GPU accelerated population annealing algorithm
Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures.
The manual sets out the requirements of science and industry, leading to use of multicomputer systems and multiprocessor systems, which inevitably use the principle of parallel computing, background and state of the art, describes the main approaches to the organization of multiprocessor computer systems, development of parallel algorithms for the numerical solution of problems and parallel programming techniques.
The book contains selected papers that were presented on PhD Summer schools on Scientific Computing jointly organized by Waterford Institute of Technology, Lomonosov Moscow State University, Kyiv National Taras Shevchenko University, Saint-Petersburg State University and Nanjing University of Technology. The schoold were mainly organized in teleconference mode and linked researchers and PhD students from several countries.
The problems of identifying latent parallelism in the algorithm by explicitly max (the construction of stacked-parallel form of the algorithm graph) and implicit (the method of streaming - DATA-FLOW - calculations), the development of parallel programs in the MPI-paradigm programming and quantitative research strength calculations for the acceleration parallelization on the parameters of a multiprocessor system and the quality of parallel programs. The manual is practical and can be used by students to prepare for the performance of laboratory and practice of the works, of course and diploma projects. Generated by network applications ra-operability in a multiprocessor environment, architecture MPP (Massively Par-allel Processing); particularly on Linux-cluster computing IT department MGUPI 4. Before working to understand whole con-SPECT lectures on 'Parallel Computing'.
In this paper, we present results of a computational evaluation of goMapReduce parallel programming model approach for solving distributed data processing problems. In some applications, particularly data center problems, including text processing the programming models can aggregate significant number of parallel processes. We first discuss the implementation of these approaches using both Linux and Plan9 operating system and conduct a comparative scalability study of the both. From these results, we empirically show that, in practical implementation and evaluation of a goMapReduce model, the degree of OS's support for distributed processing encountered in solving the resulting word counting problem is crucial. We conclude that the goMapReduce approach under Plan9 may be useful in developing a heuristic approach for the data center problems.
The research subject is the computational complexity of the probabilistic neural network (PNN) in the pattern recognition problem for large model databases. We examined the following methods of increasing the efficiency of a neuralnetwork classifier: a parallel multithread realization, reducing the PNN to a criterion with testing of homogeneity of feature histograms of input and reference images, approximate nearestneighbor analyses (BestBin First, directed enumeration methods). The approach was tested in facialrecognition experiments with FERET dataset.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.
Many electronic devices operate in a cyclic mode. This should be considered when forecastingreliability indicators at the design stage.The accuracy of the prediction and the planning for the event to ensure reliability depends on correctness of valuation and accounting greatest possiblenumber of factors. That in turn will affect the overall progress of the design and, in the end,result in the quality and competitiveness of products