Extending High-Level Synthesis with High-Performance Computing Performance Visualization

The recent maturity in High-Level Synthesis (HLS) has renewed the interest of using Field-Programmable Gate-Arrays (FPGAs) to accelerate High-Performance Computing (HPC) applications. Today, several studies have shown performance- and power-benefits of using FPGAs compared to existing approaches for a number of application kernels with ample room for improvements. Unfortunately, modern HLS tools offer little support to gain clarity and insight regarding why a certain application behaves as it does on the FPGA, and most experts rely on intuition or abstract performance models. In this work, we hypothesize that existing profiling and visualization tools used in the HPC domain are also usable for understanding performance on FPGAs. We extend an existing HLS tool-chain to support Paraver - a state-of-the-art visualization and profiling tool well-known in HPC. We describe how each of the events and states are collected, and empirically quantify its hardware overhead. Finally, we practically apply our contribution to two different applications, demonstrating how the tool can be used to provide unique insights into application execution and how it can be used to guide optimizations. In this work, we hypothesize that existing profiling and visualization tools used in the HPC domain are also usable for understanding performance on FPGAs. We extend an existing HLS tool-chain to support Paraver - a state-of-the-art visualization and profiling tool well-known in HPC. We describe how each of the events and states are collected, and empirically quantify its hardware overhead. Finally, we practically apply our contribution to two different applications, demonstrating how the tool can be used to provide unique insights into application execution and how it can be used to guide optimizations.