◾ Geophysical Imaging in High-Performance Computing Platforms in Spain

The article is devoted to the design of high‑performance computing devices for parallel processing of information. The problem of increasing the productivity of computing facilities by one or several orders of magnitude is considered on the example of the high‑ performance electronic computer M‑10, which was created in the 1970s at the NIIVK. If in a conventional computer, the method of processing numbers is given by commands, then in M‑10, the methods for processing a function were specified by operators taken from functional analysis. At the same time, the possibility of parallel processing of an entire information line appeared. Such systems began to be called «functional operator type machines». The main ideas presented in the article may be of interest to developers of specialized machines of the new generation, as well as engineers involved in the creation of high‑performance computing devices using technologies of computing platforms.

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Action: A New Metric for Evaluating the Energy Efficiency on High Performance Computing Platforms (ranked on Green500 List)

WSEAS TRANSACTIONS ON COMPUTERS ◽

10.37394/23205.2022.21.4 ◽

2022 ◽

Vol 21 ◽

pp. 23-30

Author(s):

E. M. Karanikolaou ◽

M. P. Bekakos

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Comparative Study ◽

High Performance Computing ◽

High Performance ◽

Ranking Systems ◽

Performance Per Watt ◽

Computing Platforms ◽

Performance Computing

The need for new and more reliable metrics is always in demand. In this paper, a new metric is proposed for the evaluation of high performance computing platforms in conjunction with their energy consumption. The aim of the new metric is to reliably compare different HPC systems concerning their energy efficiency. The metric provides a mean to rank supercomputers of similar capabilities, avoiding the misleading results of metrics like performance-per-watt, currently used for ranking systems, as in the Green500 list, where systems with totally different sizes and capabilities are ranked consecutively. An example of this misuse for two adjacent systems in the Green500 list, is discussed. A comparative study for the energy efficiency of three high performance computing platforms, with different architectures, using the proposed metric is presented.

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On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

Grid and Cloud Computing ◽

10.4018/978-1-4666-0879-5.ch405 ◽

2012 ◽

pp. 841-861

Author(s):

Chao-Tung Yang ◽

Wen-Chung Shih

Keyword(s):

Grid Computing ◽

High Performance Computing ◽

High Speed ◽

High Performance ◽

Cluster Computing ◽

Structural Features ◽

Performance Technology ◽

Data Intensive ◽

Computing Platforms ◽

Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

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