The Future of Supercomputers and High-Performance Computing

2015 ◽  
pp. 152-164
Author(s):  
Domen Verber
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computational Models ◽  
Production Lines ◽  
The Future ◽  
Hardware Architectures ◽  
Primary Focus ◽  
Mobile Computers ◽  
Performance Computing ◽  
The Internet Of Things

A state-of-the-art and a possible future of High Performance Computing (HPC) are discussed. The steady advances in hardware have resulted in increasingly more powerful computers. Some HPC applications that were years ago only in the domain of supercomputers can nowadays be executed on desktop and mobile computers. Furthermore, the future of computers is in the “Internet-of-things” and cyber-physical systems. There, computers are embedded into the devices such as cars, house appliances, production lines, into our clothing, etc. They are interconnected with each other and they may cooperate. Based on that, a new kind of application emerges, which requires the HPC architectures and development techniques. The primary focus of the chapter is on different hardware architectures for HPC and some particularities of HPC programming. Some alternatives to traditional computational models are given. At the end, some replacements for semiconductor technologies of modern computers are debated.

scholarly journals High Performance Computing on New Accelerated Hardware Architectures

2010 ◽  
Vol Special Issue (1) ◽  
pp. 71-79 ◽  
Author(s):  
Marek Błażewicz ◽  
Krzysztof Kurowski ◽  
Bogdan Ludwiczak ◽  
Krystyna Napierała
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Hardware Architectures ◽  
Performance Computing

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.

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.

The Need for HPC Computing in Network Science

2018 ◽  
pp. 1-29
Keyword(s):  
High Performance Computing ◽  
Real World ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Network Science ◽  
Memory Access ◽  
Combined Use ◽  
Hardware Architectures ◽  
Many Core ◽  
Performance Computing

The size of complex networks introduces large amounts of traversal times that can be tackled by exploiting pervasive multi-core and many-core parallel hardware architectures. However, there is a list of factors that make the design of efficient parallel traversal algorithms for graphs difficult: unstructured problems, data-driven computation, irregular memory access, poor locality, and low computing load. In this chapter, the authors introduce the synergy between Network Science and High Performance Computing and motivate the combined use of multi/many-core heterogeneous computing and Network Science techniques to tackle the above-mentioned challenges and to efficiently traverse the structure of massive real-world graphs.

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