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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Implementing Apache Spark jobs execution and Apache Spark cluster creation for Openstack Sahara

Proceedings of the Institute for System Programming of the RAS. 2015. Vol. 27. No. 5. P. 35–48.
Turdakov D., Aleksiyants A., Borisenko O., Sher A., Kuznetsov S.

In this paper the problem of creating virtual clusters in clouds for big data analysis with Apache Hadoop and Apache Spark is discussed. Both clouds and MapReduce models are popular nowadays for a bunch of reasons: cheapness and efficient big data analysis respectively. For these thoughts, having an open source solution for building clusters is important. The article gives an overview on existing methods for Apache Spark cluster creation in clouds. We consider two open source cloud engines OpenStack and Eucalyptus and the most popular proprietary cloud service Amazon Web Services and examine cloud related features presented by these systems. Afterwards, we regard possible ways of creating virtual clusters for big data processing in OpenStack and describe their pros and cons. In the second part we describe in details one of these solutions that uses service Sahara. Sahara represents a cluster management system for OpenStack and it is used for setting up virtual clusters and executing MapReduce jobs. Sahara did not support contemporary versions of Apache Spark. The article introduces the results of our work that led to a Sahara modification, describes its idea and implementation details. By virtue of our modification, Sahara is able to create and use virtual clusters with contemporary versions of Apache Spark in OpenStack clouds.

Priority areas: IT and mathematics
Language: English
DOI
Keywords: IaaSApache Spark Openstack
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