N-body methods, used in molecular dynamics and astronomy, are another an important class of parallel algorithms for high-performance computing (HPC) that has been considered for FPGA acceleration, recently even in a multi-FPGA setup. Targeting scientific simulations, FPGA acceleration work includes stencil computations, computational fluid dynamics and shallow water simulations, dense and sparse linear algebra, and FFTs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency.ĭuring the past couple of years, Field-Programmable Gate Arrays (FPGAs) have started to receive increased attention as accelerators in compute and data centers due to their power efficiency and dependable performance. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles.
Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs.
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