Чулкевич Роман Андреевич
Отдел суперкомпьютерного моделирования
Профессиональные интересы
Должности
- Ведущий инженер — Отдел суперкомпьютерного моделирования
Био
- · Начал работать в НИУ ВШЭ в 2019 году.
Образование
- 2019 · Магистратура: Южно-Уральский государственный университет (национальный исследовательский университет), факультет: ВШЭКН, специальность «Фундаментальная информатика и информационные технологии», квалификация «Магистр»
Опыт работы
- · Отдел суперкомпьютерного моделирования
- · Должность: ведущий инженер
- · Лаборатория Суперкомпьютерного моделирования ЮУрГУ
- · Должность: Директор суперкомпьютерного центра
- · Лаборатория Суперкомпьютерного моделирования ЮУрГУ
- · Должность: Программист суперкомпьютерного центра
- · Кафедра Системного программирования ВШЭКН ЮУрГУ Должность: Преподаватель
Награды и поощрения
- · Благодарность старшего директора по цифровой трансформации НИУ ВШЭ (ноябрь 2020)
Гранты и проекты
- 2020 · 2018-2020 Фонд содействия инновациям "УМНИК" - Разработка программной системы автоматического обнаружения и классификации дефектов холодного металлопроката с покрытием на принципах машинного зрения.
Конференции (4)
Показать все
- · 2024: Параллельные вычислительные технологии (ПаВТ) 2024 (Челябинск). Доклад: Enhancement of the data analysis subsystem for the task efficiency monitoring system HPC TaskMaster for the cHARISMa supercomputer complex of the HSE University
- · 2024: Суперкомпьютерные дни в России 2024 (Москва). Доклад: Разработка новых индикаторов для системы HPC TaskMaster
- · 2022: Параллельные вычислительные технологии (ПаВТ 2022) (Дубна). Доклад: HPC TaskMaster - система мониторинга эффективности задач для суперкомпьютерного центра
- · 2016: Russian Supercomputing Days (Москва). Доклад: Моделирование аппаратной архитектуры многоядерного ускорителя Xeon Phi KNL в контексте параллельной обработки баз данных
Идентификаторы исследователя
- ORCID:
0000-0002-8555-9270 - Google Scholar: https://scholar.google.com/citations?user=fBoH7zwAAAAJ&hl=ru&oi=ao
- Scopus AuthorID:
57203004946
Публикации (13)
Administration, Monitoring and Analysis of Supercomputers in Russia: a Survey of 10 HPC Centers
2021 · ARTICLE · en
Supercomputer technologies are in demand for solving many important and computationallyintensive tasks in various fields of science and technology. Therefore, it is not surprising that there are several dozen supercomputer centers only in Russia. However, the goals of creating such centers, as well as the range of tasks solved in them, can vary greatly, therefore the structure of supercomputers and the policies for their usage can significantly differ. This leads to the fact that many supercomputer centers live an isolated life – the administrators of such centers tend to solve administration-related tasks on their own, despite the fact that solutions for many similar tasks have already been developed and applied in other centers. This can happen due to different reasons, but in any case, this situation could and should be improved. To do this, it is worth establishing a closer connection between supercomputer centers, which will allow more actively exchanging experience or jointly developing desired system software. In order to understand the current situation in this area, a survey was conducted of representatives among 10 large supercomputer centers in Russia, and its results are presented in this paper. Two relevant topics about using monitoring data in practice and real-life examples of supercomputer functioning improvement are also discussed here in more detail. Their vision on these topics is provided by the system administrators of HSE University, Skoltech and Moscow State University.
Algorithm for the replica redistribution in the implementation of parallel annealing method on the hybrid supercomputer architecture
2020 · PREPRINT · en
The parallel annealing method is one of the promising approaches for large scale simulations as potentially scalable on any parallel architecture. We present an implementation of the algorithm on the hybrid program architecture combining CUDA and MPI. The problem is to keep all general-purpose graphics processing unit devices as busy as possible redistributing replicas and to do that efficiently. We provide details of the testing on Intel Skylake/Nvidia V100 based hardware running in parallel more than two million replicas of the Ising model sample. The results are quite optimistic because the acceleration grows toward the perfect line with the growing complexity of the simulated system.
Real-Time System for Automatic Cold Strip Surface Defect Detection
2019 · ARTICLE · en
Detection and classification of surface defects of the rolled metal is one of the main tasks for correctly assessing product quality. Historically, these tasks were performed by human. But due to a multitude of production factors, such as high rolling rate and temperature of the metal, the results of such human work are rather low. Replacing a human controller with an artificial intelligence system has been relevant for a long time; it is simply necessary within the concept of the Industry 4.0. This paper is devoted to the development of the prototype system automatic detection and classification of defects for one of the Iron-and-Steel Works of the Chelyabinsk region in the Russian Federation. The prototype consists of the Preprocessor, Classifier, Server, Database and User interface. The main focus is on achieving high classification accuracy, which is planned to be achieved through the use of convolutional neural networks.
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