scholarly journals High Performance Computing and Real Time Software for High Dimensional Data Classification

2018 ◽  
Author(s):  
Zhaoyi Meng ◽  
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional ◽  
Performance Computing

scholarly journals Viability of Cloud Computing for Real-Time Numerical Weather Prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1985-1996 ◽  
Author(s):  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
Roland Stull
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
High Performance ◽  
Virtual Machines ◽  
Weather Prediction ◽  
Cloud Platform ◽  
Numerical Weather ◽  
Performance Computing

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

scholarly journals Timing Predictability in High-Performance Computing With Probabilistic Real-Time

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 208566-208582
Author(s):  
Federico Reghenzani ◽  
Giuseppe Massari ◽  
William Fornaciari
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
Performance Computing

scholarly journals The Role of High Performance Computing and Communication for Real-Time Biofeedback in Sport

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Anton Umek ◽  
Anton Kos
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
Tracking System ◽  
Optical Tracking ◽  
Local Processing ◽  
Special Focus ◽  
Dataflow Computing ◽  
Performance Computing

This paper studies the main technological challenges of real-time biofeedback in sport. We identified communication and processing as two main possible obstacles for high performance real-time biofeedback systems. We give special attention to the role of high performance computing with some details on possible usage of DataFlow computing paradigm. Motion tracking systems, in connection with the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Inertial sensor tracking system accuracy is tested in comparison with a high performance optical tracking system. Special focus is given on feedback loop delays. Real-time sensor signal acquisitions and real-time processing challenges, in connection with biomechanical biofeedback, are presented. Despite the fact that local processing requires less energy consumption than remote processing, many other limitations, most often the insufficient local processing power, can lead to distributed system as the only possible option. A multiuser signal processing in football match is recognised as an example for high performance application that needs high-speed communication and high performance remote computing. DataFlow computing is found as a good choice for real-time biofeedback systems with large data streams.

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