Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

Abstract

For decades, the streaming architecture of FPGAs has delivered accelerated performance across many application domains, such as option pricing solvers in finance, computational fluid dynamics in oil and gas, and packet processing in network routers and firewalls. However, this performance has come at the significant expense of programmability, i.e., the performance-programmability gap. In particular, FPGA developers use a hardware design language (HDL) to implement the application data path and to design hardware modules for computation pipelines, memory management, synchronization, and communication. This process requires extensive low-level knowledge of the target FPGA architecture and consumes significant development time and effort. To address this lack of programmability of FPGAs, OpenCL provides an easy-to-use and portable programming model for CPUs, GPUs, APUs, and now, FPGAs. However, this significantly improved programmability can come at the expense of performance, that is, there still remains a performance-programmability gap. To improve the performance of OpenCL kernels on FPGAs, and thus, bridge the performance-programmability gap, we apply and evaluate the effect of various optimization techniques on GEM, an N-body method from the OpenDwarfs benchmark suite.