Pdes-A

Rahman, Shafiur; Abu-Ghazaleh, Nael; Najjar, Walid

doi:10.1145/3064911.3064930

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…A general FPGA-based discrete event simulation accelerator is presented by Rahman et al [9]. Their work focuses on the efficient maintaining of the event queues on FPGAs.…”

Section: Related Workmentioning

confidence: 99%

Pedal to the Bare Metal

Xiao¹,

Kilinç²,

Andelfinger

et al. 2020

Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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The performance of Agent-based Traffic Simulations (ABTS) has been shown to benefit tremendously from offloading to accelerators such as GPUs. In the search for the most suitable hardware platform, reconfigurable hardware is a natural choice. Some recent work considered ABTS on Field-Programmable Gate Arrays (FPGAs), yet only implemented simplified cellular automaton-based models. The recent introduction of support for high-level synthesis from C, C++, and OpenCL in FPGA toolchains allows FPGA designs to be expressed in a form familiar to software developers. However, the performance achievable with this approach in a simulation context is not well-understood. In this work, to the best of our knowledge, we present the first FPGA-accelerated ABTS based on widely-accepted microscopic traffic simulation models, and the first to be generated from high-level code. The achieved speedup of up to 24.3 over a sequential CPU-based execution indicates that recent FPGA toolchains allow simulationists to unlock the performance benefits of reconfigurable hardware without the need to express the simulation models in low-level hardware description languages. CCS CONCEPTS• Computing methodologies → Agent / discrete models; Massively parallel and high-performance simulations; Simulation tools; • Hardware → Hardware accelerators.

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“…A general FPGA-based discrete event simulation accelerator is presented by Rahman et al [9]. Their work focuses on the efficient maintaining of the event queues on FPGAs.…”

Section: Related Workmentioning

confidence: 99%