Abstract

Abstract

Abstract
We discuss the concept of FPGA-unfriendliness, the property of certain algorithms, programs, or domains which may limit their applicability to FPGAs. Specifically, we look at graph analysis, which has recently seen increased interest in combination with High-Level Synthesis, but has yet to find great success compared to established acceleration mechanisms. To this end, we make use of Xilinx's Vitis Graph Library to implement Single-Source Shortest Paths (SSSP) and PageRank (PR), and present a custom kernel written from the ground up for Distinctiveness Centrality (DC, a novel graph centrality measure). We use public datasets to test these implementations, and analyse power consumption and execution time. Our comparisons against published data for GPU and CPU execution show FPGA slowdowns in execution time between around 18.5x and 328x for SSSP, and around 1.8x and 195x for PR, respectively. In some instances, we obtained FPGA speedups versus CPU of up to 2.5x for PR. Regarding DC, results show speedups from 0.1x to 3.5x, and energy efficiency increases from 0.8x to 6x. Lastly, we provide some insights regarding the applicability of FPGAs in FPGA-unfriendly domains, and comment on the future as FPGA and HLS technology advances.

Abstract

Abstract

Abstract