Multiprocessor Modeling Technologies for the Applied Statistical Tasks

Скорочена інформація про документ
dc.contributor.authorShvachych G. G., Sazonova M. S., Zaporozhchenko O. E., Karpova T. P., Sushko L. F.
dc.date.accessioned2025-03-15T21:41:52Z
dc.date.available2025-03-15T21:41:52Z
dc.date.issued2019
dc.description.abstractThe work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculations come true independently on the different, separately taken blades of cluster, "calculators". The results are already processed on some separately taken master -blades ("analyzer"). This allows to get rid from the necessary presence of router-communicator between the random generator of numbers and "calculator". Obviously, that such decision allows to accelerate the process of calculations. It is shown that the parallel algorithms of the Monte Carlo method are stable to any input data and have the maximal parallel form and, thus, minimal possible time of realization using the parallel computing devices. If it is possible to appoint one processor to one knot of calculation. Thus the realization of calculations becomes possible in all knots of net area in parallel and simultaneously
dc.identifier.issnISSN 2518-167X
dc.identifier.urihttps://ir.duan.edu.ua/handle/123456789/5885
dc.language.isoen
dc.publisherInternational Academy Journal Web of Scholar. 3(33). doi: 10.31435/rsglobal_wos/31032019/6386
dc.subjecttechnology
dc.subjectmodeling tasks Monte Carlo
dc.subjectdistributed computing
dc.titleMultiprocessor Modeling Technologies for the Applied Statistical Tasks
dc.typeArticle

Файли

Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
Scholar.pdf
Розмір:
936.76 KB
Формат:
Adobe Portable Document Format
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Ескіз недоступний
Назва:
license.txt
Розмір:
2.84 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити

Зібрання

Кафедра інформаційних технологій