FAWS: FPGA Acceleration of Large-Scale Wave Simulations

Efficiently solving large-scale scientific problems described by partial differential equations (PDEs) is a critical task in high-performance computing. Application-specific hardware design is a well-known solution, but the wide range of kernels makes it infeasible to provision supercomputers with accelerators for all applications. This makes reconfigurable platforms a promising direction. In this work, we focus on wave simulations using discontinuous Galerkin solvers, as an important class of PDE applications. Existing work using FPGAs is limited to accelerating specific kernels or small problems that fit into FPGA BRAM. We present FAWS, a generic and configurable architecture for large-scale accelerated wave simulation problems running on FPGAs out of DRAM. FAWS exploits fine- and coarse-grain parallelism using a scalable array of application-specific processing cores, and incorporates novel dataflow optimizations, including prefetching, kernel fusion, and memory layout optimizations to minimize data transfers and maximize DRAM bandwidth utilization. We demonstrate FAWS on the simulation of elastic wave equations. Results show that a single FPGA core achieves 2.2x higher performance than 24 Xeon cores with 18.64x better energy efficiency, when given ~1.94x less peak DRAM bandwidth. Scaling to the same peak DRAM bandwidth, an FPGA is 4.27x and 1.96x faster than 24 CPU cores and an Nvidia P100 GPU, with 31.33x and 5.29x better efficiency, respectively.