scholarly journals Hands On High Performance Computing: Developing A Cluster Computing Course For Real World Supercomputing

2020 ◽  
Author(s):  
Thomas Hacker
Keyword(s):  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Cluster Computing ◽  
Hands On ◽  
Performance Computing

scholarly journals Improving speed of models for improved real-world decision-making

2021 ◽  
Author(s):  
Jason Thompson ◽  
Haifeng Zhao ◽  
Sachith Seneviratne ◽  
Rohan Byrne ◽  
Rajith Vidanaarachichi ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Time Pressure ◽  
Practical Importance ◽  
Model Development ◽  
Sudden Onset ◽  
Health Crisis ◽  
Performance Improvements ◽  
Performance Computing

The sudden onset of the COVID-19 global health crisis and as-sociated economic and social fall-out has highlighted the im-portance of speed in modeling emergency scenarios so that ro-bust, reliable evidence can be placed in policy and decision-makers’ hands as swiftly as possible. For computational social scientists who are building complex policy models but who lack ready access to high-performance computing facilities, such time-pressure can hinder effective engagement. Popular and ac-cessible agent-based modeling platforms such as NetLogo can be fast to develop, but slow to run when exploring broad param-eter spaces on individual workstations. However, while deploy-ment on high-performance computing (HPC) clusters can achieve marked performance improvements, transferring models from workstations to HPC clusters can also be a technically challenging and time-consuming task. In this paper we present a set of generic templates that can be used and adapted by NetLogo users who have access to HPC clusters but require ad-ditional support for deploying their models on such infrastruc-ture. We show that model run-time speed improvements of be-tween 200x and 400x over desktop machines are possible using 1) a benchmark ‘wolf-sheep predation’ model in addition to 2) an example drawn from our own work modeling the spread of COVID-19 in Victoria, Australia. We describe how a focus on improving model speed is non-trivial for model development and discuss its practical importance for improved policy and de-cision-making in the real world. We provide all associated doc-umentation in a linked git repository.

High-Performance Computing for Theoretical Study of Nanoscale and Molecular Interconnects

2014 ◽  
pp. 513-532
Author(s):  
Rasit O. Topaloglu ◽  
Swati R. Manjari ◽  
Saroj K. Nayak
Keyword(s):  
Integrated Circuits ◽  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Quantum Effects ◽  
Accurate Analysis ◽  
Graphical Processing Units ◽  
Graphical Processing ◽  
Silicon Based ◽  
Performance Computing

Interconnects in semiconductor integrated circuits have shrunk to nanoscale sizes. This size reduction requires accurate analysis of the quantum effects. Furthermore, improved low-resistance interconnects need to be discovered that can integrate with biological and nanoelectronic systems. Accurate system-scale simulation of these quantum effects is possible with high-performance computing (HPC), while high cost and poor feasibility of experiments also suggest the application of simulation and HPC. This chapter introduces computational nanoelectronics, presenting real-world applications for the simulation and analysis of nanoscale and molecular interconnects, which may provide the connection between molecules and silicon-based devices. We survey computational nanoelectronics of interconnects and analyze four real-world case studies: 1) using graphical processing units (GPUs) for nanoelectronic simulations; 2) HPC simulations of current flow in nanotubes; 3) resistance analysis of molecular interconnects; and 4) electron transport improvement in graphene interconnects. In conclusion, HPC simulations are promising vehicles to advance interconnects and study their interactions with molecular/biological structures in support of traditional experimentation.

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2012 ◽  
pp. 841-861
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Performance Technology ◽  
Data Intensive ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2011 ◽  
Vol 3 (1) ◽  
pp. 69-88
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Biological Data ◽  
Performance Technology ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

Super Leaders

2015 ◽  
pp. 351-377
Author(s):  
Kim Grover-Haskin
Keyword(s):  
High Performance Computing ◽  
Baby Boomers ◽  
Real World ◽  
High Performance ◽  
Leadership Studies ◽  
Talent Pool ◽  
Real World Problem ◽  
The Future ◽  
It Leadership ◽  
Performance Computing

Present day and projected labor demands forecast a need for minds to comprehend in algorithm in order to leverage computing developments for real world problem resolutions. This chapter focuses not so much on solutions to the preparation of the learners and the scientists, but on the future leadership that will advocate and open doors for the high performance computing community to be funded, supported, and practiced. Supercomputing's sustainable future lies in its future of leadership. Studies over the last ten years identify a shift in leadership as the Baby Boomers enter retirement. The talent pool following the Baby Boomers will shrink in numbers between 2010-2020. Women continue to be under represented in IT leadership. This chapter provides information on the talent pool for supercomputing, discusses leadership and organizational culture as influenced by gender, and explores how a mentoring community fosters leaders for the future.

scholarly journals An Embedded System for applying High Performance Computing in Educational Learning Activity

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Erlyn Wina Rachmawan ◽  
Nurul Fahmi ◽  
Edi Wahyu Widodo ◽  
Samsul Huda ◽  
M. Unggul Pamenang ◽  
...  
Keyword(s):  
Embedded System ◽  
High Performance Computing ◽  
High Performance ◽  
Cluster Computing ◽  
Low Cost ◽  
Learning Activity ◽  
Learning Satisfaction ◽  
Parallel Case ◽  
High Performance Computing Cluster ◽  
Performance Computing

HPC (High Performance Computing) has become more popular in the last few years. With the benefits on high computational power, HPC has impact on industry, scientific research and educational activities. Implementing HPC as a curriculum in universities could be consuming a lot of resources because well-known HPC system are using Personal Computer or Server. By using PC as the practical moduls it is need great resources and spaces.  This paper presents an innovative high performance computing cluster system to support education learning activities in HPC course with small size, low cost, and yet powerful enough. In recent years, High Performance computing usually implanted in cluster computing and require high specification computer and expensive cost. It is not efficient applying High Performance Computing in Educational research activiry such as learning in Class. Therefore, our proposed system is created with inexpensive component by using Embedded System to make High Performance Computing applicable for leaning in the class. Students involved in the construction of embedded system, built clusters from basic embedded and network components, do benchmark performance, and implement simple parallel case using the cluster.  In this research we performed evaluation of embedded systems comparing with i5 PC, the results of our embedded system performance of NAS benchmark are similar with i5 PCs. We also conducted surveys about student learning satisfaction that with embedded system students are able to learn about HPC from building the system until making an application that use HPC system.

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