scholarly journals Computación de alto rendimiento para el procesamiento de imágenes digitales [High-performance computing for digital image processing]

2014 ◽  
Author(s):  
Manuel Bolaños González ◽  
José María Muñoz ◽  
Mario Montenegro
Keyword(s):  
Image Processing ◽  
Grid Computing ◽  
High Performance ◽  
Satellite Images ◽  
Virtual Machines ◽  
Heterogeneous Cluster ◽  
Computing Power ◽  
Palabras Clave ◽  
Tightly Coupled ◽  
Performance Computing

Resumen En este proyecto se planteó la integración de un prototipo de malla computacional con un clúster heterogéneo fuertemente acoplado, con el propósito de aprovechar la capacidad de computo de estas tecnologías en el procesamiento de imágenes digitales obtenidas a través de un satélite durante el monitoreo del volcán Galeras. En el desarrollo de la investigación se utilizaron cuatro computadores, en los que mediante máquinas virtuales se implementó un prototipo de malla computacional y un prototipo del clúster. Estas máquinas se comunican a través de uno de los servicios de la malla, permitiendo enviar información de una imagen digital al clúster para realizar el tratamiento y el proceso de renderización de la imagen en paralelo, posteriormente se regresa el archivo resultante a la malla computacional, así los usuarios especializados pueden tomar decisiones relacionadas con procesos de sismografía o verificación de la emisión de ozono entre otros. Palabras clave: Malla computacional; clúster; middleware; procesamiento de imágenes; renderización.   Abstract This project integrated a grid computing prototype with a tightly-coupled heterogeneous cluster, in order to leverage the computing power of these technologies in processing digital satellite images during the monitoring of the Galeras volcano. In the development of the research, four computers were used in which a prototype of grid computing and a cluster were implemented by using virtual machines. These machines of the cluster are communicating via one of the services of the grid, allowing delivering information digital from an image to the cluster for processing it and rendering it in parallel, then the resulting file is returned to the grid computing; thus, specialized users can make decisions related to processes or verification seismographic ozone emissions among others. Keywords: grid computing; cluster; middleware; image processing; rendering.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

2015 ◽  
Vol 6 (4) ◽  
pp. 65-81 ◽  
Author(s):  
Ouidad Achahbar ◽  
Mohamed Riduan Abid
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Virtual Machines ◽  
Performance Trends ◽  
Tightly Coupled ◽  
Physical Clusters ◽  
User Friendly ◽  
The Impact ◽  
Performance Computing

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

The Impact of Virtualization on High Performance Computing Clustering in the Cloud

Big Data ◽  
2016 ◽  
pp. 1687-1704
Author(s):  
Ouidad Achahbar ◽  
Mohamed Riduan Abid
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Virtual Machines ◽  
Performance Trends ◽  
Tightly Coupled ◽  
Physical Clusters ◽  
User Friendly ◽  
The Impact ◽  
Performance Computing

The ongoing pervasiveness of Internet access is intensively increasing Big Data production. This, in turn, increases demand on compute power to process this massive data, and thus rendering High Performance Computing (HPC) into a high solicited service. Based on the paradigm of providing computing as a utility, the Cloud is offering user-friendly infrastructures for processing Big Data, e.g., High Performance Computing as a Service (HPCaaS). Still, HPCaaS performance is tightly coupled with the underlying virtualization technique since the latter is responsible for the creation of virtual machines that carry out data processing jobs. In this paper, the authors evaluate the impact of virtualization on HPCaaS. They track HPC performance under different Cloud virtualization platforms, namely KVM and VMware-ESXi, and compare it against physical clusters. Each tested cluster provided different performance trends. Yet, the overall analysis of the findings proved that the selection of virtualization technology can lead to significant improvements when handling HPCaaS.

Hierarchical Load Balancing Model by Optimal Resource Utilization

2021 ◽  
pp. 150-164
Author(s):  
Jagdish Chandra Patni
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Resource Utilization ◽  
Resource Availability ◽  
High Performance ◽  
Low Cost ◽  
Original Form ◽  
Computing Paradigm ◽  
Optimal Resource ◽  
Performance Computing

Powerful computational capabilities and resource availability at a low cost is the utmost demand for high performance computing. The resources for computing can viewed as the edges of an interconnected grid. It can attain the capabilities of grid computing by balancing the load at various levels. Since the nature of resources are heterogeneous and distributed geographically, the grid computing paradigm in its original form cannot be used to meet the requirements, so it can use the capabilities of the cloud and other technologies to achieve the goal. Resource heterogeneity makes grid computing more dynamic and challenging. Therefore, in this article the problem of scalability, heterogeneity and adaptability of grid computing is discussed with a perspective of providing high computing, load balancing and availability of resources.

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 Towards Portable Large-Scale Image Processing with High-Performance Computing

2018 ◽  
Vol 31 (3) ◽  
pp. 304-314 ◽  
Author(s):  
Yuankai Huo ◽  
Justin Blaber ◽  
Stephen M. Damon ◽  
Brian D. Boyd ◽  
Shunxing Bao ◽  
...  
Keyword(s):  
Image Processing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Performance Computing

Direct Time Domain Simulations for a FPSO Tandem Offloading Operation

2019 ◽  
Author(s):  
Bonjun Koo ◽  
Manoj Jegannathan ◽  
Johyun Kyoung ◽  
Ho-Joon Lim
Keyword(s):  
Time Domain ◽  
High Performance ◽  
Flow Line ◽  
Time Windows ◽  
Economic Feasibility ◽  
Floating Platform ◽  
Computing Power ◽  
Field Development ◽  
Performance Computing ◽  
Operational Time

Abstract In this study, direct time domain offloading simulations are conducted without condensing the metocean data using High Performance Computing (HPC). With rapidly growing computing power, from increased CPU speeds and parallel processing capability, the direct time domain simulation for offloading analyses has become a practical option. For instance, 3-hour time domain simulations, covering the entire service life (e.g. 100,000 simulations for 35 years) of a floating platform, can now be conducted within a day. The simulation results provide realistic offloading operational time windows which consider both offloading operation sequence (i.e. berthing, connection, offloading duration and disconnection) and required criteria (i.e. relative responses, loads on hawser and flow line, etc.). The direct time domain offloading analyses improve the prediction of offloading operability, the sizing of the FPSO tank capacity, and the shuttle tanker selection. In addition, this method enables accurate evaluations of the economic feasibility for field development using FPSOs.

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