scholarly journals Developing Virtual Clusters for High Performance Computing Using OpenNebula

2020 ◽  
Author(s):  
Jason St. John ◽  
Thomas Hacker
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Virtual Clusters ◽  
Performance Computing

Applications of Virtualization Technology in Grid Systems and Cloud Servers

2018 ◽  
pp. 1-28
Author(s):  
Mohammad Samadi Gharajeh
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Global Network ◽  
Grid Systems ◽  
Virtual Clusters ◽  
Virtualization Technology ◽  
Computing Environments ◽  
Cloud Servers ◽  
Performance Rate ◽  
Performance Computing

Grid systems and cloud servers are two distributed networks that deliver computing resources (e.g., file storages) to users' services via a large and often global network of computers. Virtualization technology can enhance the efficiency of these networks by dedicating the available resources to multiple execution environments. This chapter describes applications of virtualization technology in grid systems and cloud servers. It presents different aspects of virtualized networks in systematic and teaching issues. Virtual machine abstraction virtualizes high-performance computing environments to increase the service quality. Besides, grid virtualization engine and virtual clusters are used in grid systems to accomplish users' services in virtualized environments, efficiently. The chapter, also, explains various virtualization technologies in cloud severs. The evaluation results analyze performance rate of the high-performance computing and virtualized grid systems in terms of bandwidth, latency, number of nodes, and throughput.

scholarly journals Large-scale HPC deployment of Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)

2020 ◽  
Vol 245 ◽  
pp. 09011
Author(s):  
Michael Hildreth ◽  
Kenyi Paolo Hurtado Anampa ◽  
Cody Kankel ◽  
Scott Hampton ◽  
Paul Brenner ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
High Performance Computing ◽  
High Throughput ◽  
High Performance ◽  
Large Scale ◽  
Starting Point ◽  
Virtual Clusters ◽  
Analysis Platform ◽  
Performance Computing

The NSF-funded Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project aims to develop and deploy artificial intelligence (AI) and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) built on top of existing CI elements. Specifically, the project has extended the CERN-based REANA framework, a cloud-based data analysis platform deployed on top of Kubernetes clusters that was originally designed to enable analysis reusability and reproducibility. REANA is capable of orchestrating extremely complicated multi-step workflows, and uses Kubernetes clusters both for scheduling and distributing container-based workloads across a cluster of available machines, as well as instantiating and monitoring the concrete workloads themselves. This work describes the challenges and development efforts involved in extending REANA and the components that were developed in order to enable large scale deployment on High Performance Computing (HPC) resources. Using the Virtual Clusters for Community Computation (VC3) infrastructure as a starting point, we implemented REANA to work with a number of differing workload managers, including both high performance and high throughput, while simultaneously removing REANA’s dependence on Kubernetes support at the workers level.

scholarly journals Applications of on-demand virtual clusters to high performance computing

2015 ◽  
Vol 7 (3) ◽  
pp. 511-516
Author(s):  
I. G. Gankevich ◽  
S. G. Balyan ◽  
S. A. Abrahamyan ◽  
V. V. Korkhov
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
On Demand ◽  
Virtual Clusters ◽  
Performance Computing

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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