Analytical Estimation of the Scalability of Iterative Numerical Algorithms on Distributed Memory Multiprocessors

This article presents a new high-level parallel computational model named BSF - Bulk Synchronous Farm. The BSF model extends the BSP model to deal with the compute-intensive iterative numerical methods executed on distributed-memory multiprocessor systems. The BSF model is based on the master-worker paradigm and the SPMD programming model. The BSF model makes it possible to predict the upper scalability bound of a BSF-program with great accuracy. The BSF model also provides equations for estimating the speedup and parallel efficiency of a BSF-program.Comment: Submitted to a special issue of Lobachevskii Journal of Mathematics on "Parallel Structure of Algorithms

BSF-skeleton: A Template for Parallelization of Iterative Numerical Algorithms on Cluster Computing Systems

This article describes a method for creating applications for cluster computing systems using the parallel BSF skeleton based on the original BSF (Bulk Synchronous Farm) model of parallel computations developed by the author earlier. This model uses the master/slave paradigm. The main advantage of the BSF model is that it allows to estimate the scalability of a parallel algorithm before its implementation. Another important feature of the BSF model is the representation of problem data in the form of lists that greatly simplifies the logic of building applications. The BSF skeleton is designed for creating parallel programs in C++ using the MPI library. The scope of the BSF skeleton is iterative numerical algorithms of high computational complexity. The BSF skeleton has the following distinctive features. - The BSF-skeleton completely encapsulates all aspects that are associated with parallelizing a program. - The BSF skeleton allows error-free compilation at all stages of application development. - The BSF skeleton supports OpenMP programming model and workflows.Comment: Submitted to Methods

Surface Movement Method for Linear Programming

The article presents a new method of linear programming, called the surface movement method. This method constructs an optimal objective path on the surface of the feasible polytope from the initial boundary point to the point at which the optimal value of the objective function is achieved. The optimality of the path means moving in the direction of maximum increase/decrease in the value of the objective function. A formal description of the algorithm implementing the surface movement method is described. The convergence theorem of this algorithm is proved. The presented method can be effectively implemented using a feed forward deep neural network to determine the optimal direction of movement along the faces of the feasible polytope. To do this, a multidimensional local image of the linear programming problem is constructed at the point of the current approximation. This image is fed to the input of the deep neural network, which returns a vector determining the direction of the optimal objective path on the polytope surface

Функции интегративного поиска вузовских библиотечных порталов, построенных на основе J-ИРБИС 2.0

Integration of universities’ full-text and bibliographic resources is examined. The authors suggest using the hybrid model of aggregated-distributed retrieval and its realization. They also analyze functionalities and advantages of J-IRBIS 2.0 as an instrument to build web-portal of university libraries and the system of supporting services.Рассмотрены проблемы интеграции вузовских полнотекстовых и библиографических ресурсов. Предложено использование гибридной модели сводно-распределённого поиска и её реализации. Проанализированы возможности и отмечены преимущества J-ИРБИС 2.0 как инструмента для создания портала вузовской библиотеки и системы вспомогательных сервисов. Подчеркнуто, что благодаря возможностям этой системы интегративный поиск становится доступной технологией, которая может использоваться без привлечения технических специалистов и дополнительных финансовых затрат