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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 targeting CPU-based systems, our work makes an inroads towards developing a Datalog implementation for GPUs. We demonstrate the feasibility of a high-performance relational algebra backend for a 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 paper, we analyze and evaluate the performance of cuDF versus Pandas for two graph-mining problems implemented in Datalog, (1) triangle counting and (2) transitive-closure computation.