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displacements) in a natural way. Many structures from the COD have such defects. The open-access database of simulated x-ray and neutron powder diffraction patterns of the nanocrystalline materials will be useful for the diagnostics of these materials. It may complement the Full Profile Search Match method in the analysis of the sizes of the crystallites in the sample. The “XANSONS for COD” ( http://xansons4cod.com ) project is aimed at creating such database for most of entries of the COD using volunteer computing. The project is powered by BOINC [ 3 ]. To simulate powder

speed. Volunteer computing is a form of Desktop Grid. The first large volunteer computing project, SETI@home, was launched in 1999, providing the basis for development of the BOINC platform. By now, there are several middleware systems for Desktop Grid computing. However, the open source BOINC platform [ 6 ] is nowadays considered as a de facto standard among them. Today there are more than 60 active BOINC-based projects using more than 15 million computers worldwide [ 1 ]. So, Desktop Grids hold their place among other high-performance systems, such as Computing

particular medical images. We are currently testing the feasibility of this technology, which would allow, in the long term, health professionals to send diagnostic images and receive results making few clicks on his/her mobile devices, while examining his/her patients. With mobile devices, it is also possible to reduce costs and space in computing resources for medical institutions as well as to reach places and communities with low penetration by other types of computer systems. The chosen platform used to build our proposal was BOINC [ 11 ]. BOINC is an open grid

1 Introduction This paper addresses the use of volunteer computing at CERN, and its integration with Grid infrastructure and applications in High Energy Physics (HEP). The motivation for bringing LHC computing under the Berkeley Open Infrastructure for Network Computing (BOINC) [ 1 ] is that available computing resources at CERN and in the HEP community are not sufficient to cover the needs for numerical simulation capacity. Today, active BOINC projects together harness about 7.5 Petaflops of computing power, covering a wide range of physical application, and

the questions of volunteers is crucial for the success of a project. A distinctive advantage of volunteer computing consists in the fact that it allows to perform computational experiments for months or even years, unlike computing clusters or grids. One of the key milestones in the development and popularization of volunteer computing was the birth of the Berkeley Open Infrastructure for Network Computing (BOINC) [ 1 ], developed in Berkeley in 2002. The majority of modern projects are based on BOINC. Currently, there are about 70 active BOINC projects with total

computing project Gerasim@Home http://gerasim.boinc.ru on BOINC platform [ 22 ]. Within this experiment we studied the behavior of methods for 2 ≤ N ≤ 500 and 0 < d ≤ 0.2 with steps ΔN = 1 and Δd = 0.001, the obtained results are presented in Section 4 . For each set of source data for iteration methods there was organized a formation of C max = 1 000 decisions with selection the best of them. 3 Brief Classification of Heuristic Methods with Limited Depth-First Search Techniques In this section we give a brief description of the heuristic methods (a detailed

optimization algorithm can help to overcome this problem. Evolutionary algorithms, particle swarm optimization, random sampling, simulated annealing, and metadynamics are the most popular methods for this purpose [ 1 ]. In the current study, an adaptation of the USPEX software [ 2 ] to the BOINC [ 3 , 4 ] distributed computing system interface is considered. USPEX is a well-known and widely used application, originally developed for the crystal structure prediction and is now capable of predicting structures of surfaces [6] , two-dimensional crystals [ 7 ], nanoclusters

why we launched the volunteer computing project Acoustics@home , aimed at solving such problems. The project is based on the popular platform BOINC (Berkley Open Infrastructure for Network Compuitng [ 3 ]). The notion of geoacoustic inversion refers to a variety of techniques in underwater acoustics that can be used for the reconstruction of water column and bottom parameters from acoustic data [ 20 ]. While previously the data for the geoacoustic inversion was mostly obtained using expensive receiver arrays, recently it was shown that a single-hydrophone recording

Section 3 , it would take about 3-4 months to solve all these instances on a single GPU. Meanwhile, any supercomputer equipped with state-of-the-art GPUs can cope with them in a reasonable time. However, at that moment the authors did not have access to such a supercomputer. Therefore, it was decided to employ volunteer computing [ 4 ] to run the experiment. 4.1 Volunteer Computing and BOINC Being a form of distributed computing, volunteer computing suits well to solving computationally hard problems that can be decomposed into independent subproblems ( i . e . the

References Anderson, D.P. (2004). BOINC: A system for public-resource computing and storage, 5th IEEE/ACM International Workshop on Grid Computing, Pittsburgh, PA, USA , pp. 4-10. Arthur, D. and Panigrahy, R. (2006). Analyzing BitTorrent and related peer-to-peer networks, Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, SODA’06, ACM, New York, NY, pp. 961-969, DOI: 10.1145/1109557.1109664. BOINC (2011). BOINC poject, http://boinc.berkeley.edu/. Chmaj, G. and Walkowiak, K. (2008). Data distribution in public-resource computing