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Field‐programmable gate arrays and quantum Monte Carlo: Power efficient coprocessing for scalable high‐performance computing
Author(s) -
Cardamone Salvatore,
Kimmitt Jonathan R. R.,
Burton Hugh G. A.,
Todman Timothy J.,
Li Shurui,
Luk Wayne,
Thom Alex J. W.
Publication year - 2019
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.25853
Subject(s) - massively parallel , scalability , computer science , exascale computing , field programmable gate array , monte carlo method , exploit , parallel computing , field (mathematics) , multi core processor , quantum computer , supercomputer , computational science , scaling , computation , computer architecture , quantum , embedded system , algorithm , physics , statistics , mathematics , computer security , quantum mechanics , database , pure mathematics , geometry
Massively parallel architectures offer the potential to significantly accelerate an application relative to their serial counterparts. However, not all applications exhibit an adequate level of data and/or task parallelism to exploit such platforms. Furthermore, the power consumption associated with these forms of computation renders “scaling out” for exascale levels of performance incompatible with modern sustainable energy policies. In this work, we investigate the potential for field‐programmable gate arrays (FPGAs) to feature in future exascale platforms, and their capacity to improve performance per unit power measurements for the purposes of scientific computing. We have focused our efforts on variational Monte Carlo, and report on the benefits of coprocessing with a FPGA relative to a purely multicore system.