This conference paper appears in the 2016 IEEE InternationalParallel and Distributed Processing Symposium Workshops (May 2016).
GraphBLAS is an emerging paradigm for graph computation that makes it easy to program new graph algorithms in a highly abstract language of linear algebra. The promise of GraphBLAS is that an abstract graph program will execute in a wide variety of programming environments, ranging from embedded environments to distributed memory computers. In this paper we present our initial implementation of GraphBLAS primitives for graphics processing unit (GPU) systems called GraphBLAS Template Library (GBTL). Our implementation is an ongoing effort in the context of GraphBLAS standardization efforts by a diverse group of academics and representatives of the industry. Our implementation consists of a high-level C ++ frontend, and the GPU functionality is implemented with a combination of the CUSP library for sparse-matrix computation on GPU and the NVIDIA Thrust framework for abstract GPU programs. We give initial performance results of our implementations, and we discuss solutions to the problems we encountered when providing a low-level implementation for a high-level generic interface.