Demonstration of FPGA acceleration of Monte Carlo simulation
File(s)Barbone_2023_J._Phys.__Conf._Ser._2438_012023.pdf (595.95 KB)
Published version
Author(s)
Type
Journal Article
Abstract
We present results from a stand-alone simulation of electron single Coulomb scattering as implemented completely on an Field Programmable Gate Array (FPGA) architecture and compared with an identical simulation on a standard CPU. FPGA architectures offer unprecedented speed-up capability for Monte Carlo simulations, however with the caveats of lengthy development cycles and resource limitation, particularly in terms of on-chip memory and DSP blocks. As a proof of principle of acceleration on an FPGA, we chose a single scattering process of electrons in water at an energy of 6 MeV. The initial code-base was implemented in C++ and optimised for CPU processing. To measure the potential performance gains of FPGAs compared to modern multi-core CPUs we computed 100M histories of a 6 MeV electron interacting in water. Without performing any hardware-specific optimisation, the results show that the FPGA implementation is over 110 times faster than an optimised parallel implementation running on 12 CPU-cores, and over 270 times faster than a sequential single-core CPU implementation. The results on both architectures were statistically equivalent. The successful implementation and acceleration results are very encouraging for the future exploitation of more sophisticated Monte Carlo simulation on FPGAs for High Energy Physics applications.
Date Issued
2023-02-01
Date Acceptance
2023-01-01
Citation
Journal of Physics : Conference Series, 2023, 2438 (1), pp.1-7
ISSN
1742-6588
Publisher
Institute of Physics (IoP)
Start Page
1
End Page
7
Journal / Book Title
Journal of Physics : Conference Series
Volume
2438
Issue
1
Copyright Statement
© 2023 The Author(s). Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
License URL
Identifier
https://iopscience.iop.org/article/10.1088/1742-6596/2438/1/012023
Publication Status
Published
Date Publish Online
2023-02-01