High-performance computing for computational modelling in built environment-related studies – a scientometric review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Imuetinyan Aghimien ◽  
Lerato Millicent Aghimien ◽  
Olutomilayo Olayemi Petinrin ◽  
Douglas Omoregie Aghimien
Keyword(s):  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Computational Modelling ◽  
Scientometric Analysis ◽  
Content Type ◽  
Performance Computing ◽  
Aec Industry

Purpose This paper aims to present the result of a scientometric analysis conducted using studies on high-performance computing in computational modelling. This was done with a view to showcasing the need for high-performance computers (HPC) within the architecture, engineering and construction (AEC) industry in developing countries, particularly in Africa, where the use of HPC in developing computational models (CMs) for effective problem solving is still low. Design/methodology/approach An interpretivism philosophical stance was adopted for the study which informed a scientometric review of existing studies gathered from the Scopus database. Keywords such as high-performance computing, and computational modelling were used to extract papers from the database. Visualisation of Similarities viewer (VOSviewer) was used to prepare co-occurrence maps based on the bibliographic data gathered. Findings Findings revealed the scarcity of research emanating from Africa in this area of study. Furthermore, past studies had placed focus on high-performance computing in the development of computational modelling and theory, parallel computing and improved visualisation, large-scale application software, computer simulations and computational mathematical modelling. Future studies can also explore areas such as cloud computing, optimisation, high-level programming language, natural science computing, computer graphics equipment and Graphics Processing Units as they relate to the AEC industry. Research limitations/implications The study assessed a single database for the search of related studies. Originality/value The findings of this study serve as an excellent theoretical background for AEC researchers seeking to explore the use of HPC for CMs development in the quest for solving complex problems in the industry.

scholarly journals High-performance computing in water resources hydrodynamics

2020 ◽  
Vol 22 (5) ◽  
pp. 1217-1235 ◽  
Author(s):  
M. Morales-Hernández ◽  
M. B. Sharif ◽  
S. Gangrade ◽  
T. T. Dullo ◽  
S.-C. Kao ◽  
...  
Keyword(s):  
Water Resources ◽  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Test Case ◽  
Processing Unit ◽  
Central Processing ◽  
Graphics Processing ◽  
Performance Computing

Abstract This work presents a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing (HPC). The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.

Constructing a Bioinformatics Platform with Web and Mobile Services Based on NVIDIA Jetson TK1

2015 ◽  
Vol 7 (4) ◽  
pp. 57-73 ◽  
Author(s):  
Chun-Yuan Lin ◽  
Jin Ye ◽  
Che-Lun Hung ◽  
Chung-Hung Wang ◽  
Min Su ◽  
...  
Keyword(s):  
Power Consumption ◽  
High Performance Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Low Cost ◽  
Research Direction ◽  
Mobile Services ◽  
Performance Ratio ◽  
The Cost ◽  
Performance Computing

Current high-end graphics processing units (abbreviate to GPUs), such as NVIDIA Tesla, Fermi, Kepler series cards which contain up to thousand cores per-chip, are widely used in the high performance computing fields. These GPU cards (called desktop GPUs) should be installed in personal computers/servers with desktop CPUs; moreover, the cost and power consumption of constructing a high performance computing platform with these desktop CPUs and GPUs are high. NVIDIA releases Tegra K1, called Jetson TK1, which contains 4 ARM Cortex-A15 CPUs and 192 CUDA cores (Kepler GPU) and is an embedded board with low cost, low power consumption and high applicability advantages for embedded applications. NVIDIA Jetson TK1 becomes a new research direction. Hence, in this paper, a bioinformatics platform was constructed based on NVIDIA Jetson TK1. ClustalWtk and MCCtk tools for sequence alignment and compound comparison were designed on this platform, respectively. Moreover, the web and mobile services for these two tools with user friendly interfaces also were provided. The experimental results showed that the cost-performance ratio by NVIDIA Jetson TK1 is higher than that by Intel XEON E5-2650 CPU and NVIDIA Tesla K20m GPU card.

scholarly journals High-Performance Computing Framework Based on Distributed Systems for Large-Scale Neurophysiological Data

2021 ◽  
Author(s):  
Mohsen Hadianpour ◽  
Ehsan Rezayat ◽  
Mohammad-Reza Dehaqani
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Electrophysiological Recording ◽  
Neural Data ◽  
Data Framework ◽  
Neurophysiological Data ◽  
Computing Framework ◽  
Performance Computing ◽  
Neuroscience Community

Abstract Due to the significantly drastic progress and improvement in neurophysiological recording technologies, neuroscientists have faced various complexities dealing with unstructured large-scale neural data. In the neuroscience community, these complexities could create serious bottlenecks in storing, sharing, and processing neural datasets. In this article, we developed a distributed high-performance computing (HPC) framework called `Big neuronal data framework' (BNDF), to overcome these complexities. BNDF is based on open-source big data frameworks, Hadoop and Spark providing a flexible and scalable structure. We examined BNDF on three different large-scale electrophysiological recording datasets from nonhuman primate’s brains. Our results exhibited faster runtimes with scalability due to the distributed nature of BNDF. We compared BNDF results to a widely used platform like MATLAB in an equitable computational resource. Compared with other similar methods, using BNDF provides more than five times faster performance in spike sorting as a usual neuroscience application.

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.

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

Green Computing

2014 ◽  
pp. 248-260
Keyword(s):  
Climate Change ◽  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Green Computing ◽  
Research Topic ◽  
The Other ◽  
Cloud Infrastructures ◽  
Performance Computing

Green computing is a contemporary research topic to address climate and energy challenges. In this chapter, the authors envision the duality of green computing with technological trends in other fields of computing such as High Performance Computing (HPC) and cloud computing on one hand and economy and business on the other hand. For instance, in order to provide electricity for large-scale cloud infrastructures and to reach exascale computing, we need huge amounts of energy. Thus, green computing is a challenge for the future of cloud computing and HPC. Alternatively, clouds and HPC provide solutions for green computing and climate change. In this chapter, the authors discuss this proposition by looking at the technology in detail.

scholarly journals Editorial: Linking experimental and computational connectomics

2019 ◽  
Vol 3 (4) ◽  
pp. 902-904
Author(s):  
Alexander Peyser ◽  
Sandra Diaz Pier ◽  
Wouter Klijn ◽  
Abigail Morrison ◽  
Jochen Triesch
Keyword(s):  
High Performance Computing ◽  
In Silico ◽  
High Performance ◽  
Large Scale ◽  
Generative Models ◽  
Anatomical Structure ◽  
Global Function ◽  
Theoretical Neuroscience ◽  
New Generation ◽  
Performance Computing

Large-scale in silico experimentation depends on the generation of connectomes beyond available anatomical structure. We suggest that linking research across the fields of experimental connectomics, theoretical neuroscience, and high-performance computing can enable a new generation of models bridging the gap between biophysical detail and global function. This Focus Feature on ”Linking Experimental and Computational Connectomics” aims to bring together some examples from these domains as a step toward the development of more comprehensive generative models of multiscale connectomes.

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