scholarly journals Implement of a high-performance computing system for parallel processing of scientific applications and the teaching of multicore and parallel programming

2018 ◽  
Author(s):  
Apolinar Velarde Martinez
Keyword(s):  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Education Institution ◽  
Computing System ◽  
Memory Systems ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
High Performance Computing System ◽  
Performance Computing

Increasingly complex algorithms for the modeling and resolution of different problems, which are currently facing humanity, has made it necessary the advent of new data processing requirements and the consequent implementation of high performance computing systems; but due to the high economic cost of this type of equipment and considering that an education institution cannot acquire, it is necessary to develop and implement computable architectures that are economical and scalable in their construction, such as heterogeneous distributed computing systems, constituted by several clustering of multicore processing elements with shared and distributed memory systems. This paper presents the analysis, design and implementation of a high-performance computing system called Liebres InTELigentes, whose purpose is the design and execution of intrinsically parallel algorithms, which require high amounts of storage and excessive processing times. The proposed computer system is constituted by conventional computing equipment (desktop computers, lap top equipment and servers), linked by a high-speed network. The main objective of this research is to build technology for the purposes of scientific and educational research.

scholarly journals Creating of an individual modeling environment in a hybrid high-performance computing system

2020 ◽  
Vol 22 (3) ◽  
pp. 197-201
Author(s):  
K. I. Volovich ◽  
S. A. Denisov ◽  
S. I. Malkovsky
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Optimal Solution ◽  
Computing System ◽  
Computing Environment ◽  
Computing Systems ◽  
Modeling Environment ◽  
Modeling Systems ◽  
High Performance Computing System ◽  
Performance Computing

The article is devoted to the problem of solving scientific problems in the field of high-performance computing systems. An approach to solving a certain kind of problems in materials science is the use of mathematical modeling technologies implemented by specialized modeling systems. The greatest efficiency of the modeling system is shown when deployed in hybrid high-performance computing systems (HHPC), which have high performance and allow solving problems in an acceptable time with sufficient accuracy. However, there are a number of limitations that affect the work of the research team with modeling systems in the HHPC computing environment: the need to access graphics accelerators at the stage of development and debugging of algorithms in the modeling system, the need to use several modeling systems in order to obtain the most optimal solution, the need to dynamically change settings modeling systems for solving problems. The solution to the problem of the above limitations is assigned to an individual modeling environment functioning in the HHPC computing environment. The optimal solution for creating an individual modeling environment is the technology of virtual containerization. An algorithm for the formation of an individual modeling environment in a hybrid high-performance computing complex based on the «docker» virtual containerization system is proposed. An individual modeling environment is created by installing the necessary software in the base container, setting environment variables, installing custom software and licenses. A feature of the algorithm is the ability to form a library image from a base container with a customized individual modeling environment. In conclusion, the direction for further research work is indicated. The algorithm presented in the article is independent of the implementation of the job management system and can be used for any high-performance computing system.

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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