GraphMat: High performance graph analytics made productive

TL;DR

GraphMat is a C++ framework that combines high productivity in graph analytics with performance close to native code by leveraging sparse matrix operations, outperforming existing frameworks in speed and scalability.

Contribution

It introduces a vertex programming framework that maps to optimized sparse matrix operations, achieving high performance without sacrificing user productivity.

Findings

GraphMat is 1.2-7X faster than GraphLab, CombBLAS, and Galois.

It scales 13-15X on 24 cores, outperforming other frameworks.

Performance is close to native hand-optimized code, within 1.2X.

Abstract

Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is in C++, and we have been able to write a diverse set of graph algorithms in this framework with the same effort compared to other vertex programming frameworks. GraphMat performs 1.2-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of different graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMatcan naturally benefit from the trend of increasing parallelism on future hardware.