FP-AMR: A Reconfigurable Fabric Framework for Adaptive Mesh Refinement Applications

Abstract

Adaptive mesh refinement (AMR) is one of the most widely used methods in High Performance Computing accounting a large fraction of all supercomputing cycles. AMR operates by dynamically and adaptively applying computational resources non-uniformly to emphasize regions of the model as a function of their complexity. Because AMR generally uses dynamic and pointer-based data structures, acceleration is challenging, especially in hardware. As far as we are aware there has been no previous work published on accelerating AMR with FPGAs. In this paper, we introduce a reconfigurable fabric framework called FP-AMR. The work is in two parts. In the first FP-AMR offloads the bulk per-timestep computations to the FPGA; analogous systems have previously done this with GPUs. In the second part we show that the rest of the CPU-based tasks–including particle mesh mapping, mesh refinement, and coarsening–can also be mapped efficiently to the FPGA. We have evaluated FP-AMR using the widely used program AMReX and found that a single FPGA outperforms a Xeon E5-2660 CPU server (8 cores) by from 21x -23x depending on problem size and data distribution.