scholarly journals The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0189984 ◽  
Author(s):  
Mathew Schwartz ◽  
Philippe C. Dixon
2014 ◽  
Vol 444 (4) ◽  
pp. 3089-3117 ◽  
Author(s):  
Andreas Hiemer ◽  
Marco Barden ◽  
Lee S. Kelvin ◽  
Boris Häußler ◽  
Sabine Schindler

2021 ◽  
Vol 13 (21) ◽  
pp. 11782
Author(s):  
Taha Al-Jody ◽  
Hamza Aagela ◽  
Violeta Holmes

There is a tradition at our university for teaching and research in High Performance Computing (HPC) systems engineering. With exascale computing on the horizon and a shortage of HPC talent, there is a need for new specialists to secure the future of research computing. Whilst many institutions provide research computing training for users within their particular domain, few offer HPC engineering and infrastructure-related courses, making it difficult for students to acquire these skills. This paper outlines how and why we are training students in HPC systems engineering, including the technologies used in delivering this goal. We demonstrate the potential for a multi-tenant HPC system for education and research, using novel container and cloud-based architecture. This work is supported by our previously published work that uses the latest open-source technologies to create sustainable, fast and flexible turn-key HPC environments with secure access via an HPC portal. The proposed multi-tenant HPC resources can be deployed on a “bare metal” infrastructure or in the cloud. An evaluation of our activities over the last five years is given in terms of recruitment metrics, skills audit feedback from students, and research outputs enabled by the multi-tenant usage of the resource.


2014 ◽  
Vol 519-520 ◽  
pp. 85-89
Author(s):  
Xiang Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
Feng Zhang ◽  
Xiao Qi Xi ◽  
...  

To investigate the performance of acceleration technologies for FDK algorithm, two of the most common high-performance computing hardware, multi-core CPU and GPU, are involved in our experiment. Both runtime and accuracy are regarded as the standards to evaluate the performance of four different programming methods: OpenMP, GLSL, CUDA and OpenCL. All the methods are estimated with comparable optimization strategies. The experimental results show that GPU has higher efficiency than multi-core CPU for fast cone-beam reconstruction, meanwhile CUDA is the best choice for programming on the multi-processor featured GPU.


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