Rethinking High Performance Computing System Architecture for Scientific Big Data Applications

2016 ◽  
Author(s):  
Yong Chen ◽  
Chao Chen ◽  
Yanlong Yin ◽  
Xian-He Sun ◽  
Rajeev Thakur ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
System Architecture ◽  
High Performance ◽  
Computing System ◽  
Big Data Applications ◽  
High Performance Computing System ◽  
Performance Computing

scholarly journals CHALLENGES OF USING BIG DATA IN DISTRIBUTED EXASCALE SYSTEMS

2020 ◽  
Vol 3 (2) ◽  
pp. 245-254
Author(s):  
Firuza Tahmazli-Khaligova ◽  
Keyword(s):  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Research Work ◽  
Computing System ◽  
Huge Data ◽  
High Performance Computing System ◽  
Interactive Nature ◽  
Data Volume ◽  
Performance Computing

In a traditional High Performance Computing system, it is possible to process a huge data volume. The nature of events in classic High Performance computing is static. In Distributed Exa-scale System has a different nature. The processing Big data in a distributed exascale system evokes a new challenge. The dynamic and interactive character of a distributed exascale system changes processes status and system elements. This paper discusses the challenge that Big data attributes: volume, velocity, variety, how they influence distributed exascale system dynamic and interactive nature. While investigating the effect of the Dynamic and Interactive nature of exascale systems in computing Big data, this research work suggests the Markov chains model. This model suggests the transition matrix, which identifies system status and memory sharing. It lets us analyze the two systems convergence. As a result in both systems are explored by the influence of each other.

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.

scholarly journals An Application Of Pde To Predict Brain Tumor Growth Using High Performance Computing System

1970 ◽  
Vol 6 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Md Rajibul Islam ◽  
Norma Alias ◽  
Siew Young Ping
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Two Dimensional ◽  
Explicit Method ◽  
Parallel Virtual Machine ◽  
High Performance Computing System ◽  
Performance Computing

This study is to predict two-dimensional brain tumors growth through parallel algorithm using the High Performance Computing System. The numerical finite-difference method is highlighted as a platform for discretization of twodimensional parabolic equations. The consequence of a type of finite difference approximation namely explicit method will be presented in this paper. The numerical solution is applied in the medical field by solving a mathematical model for the diffusion of brain tumors which is a new technique to predict brain tumor growth. A parabolic mathematical model used to describe and predict the evolution of tumor from the avascular stage to the vascular, through the angiogenic process. The parallel algorithm based on High Performance Computing (HPC) System is used to capture the growth of brain tumors cells in two-dimensional visualization. PVM (Parallel Virtual Machine) software is used as communication platform in the HPC System. The performance of the algorithm evaluated in terms of speedup, efficiency, effectiveness and temporal performance. Keywords: Partial Differential Equation (PDE); parabolic equation; explicit method; Red Black Gauss-Seidel; Parallel Virtual Machine (PVM); High Performance Computing (HPC); Brain Tumor. DOI: http://dx.doi.org/10.3329/diujst.v6i1.9335 DIUJST 2011; 6(1): 60-68

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