Algorithm for the replica redistribution in the implementation of parallel annealing method on the hybrid supercomputer architecture
Modern supercomputers use GPUs as accelerators in computing nodes. GPUs allow scientific applications to greatly boost performance using fine-grained parallelism. CUDA programming model oriented to take advantage of the SIMT GPU architecture writing low-level code. Contrary to this approach, OpenACC and OpenMP 4.5 represent a declarative model of parallel programming using compiler pragmas with support of GPU offloading. In this paper the efficiency of matrix multiplication using these programming models is considered. A comparative analysis of the performance of naive and hand tuned matrix multiplication on Nvidia Tesla V100 and MX940 GPUs and modern CPUs is carried out. Analysis of vendor-optimized BLAS libraries is also present.
We present optimization guidelines and implementations of cryptographic hash functions GOST R 34.11-94 and GOST R 34.11-2012. Results for x86_64 CPUs and NVIDIA CUDA-capable GPUs are provided for our and several other well-known implementations. It is shown that the new standard may be twice as fast as the old one on modern CPUs, but it may be slower on embedded devices and GPUs. The results given for our implementation are the fastest among all the tested implementations on both platforms.
An approach is described to implementation of the Method of Four Russians for reducing the dense matrices over GF(2) to row echelon form using the NVIDIA CUDA platform. Estimates of the algorithm running time and recommendations on choosing the algorithm parameters are given. It is shown that the developed implementation is most effective in comparison with the existing solutions for matrices of a size 2^17 x 2^17.
In this work, we describe the problem of automated pollen recognition using images from lighting microscope. Automated pollen recognition related to such important tasks as honey quality control, air quality control for helping to asthma and allergy patients, paleopalynology, forensic palynology. We describe the problem solution based on machine learning and CUDA. Extracted features and preprocessing steps are described. Results are compared on dataset of 5 specie. The best model is convolutional neural network with 89% of accuracy. Its performance was particularly up twice using CUDA.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The dynamics of a two-component Davydov-Scott (DS) soliton with a small mismatch of the initial location or velocity of the high-frequency (HF) component was investigated within the framework of the Zakharov-type system of two coupled equations for the HF and low-frequency (LF) fields. In this system, the HF field is described by the linear Schrödinger equation with the potential generated by the LF component varying in time and space. The LF component in this system is described by the Korteweg-de Vries equation with a term of quadratic influence of the HF field on the LF field. The frequency of the DS soliton`s component oscillation was found analytically using the balance equation. The perturbed DS soliton was shown to be stable. The analytical results were confirmed by numerical simulations.