Proceedings of the second international workshop on Software engineering for high performance computing system applications - SE-HPCS '05

2005 ◽  
Keyword(s):  
Software Engineering ◽  
High Performance Computing ◽  
High Performance ◽  
International Workshop ◽  
Computing System ◽  
High Performance Computing System ◽  
Performance Computing ◽  
Second International

Construction of high performance computing system for fusion research using cluster technology

2009 ◽  
Vol 29 (8) ◽  
pp. 2132-2135
Author(s):  
Wei PAN ◽  
Liao-yuan CHEN ◽  
Yong-ge LI ◽  
Jin-hua ZHANG ◽  
Li PAN ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Fusion Research ◽  
High Performance Computing System ◽  
Performance Computing ◽  
Cluster Technology

A High-Performance Computing System for Probabilistic Weather Forecasts

2019 ◽  
Author(s):  
Weiming Hu ◽  
Guido Cervone ◽  
Vivek Balasubramanian ◽  
Matteo Turilli ◽  
Shantenu Jha
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Weather Forecasts ◽  
High Performance Computing System ◽  
Performance Computing

scholarly journals Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran
Keyword(s):  
High Performance Computing ◽  
Assignment Problem ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Metagenomic Data ◽  
Taxonomic Assignment ◽  
High Performance Computing System ◽  
Powerful Approach ◽  
Performance Computing

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.

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