Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bag-of-work model. We improve the efficiency of the subtask-scheduling scheme by using an N-out-of-M strategy, and develop a Monte Carlo-specific lightweight checkpoint technique, which leads to a performance improvement for...

Monte Carlo and Quasi-Monte Carlo Methods Optimized Particle Regeneration Scheme for the Wigner Monte Carlo Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Paul Ellinghaus, Mihail Nedjalkov, and Siegfried Selberherr Sensitivity Analysis of Design Parameters for Silicon Diodes . . . . . . . . . . . . 34 J.M. Sellier, Rayna Georgieva, and Ivan D...

Population annealing is a Monte Carlo algorithm that marries features from simulated-annealing and parallel-tempering Monte Carlo. As such, it is ideal to overcome large energy barriers in the free-energy landscape while minimizing a Hamiltonian. Thus, population-annealing Monte Carlo can be used as a heuristic to solve combinatorial optimization problems. We illustrate the capabilities of popu...

abstractbackground: an electron beam generated x-ray spectrum consists of characteristic x-ray and continuous bermsstrahlung. the aim of this research is calculating and comparing x-ray spectra for different target filter of mammograms. materials and methods: monte carlo is a very powerful tool to simulate a series of different target-filter assembly in order to calculate the x-ray spectra. mcn...

background: the purpose of this study was to investigate the application of the monte carlo technique to calculate and analysis of dosimetric parameters for electron beams used in radiotherapy. this technique is based on statistical method and has a powerful role in different radiotherapy aspects. materials and methods: the simulated medical linear accelerator was the varian clinac 2100c. the e...

In this paper we introduce a Monte Carlo based hyper-heuristic. The Monte Carlo hyper-heuristic manages a set of low level heuristics (in this case just simple 2-opt swaps but they could be any other heuristics). Each of the low level heuristics is responsible for creating a unique neighbour that may be impossible to create by the other low level heuristics. On each iteration, the Monte Carlo h...

We consider the problem of numerical integration in dimension s, with eventually large s; the usual rules need a very huge number of nodes with increasing dimension to obtain some accuracy, say an error bound less than 10−2; this phenomenon is called ”the curse of dimensionality”; to overcome it, two kind of methods have been developped: the so-called Monte-Carlo and Quasi-Monte-Carlo methods. ...

This article reports on the contents of a tutorial session at MCQMC 2008. The tutorial explored various places in statistics where Monte Carlo methods can be used. There was a special emphasis on areas where Quasi-Monte Carlo ideas have been or could be applied, as well as areas that look like they need more research.