ScalaBFS2: A High-performance BFS Accelerator on an HBM-enhanced FPGA Chip

Abstract

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for the Breadth-First Search (BFS) algorithm that demands a high bandwidth for accessing the graph data stored in memory. Different from traditional FPGA-DRAM platforms where memory bandwidth is the precious resource due to the limited DRAM channels, FPGA chips equipped with HBM have much higher memory bandwidths provided by the large quantities of HBM channels, but still a limited amount of logic (LUT, FF, and BRAM/URAM) resources. Therefore, the key to design a high-performance BFS accelerator on an HBM-enhanced FPGA chip is to efficiently use the logic resources to build as many as possible Processing Elements (PEs) and configure them flexibly to obtain as high as possible effective memory bandwidth that is useful to the algorithm from the HBM, rather than partially emphasizing the absolute memory bandwidth. To exploit as high as possible effective bandwidth from the HBM, ScalaBFS2 conducts BFS in graphs in a vertex-centric manner and proposes designs, including the independent module (HBM Reader) for memory accessing, multi-layer crossbar, and PEs that implement hybrid mode (i.e., capable of working in both push and pull modes) algorithm processing, to utilize the FPGA logic resources efficiently. Consequently, ScalaBFS2 is able to build up to 128 PEs on the XCU280 FPGA chip (produced with the 16 nm process and configured with two HBM2 stacks) of a Xilinx Alveo U280 board and achieves performance of 56.92 Giga Traversed Edges Per Second (GTEPS) by fully using its 32 HBM memory channels. Compared with the state-of-the-art graph processing system (i.e., ReGraph) built on top of the same board, ScalaBFS2 achieves 2.52x~4.40x performance speedups. Moreover, when compared with Gunrock running on an Nvidia A100 GPU that is produced with the 7 nm process and configured with five HBM2e stacks, ScalaBFS2 achieves 1.34x~2.40x speedups on absolute performance, and 7.35x~13.18x speedups on power efficiency.