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Bychkov, Igor, Feoktistov, Alexander, Gorsky, Sergey, Edelev, Alexei, Sidorov, Ivan, Kostromin, Roman, Fereferov, Evgeniy, Fedorov, Roman.  2020.  Supercomputer Engineering for Supporting Decision-making on Energy Systems Resilience. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). :1—6.
We propose a new approach to creating a subject-oriented distributed computing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on the idea of advancing and integrating the following important capabilities in supercomputer engineering: continuous integration, delivery, and deployment of the system and applied software, high-performance computing in heterogeneous environments, multi-agent intelligent computation planning and resource allocation, big data processing and geo-information servicing for subject information, including weakly structured data, and decision-making support. This combination of capabilities and their advancing are unique to the subject domain under consideration, which is related to combinatorial studying critical objects of energy systems. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).
Sheptunov, Sergey A., Sukhanova, Natalia V..  2020.  The Problems of Design and Application of Switching Neural Networks in Creation of Artificial Intelligence. 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :428–431.
The new switching architecture of the neural networks was proposed. The switching neural networks consist of the neurons and the switchers. The goal is to reduce expenses on the artificial neural network design and training. For realization of complex models, algorithms and methods of management the neural networks of the big size are required. The number of the interconnection links “everyone with everyone” grows with the number of neurons. The training of big neural networks requires the resources of supercomputers. Time of training of neural networks also depends on the number of neurons in the network. Switching neural networks are divided into fragments connected by the switchers. Training of switcher neuron network is provided by fragments. On the basis of switching neural networks the devices of associative memory were designed with the number of neurons comparable to the human brain.
Chugunkov, Ilya V., Ivanov, Michael A., Kliuchnikova, Bogdana V..  2019.  Hash Functions are Based on Three-Dimensional Stochastic Transformations. 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :202–205.
The methods are based on injecting unpredictability into means and objects of protection are called stochastic methods of information security. The effective protection can be done only by using stochastic methods against an active opponent. The effectiveness of stochastic protection methods is defined by the quality of the used pseudo-random number generators and hash functions. The proposed hashing algorithm DOZENHASH is based on the using of 3D stochastic transformations of DOZEN family. The principal feature of the algorithm is that all input and output data blocks as well as intermediate results of calculations are represented as three-dimensional array of bytes with 4 bytes in each dimension. Thus, the resulting transformation has a high degree of parallelism at the level of elementary operations, in other words, it is focused on the implementation using heterogeneous supercomputer technologies.
Molyakov, Andrey.  2019.  New security descriptor computing algorithm of Supercomputers. 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). :349–350.
The author describes computing algorithm based on new scientific definition - “The resulting convolution, which takes into account changes in the significant bits of variables of the Zhegalkin polynomial, is a superposition of hash function calculations for the i-th process”.
Innokentievich, T. P., Vasilevich, M. V..  2017.  The Evaluation of the Cryptographic Strength of Asymmetric Encryption Algorithms. 2017 Second Russia and Pacific Conference on Computer Technology and Applications (RPC). :180–183.

We propose a method for comparative analysis of evaluation of the cryptographic strength of the asymmetric encryption algorithms RSA and the existing GOST R 34.10-2001. Describes the fundamental design ratios, this method is based on computing capacity used for decoding and the forecast for the development of computer technology.

Zhu, G., Zeng, Y., Guo, M..  2017.  A Security Analysis Method for Supercomputing Users \#x2019; Behavior. 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). :287–293.

Supercomputers are widely applied in various domains, which have advantage of high processing capability and mass storage. With growing supercomputing users, the system security receives comprehensive attentions, and becomes more and more important. In this paper, according to the characteristics of supercomputing environment, we perform an in-depth analysis of existing security problems in the process of using resources. To solve these problems, we propose a security analysis method and a prototype system for supercomputing users' behavior. The basic idea is to restore the complete users' behavior paths and operation records based on the supercomputing business process and track the use of resources. Finally, the method is evaluated and the results show that the security analysis method of users' behavior can help administrators detect security incidents in time and respond quickly. The final purpose is to optimize and improve the security level of the whole system.

Johnston, B., Lee, B., Angove, L., Rendell, A..  2017.  Embedded Accelerators for Scientific High-Performance Computing: An Energy Study of OpenCL Gaussian Elimination Workloads. 2017 46th International Conference on Parallel Processing Workshops (ICPPW). :59–68.

Energy efficient High-Performance Computing (HPC) is becoming increasingly important. Recent ventures into this space have introduced an unlikely candidate to achieve exascale scientific computing hardware with a small energy footprint. ARM processors and embedded GPU accelerators originally developed for energy efficiency in mobile devices, where battery life is critical, are being repurposed and deployed in the next generation of supercomputers. Unfortunately, the performance of executing scientific workloads on many of these devices is largely unknown, yet the bulk of computation required in high-performance supercomputers is scientific. We present an analysis of one such scientific code, in the form of Gaussian Elimination, and evaluate both execution time and energy used on a range of embedded accelerator SoCs. These include three ARM CPUs and two mobile GPUs. Understanding how these low power devices perform on scientific workloads will be critical in the selection of appropriate hardware for these supercomputers, for how can we estimate the performance of tens of thousands of these chips if the performance of one is largely unknown?