FDGLib: A Communication Library for Efficient Large-Scale Graph Processing in FPGA-Accelerated Data Centers

With the rapid growth of real-world graphs, the size of which can easily exceed the on-chip (board) storage capacity of an accelerator, processing large-scale graphs on a single Field Programmable Gate Array (FPGA) becomes difficult. The multi-FPGA acceleration is of great necessity and importance. Many cloud providers (e.g., Amazon, Microsoft, and Baidu) now expose FPGAs to users in their data centers, providing opportunities to accelerate large-scale graph processing. In this paper, we present a communication library, called FDGLib, which can easily scale out any existing single FPGA-based graph accelerator to a distributed version in a data center, with minimal hardware engineering efforts. FDGLib provides six APIs that can be easily used and integrated into any FPGA-based graph accelerator with only a few lines of code modifications. Considering the torus-based FPGA interconnection in data centers, FDGLib also improves communication efficiency using simple yet effective torus-friendly graph partition and placement schemes. We interface FDGLib into AccuGraph, a state-of-the-art graph accelerator. Our results on a 32-node Microsoft Catapult-like data center show that the distributed AccuGraph can be 2.32x and 4.77x faster than a state-of-the-art distributed FPGA-based graph accelerator ForeGraph and a distributed CPU-based graph system Gemini, with better scalability.