Abstract
This paper presents a workflow for synthesizing near-optimal FPGA implementations of structured-mesh based stencil applications for explicit solvers. It leverages key characteristics of the application class and its computation-communication pattern and the architectural capabilities of the FPGA to accelerate solvers for high-performance computing applications. Key new features of the workflow are (1) the unification of standard state-of-the-art techniques with a number of high-gain optimizations such as batching and spatial blocking/tiling, motivated by increasing throughput for real-world workloads and (2) the development and use of a predictive analytical model to explore the design space, and obtain resource and performance estimates. Three representative applications are implemented using the design workflow on a Xilinx Alveo U280 FPGA, demonstrating near-optimal performance and over 85% predictive model accuracy. These are compared with equivalent highly-optimized implementations of the same applications on modern HPC-grade GPUs (Nvidia V100), analyzing time to solution, bandwidth, and energy consumption. Performance results indicate comparable runtimes with the V100 GPU, with over 2× energy savings for the largest non-trivial application on the FPGA. Our investigation shows the challenges of achieving high performance on current generation FPGAs compared to traditional architectures. We discuss determinants for a given stencil code to be amenable to FPGA implementation, providing insights into the feasibility and profitability of a design and its resulting performance.