Workshop: IA^3 2022 - 12th Workshop on Irregular Applications: Architectures and Algorithms
Authors: Ahmedur Rahman Shovon and Landon Richard Dyken (University of Alabama, Birmingham); Oded Green (NVIDIA Corporation); and Thomas Gilray and Sidharth Kumar (University of Alabama, Birmingham)
Abstract: Datalog, a bottom-up declarative logic programming language, has a wide variety of uses for deduction, modeling, and data analysis, across application domains. Datalog can be efficiently implemented using relational algebra primitives such as join, projection and union. While, there exist several multi-threaded and multi-core implementations of Datalog that target CPU-based systems, our work makes an inroad towards developing a Datalog implementation for GPUs. We demonstrate the feasibility of a high performance relational algebra backend for a small subset of Datalog applications that can effectively leverage the parallelism of GPUs using cuDF. cuDF is a library from the Rapids suite that uses the NVIDIA CUDA programming model for GPU parallelism. It provides similar functionalities to Pandas, a popular data analysis engine. In this presentation, we analyze and evaluate the performance of cuDF versus Pandas for two graph mining problems implemented in Datalog, (1) triangles counting and (2) transitive closure computation.