Computing Sciences

With reconfigurable fabrics delivering increasing performance over the years, Field-Programmable Gate Arrays (FPGAs) are becoming an appealing solution for next-generation High-Performance Computing (HPC) systems. However, in order to gain traction among traditional von Neumann architectures, the optimization process of Field-Programmable Gate Array (FPGA) designs should be further abstracted to a higher level. In fact, while High-Level Synthesis (HLS) already provides a handy way to write FPGA code with common high-level languages, substantial effort and expertise are still required to optimize the resulting FPGA design for the underlying hardware. To overcome this problem, we propose a semi-automated performance optimization methodology based on a Hierarchical Roofline model for FPGAs. System-wide and applications-specific optimizations such as off-chip memory transfer and data locality optimizations are guided by the FPGA Roofline model whereas FPGA-specific optimizations are automatically searched by a Design Space Exploration (DSE) engine. We demonstrate the way this methodology allows to easily analyze and optimize to peak system performance a wide set of applications ranging from particle methods, wavefront algorithms, and sparse arithmetic computations. In addition, we prove that the integrated Design Space Exploration (DSE) engine achieves a 14.36x maximum speedup if compared to previous automated solutions in the literature.