SUSU Supercomputer Resources for Industry and fundamental Science

Kostenetskiy, Pavel S.; Semenikhina, Polina

doi:10.1109/glosic.2018.8570068

Cited by 37 publications

(23 citation statements)

References 58 publications

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“…There is a clear need for decentralized development and extensive use of supercomputers at universities and enterprises. A supercomputing complex at SUSU in Chelyabinsk is an example of such successful implementation and application [27,28].…”

Section: Resultsmentioning

confidence: 99%

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

Loginovsky

Shestakov

Shinkarev

2020

JSFI

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The article presents an analysis of approaches to the development of enterprise information systems that are in use today. One of the major trends that predetermines the agenda of information technology is the focus on parallel computing of large volumes of data using supercomputing technologies. The article considers the resulting ubiquitous move to distributed patterns of building enterprise information systems and avoiding monolithic architectures. The emphasis is placed on the importance of such fundamental characteristics of enterprise information systems as reliability, scalability, and maintainability. The article justifies the importance of machine learning in the context of effective big data analysis and competitive gain for business, vital for both maintaining a leading position in the market and surviving in conditions of global instability and digitalization of economy. Transition from storing the current state of a enterprise information system to storing a full log and history of all changes in the event stream is proposed as an instrument of achieving linearization of the data stream for subsequent parallel computing. There is a new view that is being shaped of specialists at the intersection of engineering and analytical disciplines, who would be able to effectively develop scalable systems and algorithms for data processing and integration of its results into company business processes.

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Section: Resultsmentioning

confidence: 99%

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

Loginovsky

Shestakov

Shinkarev

2020

JSFI

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“…The SHRINK parameter, which determines the number of k-points in reciprocal space in the Pak-Monkhorst scheme, at which the Kohn-Sham matrix is diagonalized, has been set to 8. The calculations were performed on the TORNADO LSM supercomputer of South Ural State University [35]. The structure of isolated molecules was optimized at the B3LYP / 6-31G** level in the GAMESS R2 program [36] with a gradient convergence of 0.000009.…”

Section: Methodsmentioning

confidence: 99%

Features of the Pnictogen Bonds Formed by Neighboring Nitro Groups in Crystals

Sobalev¹,

Matveychuk²,

Барташевич³

2019

Chem

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Features of pnictogen O…N bonds were considered from the point of view of analyzing various geometric orientations of the neighboring nitro groups NO 2 …NO 2 observed in molecular crystals. The "idealized" orientation for a pnictogen bond, where the nucleophilic fragment of the atom O of one nitro group was directed to the electrophilic site of the atom N of the other one, and the "stacking" orientation, which was characterized by the parallel stacking of nitro groups, differ in the features of the electron density and electrostatic potential distribution between O and N atoms. The applied electronic criterion, obtained with the use of quantum chemical calculations with periodic boundary conditions, confirmed that for the considered O…N interactions the necessary condition for their classification as pnictogen bonds was met.

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“…Hardware. The experiments were carried out on the node of the Tornado SUSU supercomputer [11]. Such a node consists of a host, which is two 6 core Intel Xeon CPU, and a 61 core Intel Xeon Phi coprocessor.…”

Section: Methodsmentioning

confidence: 99%

“…Tornado20M is a real dataset with one-month voltage log of the Tornado SUSU supercomputer [11] nodes. Such a log is mined to discover the strong associations among the racks, shelves, and nodes of the supercomputer, and dangerous values of voltage.…”

Section: Methodsmentioning

confidence: 99%

Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems

Zymbler

2019

CIT. J. comput. inf. technol

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Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases. Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation of candidate itemsets based on frequent itemsets found at the previous iteration, and pruning of clearly infrequent itemsets. The Dynamic Itemset Counting (DIC) algorithm is a variation of Apriori, which tries to reduce the number of passes made over a transactional database while keeping the number of itemsets counted in a pass relatively low. In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi many-core system for the case when the transactional database fits in main memory. Intel Xeon Phi provides a large number of small compute cores with vector processing units. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based internal layout for transactions and itemsets. This technique reduces the memory space for storing the transactional database, simplifies the support count via logical bitwise operation, and allows for vectorization of such a step. Experimental evaluation on the platforms of the Intel Xeon CPU and the Intel Xeon Phi coprocessor with large synthetic and real databases showed good performance and scalability of the proposed algorithm.

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SUSU Supercomputer Resources for Industry and fundamental Science

Cited by 37 publications

References 58 publications

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

Supercomputing Technologies as Drive for Development of Enterprise Information Systems and Digital Economy

Features of the Pnictogen Bonds Formed by Neighboring Nitro Groups in Crystals

Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems

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