency. In this study, two two matrices are used to measure energy consumption.…”

Section: F Related Workmentioning

confidence: 99%

“…In this work, the performance metrics we use are mainly referred to the study [25] which uses two metrics to measure the energy consumption, they are the idle power, denoted as P idle , and the processing power, denoted as, P processing , respectively. The idle power indicates the consumed power when the edge device runs all the system functions in the background.…”

Section: B Data Collection and Visualizationmentioning

confidence: 99%

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Implementation of a Cluster-Based Heterogeneous Edge Computing System for Resource Monitoring and Performance Evaluation

et al. 2022

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In the past decade, Internet of Things (IoT) technology has been widely used in various applications in people's daily life. Currently, IoT applications mainly depend on the powerful cloud datacenters as the computing and storage centers. However, with such cloud-centric frameworks, a large amount of data will be transferred between the end devices and the remote cloud datacenters via a long wide-area network, which may potentially result in intolerable latency and a lot of energy consumption. To alleviate this problem, the edge computing (EC) paradigm is exploited to sink the cloud computing capability from the network core to network edges in proximity to end devices, so as to enable computationintensive and latency-critical edge intelligence applications to be executed in a real-time manner. With the increasing amount of edge devices, it is essential to obtain the status of devices in real-time for realizing the overall resources of heterogeneous edge devices. Thus, it is important to construct a system which can be used to monitor each device's status and obtain the performance of each device. In this study, a cluster-based heterogeneous edge computing environment is implemented for resource monitoring and performance evaluation. In the experiment, three deep learning models of object detection are used to evaluate the performance of the implemented system. Through the experimental results, we can easily realize the resource usage, including the high range dynamics of electricity and power consumption of heterogeneous edge devices via the visualization results.INDEX TERMS Edge computing, resource monitoring, Kubernetes, Prometheus, Grafana.

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Section: F Related Workmentioning

confidence: 99%

Section: B Data Collection and Visualizationmentioning

confidence: 99%

Implementation of a Cluster-Based Heterogeneous Edge Computing System for Resource Monitoring and Performance Evaluation

et al. 2022

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“…Dong et al [14] analyze the energy efficiency of GPU BLAST which simulates compressible hydrodynamics using finite elements with CUDA and report a 2.5 times speedup and a 42% increase in energy efficiency. Klôh [15] report that there is a wide spread in terms of energy efficiency and performance when comparing 3D wave propagation and full waveform inversion on two different architectures. They compare an Intel Xeon coupled with an ARM-based Nvidia Jetson TX2 GPU module, and find that the Xeon platform performs best in terms of computational speed, whilst the Jetson platform is most energy efficient.…”

Section: Related Workmentioning

confidence: 99%

Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing Using Python

Holm

Brodtkorb

Sætra

2020

Parallel Computing: Technology Trends

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In this work, we examine the performance and energy efficiency when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between GPU generations; and between low-end, mid-range and high-end GPUs. Our findings show that for some combinations of GPU and GPU code, there is a significant speedup for CUDA over OpenCL, but that this does not hold in general. Our experiments show that performance in general varies more between different GPUs, than between using CUDA and OpenCL. Finally, we show that tuning for performance is a good way of tuning for energy efficiency.

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“…Khare et al [24] proposed HCud-aBLAST for protein sequencing in 2017, which actually refines and encapsulates Hadoop and CUDA without high acceleration ratio. Kloh et al [25] compared the performance of X86 architecture, ARM architecture and Tesla architecture in three standard test sets and two real applications. By comparing and analyzing the medium-sized HPC system and mobile-based Jetson TX2 development board, it shows that a deep understanding of the application background, underlying framework and programming model is essential for performance and energy consumption.…”

Section: Free Beam or Cantilever Beam Modeling Is Often Used To Analyzementioning

confidence: 99%

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

2019

IEEE Access

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Modern aircraft such as missile and rocket, due to the large slenderness ratio of slender body vehicles, the influence of elastic deformation and vibration on navigation, guidance, and engine modules in simulation can not be ignored. For the problems of slow calculation speed and incapability of real-time simulation for time-domain simulation, by analyzing the time proportion of each calculation step under different computing scale, the dynamic parallel construction of octree is used to represent the aerodynamic parameter table under the environment of single and multi GPU. Meanwhile, an innovative parallel algorithm of element stiffness matrix based on finite element model is designed in GPU architecture. Accordingly, the optimized performance is enhanced through the adaptive hardware resources and rational use of shared memory. Furthermore, A multi-threaded asynchronous framework based on task queue and thread pool is proposed to realize the parallel task calculation with different granularities. The numerical result shows that the acceleration ratio of about 20 times in the single GPU condition can be obtained, and the acceleration ratio of at least 30 times can be obtained by the parallel computing of dual GPUs, enabling the real-time simulation of the flexible aircraft with 1200 elements within 20ms. INDEX TERMS Flexible aircraft, parallel computing, parallel algorithms, heterogeneous computing, Octree.

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Performance and Energy Efficiency Evaluation for HPC Applications in Heterogeneous Architectures

Cited by 6 publications

References 19 publications

Implementation of a Cluster-Based Heterogeneous Edge Computing System for Resource Monitoring and Performance Evaluation

Implementation of a Cluster-Based Heterogeneous Edge Computing System for Resource Monitoring and Performance Evaluation

Performance and Energy Efficiency of CUDA and OpenCL for GPU Computing Using Python

Real-Time Simulation and Optimization of Elastic Aircraft Vehicle Based on Multi-GPU Workstation

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