Linear Algebra is the Right Way to Think About Graphs

Graph algorithms are challenging to implement on new accelerators such as GPUs. To address this problem, GraphBLAS is an innovative on-going e ort by the graph analytics community to formulate graph algorithms as sparse linear algebra, so that they can be expressed in a performant, succinct and in a backend-agnostic manner. Initial research e orts in implementing GraphBLAS on GPUs for graph processing and analytics have been promising, but challenges such as feature-incompleteness and poor performance still exist compared to their vertex-centric (“think like a vertex”) graph framework counterparts. For our thesis, we propose a multilanguage graph framework aiming to simplify the development of graph algorithms, which 1) provides a multi-language Graph-BLAS interface for the end-users to express, develop, and re ne graph algorithms more succinctly than existing distributed graph frameworks; 2) abstracts away from the end-users performancetuning decisions; 3) utilizes the advantages of existing low-level GPU computing primitives to maintain high performance.