Real-Time Analytics Using High-Performance Computing

2015 ◽  
pp. 161-185
Author(s):  
Pethuru Raj ◽  
Anupama Raman ◽  
Dhivya Nagaraj ◽  
Siddhartha Duggirala
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
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.

scholarly journals A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

2020 ◽  
Vol 16 (8) ◽  
pp. 155014772093275 ◽  
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Wali Khan Mashwani ◽  
Samir Brahim Belhaouari ◽  
...  
Keyword(s):  
Resource Allocation ◽  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
System Size ◽  
Operational Environment ◽  
Computing Systems ◽  
The Real ◽  
Resource Allocation Schemes ◽  
Performance Computing

An efficient resource allocation scheme plays a vital role in scheduling applications on high-performance computing resources in order to achieve desired level of service. The major part of the existing literature on resource allocation is covered by the real-time services having timing constraints as primary parameter. Resource allocation schemes for the real-time services have been designed with various architectures (static, dynamic, centralized, or distributed) and quality of service criteria (cost efficiency, completion time minimization, energy efficiency, and memory optimization). In this analysis, numerous resource allocation schemes for real-time services in various high-performance computing (distributed and non-distributed) domains have been studied and compared on the basis of common parameters such as application type, operational environment, optimization goal, architecture, system size, resource type, optimality, simulation tool, comparison technique, and input data. The basic aim of this study is to provide a consolidated platform to the researchers working on scheduling and allocating high-performance computing resources to the real-time services. This work comprehensively discusses, integrates, analysis, and categorizes all resource allocation schemes for real-time services into five high-performance computing classes: grid, cloud, edge, fog, and multicore computing systems. The workflow representations of the studied schemes help the readers in understanding basic working and architectures of these mechanisms in order to investigate further research gaps.

Gisola: A High-Performance Computing Application for Real-Time Moment Tensor Inversion

2021 ◽  
Author(s):  
Nikolaos Triantafyllis ◽  
Ioannis E. Venetis ◽  
Ioannis Fountoulakis ◽  
Erion-Vasilis Pikoulis ◽  
Efthimios Sokos ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Moment Tensor ◽  
Time Moment ◽  
Parallel Execution ◽  
Waveform Data ◽  
Notification System ◽  
Performance Computing

Abstract Automatic moment tensor (MT) determination is essential for real-time seismological applications. In this article, Gisola, a highly evolved software for MT determination, oriented toward high-performance computing, is presented. The program employs enhanced algorithms for waveform data selection via quality metrics, such as signal-to-noise ratio, waveform clipping, data and metadata inconsistency, long-period disturbances, and station evaluation based on power spectral density measurements in parallel execution. The inversion code, derived from ISOLated Asperities—an extensively used manual MT retrieval utility—has been improved by exploiting the performance efficiency of multiprocessing on the CPU and GPU. Gisola offers the ability for a 4D spatiotemporal adjustable MT grid search and multiple data resources interconnection to the International Federation of Digital Seismograph Networks Web Services (FDSNWS), the SeedLink protocol, and the SeisComP Data Structure standard. The new software publishes its results in various formats such as QuakeML and SC3ML, includes a website suite for MT solutions review, an e-mail notification system, and an integrated FDSNWS-event for MT solutions distribution. Moreover, it supports the ability to apply user-defined scripts, such as dispatching the MT solution to SeisComP. The operator has full control of all calculation aspects with an extensive and adjustable configuration. MT’s quality performance, for 531 manual MT solutions in Greece between 2012 and 2021, was measured and proved to be highly efficient.

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