Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Pavel Burovskiy"'
Autor:
Peter J. Knowles, Vitali Averbukh, B. D. Cooper, Stephen Girdlestone, Georgi Gaydadjiev, Pavel Burovskiy, Wayne Luk
Publikováno v:
Journal of Chemical Theory and Computation
We demonstrate the use of dataflow technology in the computation of the correlation energy in molecules at the Møller-Plesset perturbation theory (MP2) level. Specifically, we benchmark density fitting (DF)-MP2 for as many as 168 atoms (in valinomyc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2146fe719c4932c31708fbe3e64525a1
https://doi.org/10.1021/acs.jctc.7b00649
https://doi.org/10.1021/acs.jctc.7b00649
Publikováno v:
Journal of Signal Processing Systems
Genetic programming can be used to identify complex patterns in financial markets which may lead to more advanced trading strategies. However, the computationally intensive nature of genetic programming makes it difficult to apply to real world probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab86d51113ca46069f5222ab09aaae0d
http://europepmc.org/articles/PMC6956885
http://europepmc.org/articles/PMC6956885
Publikováno v:
ACM Transactions on Reconfigurable Technology and Systems. 10:1-22
The Finite Element Method (FEM) is a common numerical technique used for solving Partial Differential Equations on large and unstructured domain geometries. Numerical methods for FEM typically use algorithms and data structures which exhibit an unstr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e82ac88e5a10795402c58bdb22bca40a
https://doi.org/10.1145/3024064
https://doi.org/10.1145/3024064
Autor:
Marco D. Santambrogio, Anna Maria Nestorov, Hristina Palikareva, Enrico Reggiani, Tobias Becker, Pavel Burovskiy
Publikováno v:
IPDPS Workshops
Computational finance is a challenging application domain with ever-increasing performance requirements. Driven by the competition between companies, computational finance pushes High Performance Computing (HPC) technology to its limits. In this pape
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fcfa55b8f19866ac3090e47399658f33
https://doi.org/10.1109/ipdpsw.2017.67
https://doi.org/10.1109/ipdpsw.2017.67
Publikováno v:
13th International Symposium, ARC 2017
Lecture Notes in Computer Science ISBN: 9783319562575
ARC
Lecture Notes in Computer Science ISBN: 9783319562575
ARC
Highly-tuned FPGA implementations can achieve significant performance and power efficiency gains over general purpose hardware. However the limited development productivity has prevented mainstream adoption of FPGAs in many areas such as High Perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90bc5fa557f73c1d8469411e07678f62
http://hdl.handle.net/10044/1/50985
http://hdl.handle.net/10044/1/50985
Autor:
Tobias Becker, Anna Maria Nestorov, Enrico Reggiani, Hristina Palikareva, Pavel Burovskiy, Georgi Gaydadjiev
Publikováno v:
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017
DATE
DATE
Exascale computing is facing a gap between the ever increasing demand for application performance and the underlying chip technology that does no longer deliver the expected exponential increases in CPU performance. The industry is now progressively
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::149d53cf1023464468863a802e1b9e82
Research on processors based on dataflow principles has been active for over 40 years, but such processors have not become mainstream general-purpose computing. Recently, advances in field-programmable technology spark renewed interest in dataflow sy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d09614155bb90a045cd2f2d7f3747bd
https://doi.org/10.1016/bs.adcom.2017.04.002
https://doi.org/10.1016/bs.adcom.2017.04.002
Publikováno v:
2016 26th International Conference on Field Programmable Logic and Applications (FPL)
FPL
FPL
Sparse Matrix Vector multiplication (SpMV) is an important kernel in many scientific applications. In this work we propose an architecture and an automated customisation method to detect and optimise the architecture for block diagonal sparse matrice
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1ad0e5b16962bc77e9e5843efddb2d3
http://hdl.handle.net/10044/1/42864
http://hdl.handle.net/10044/1/42864
Publikováno v:
2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA '16)
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays-FPGA '16
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays-FPGA 16
FPGA
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays-FPGA '16
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays-FPGA 16
FPGA
Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays
© 2016 ACM.Sparse matrix vector multiplication (SpMV) is an impor- tant kernel in many scientific applications. To improve the performance and applicability of FPGA based SpMV, we propose an approach for exploiting properties of the input matrix to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ee56e33fd415f4cd7f3fb131763dd85
http://hdl.handle.net/10044/1/33329
http://hdl.handle.net/10044/1/33329
Publikováno v:
International Journal of Mathematics
We investigate second order quasilinear equations of the form f_{ij} u_{x_ix_j}=0 where u is a function of n independent variables x_1, ..., x_n, and the coefficients f_{ij} are functions of the first order derivatives p^1=u_{x_1}, >..., p^n=u_{x_n}
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b83c7ac55cb02836383ee0a34a91ef0
https://doi.org/10.1142/s0129167x10006215
https://doi.org/10.1142/s0129167x10006215