Source-to-source transformation based methodology for graph-parallel FPGA accelerators

Abstract

Graph applications are becoming more and more important with their widespread usage and the amounts of data they deal with. Biological and social web graphs are well-known examples which show the importance of efficient processing of the graph analytic applications and problems. Addressing those problems in an efficient manner is not a straightforward task. Distributing and parallelizing the computation, and integrating hardware accelerators are the main approaches that were tried during the last decade. However, these approaches mainly focus on specific legacy algorithms and may not completely solve the problems. Therefore, when there is an emerging need for a non-legacy algorithm targeting a specific problem, the developer has to cope with the adversaries of the distribution, parallelization techniques, and hardware specifications to parallelize and accelerate the application. Our proposed source-to-source based methodology gives the freedom of not knowing the low-level details of parallelization and distribution by translating any vertex-centric C++ graph application into pipelined SystemC model. In order to support different types of graph applications, we have implemented several features like non-standard application support, active set functionality, multi-pipeline support, etc. The generated SystemC model can be synthesized by High-Level Synthesis (HLS) tools to obtain the FPGA programming image, i.e., the bitstream. Our accelerator development flow can generate two different execution models, high-throughput (HT) and work-efficient (WE). Compared to OpenCL counterparts of the algorithms, HT and WE models perform slightly better in terms of execution time and throughput. WE model performed approximately 40% better than OpenCL in terms of work done and execution time. Therefore, the proposed source-to-source based methodology is able to provide more efficient hardware designs by only requiring a simple high-level language description from the user.