Project information
CERIT Scientific Cloud
(CERIT-SC)
- Project Identification
- CZ.02.1.01/0.0/0.0/16_013/0001802 (kod CEP: EF16_013/0001802)
- Project Period
- 5/2017 - 6/2021
- Investor / Pogramme / Project type
-
Ministry of Education, Youth and Sports of the CR
- Operational Programme Research, Development and Education
- Priority axis 1: Strengthening capacities for high-quality research
- MU Faculty or unit
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Institute of Computer Science
- Mgr. Aleš Křenek, Ph.D.
- doc. Ing. RNDr. Barbora Bühnová, Ph.D.
- doc. RNDr. Jiří Filipovič, Ph.D.
- doc. Mouzhi Ge, Ph.D.
- Mgr. Samuel Gorta
- Mgr. Jan Horáček
- Ing. Jana Hozzová, Ph.D.
- Mgr. Kristián Katanik
- Mgr. Vojtěch Krajňanský
- Mgr. Aleš Křenek, Ph.D.
- RNDr. Martin Macák, Ph.D.
- Mgr. David Myška
- RNDr. Petra Němcová
- Mgr. et Mgr. Jaroslav Oľha
- Mgr. Marek Pastierik
- RNDr. Tomáš Raček, Ph.D.
- RNDr. Tomáš Rebok, Ph.D.
- Bruno Rossi, PhD
- Bc. Maksym Skoryk
- RNDr. Terézia Slanináková
- Mgr. Radim Šašinka
- Mgr. Matúš Štovčik
- Muhammad Usman, Ph.D.
- Mgr. Vladimír Višňovský
- Project Website
- https://www.cerit-sc.cz/en
Centre CERIT-SC (CERIT Scientific Cloud) is a national centre operating a computing and data storage experimental infrastructure for research and development in the area of flexible e-infrastructures and large in-silico experiments, performed in close collaboration with other scientific disciplines. Its top-level mission can be phrased as “Speeding up the time from ideas to publications (and products) in all research disciplines”. The centre is built on three pillars: hardware resources of sufficient scale to ensure competitiveness, excellent research in specific areas of computer science (i.e. know-how to use the hardware resources efficiently), and long-term collaboration with the user communities (being true research partners of the users, not just providers of “precanned” technical solutions). This project aims at strengthening the first and the second pillar; the third one is currently being funded by other means.
Specifically, significant part of project budget is investment to hardware, pushing the centre’s equipment to the leading edge of available technology. This is complemented with the in-house research programme, with the goal to increase the efficient usage of the current and procured infrastructure.
The research programme will consist of two integrated subprogrammes aimed at big data analysis and high performance computing. Their approach to efficient use of infrastructure is complementary. While the Big Data subprogramme approaches it more top-down, concentrating on data processing methods and software architectures at a higher level, the High Performance Computing subprogramme concentrates on optimization at a lower level, from bottom-up. The Big Data research subprogramme will focus on the research in efficient big data analysis, with special focus on the careful selection of the best-fit data analysis technique to each specific problem setup. The High Performance Computing research subprogramme will focus on software parallelization, acceleration and optimization, including automated frameworks.
Publications
Total number of publications: 39
2023
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CoPAS: the Modular Data Analytics Tool
Year: 2023
2022
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On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy
Acta Crystallographica Section D: Structural Biology, year: 2022, volume: 78, edition: 4, DOI
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Using hardware performance counters to speed up autotuning convergence on GPUs
Journal of Parallel and Distributed Computing, year: 2022, volume: 160, edition: February, DOI
2021
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4D-GRAPHS
Year: 2021
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Advances in Xmipp for Cryo–Electron Microscopy: From Xmipp to Scipion
Molecules, year: 2021, volume: 26, edition: 20, DOI
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Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials
International Union of Crystallography Journals, year: 2021, volume: 8, edition: 6, DOI
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Image Processing in Cryo-Electron Microscopy of Single Particles: The Power of Combining Methods
Structural Proteomics, year: 2021, number of pages: 33 s.
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Kernel Tuning Toolkit 2.0
Year: 2021
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Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures
Data in Brief, year: 2021, volume: 39, edition: December, DOI
2020
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A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit
Future Generation Computer Systems, year: 2020, volume: 108, edition: July, DOI