GraphMat: High performance graph analytics made productive

By Narayanan Sundaram, Nadathur Rajagopalan Satish, Md. Mostofa Ali Patwary, Subramanya R. Dulloor, Michael J. Anderson, Satya Gautam Vadlamudi, Dipankar Das, Pradeep K. Dubey. Proceedings of the VLDB Endowment, 2015

344 citations recorded by the source corpus.

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 thus get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is a single-node multicore graph framework written in C++ which has enabled us to write a diverse set of graph algorithms with the same effort compared to other vertex programming frameworks. GraphMat performs 1.1-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. GraphMat also matches the performance of MapGraph, a GPU-based graph framework, despite running on a CPU platform with significantly lower compute and bandwidth resources. 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 graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMat can naturally benefit from the trend of increasing parallelism in future hardware.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/b513711621e81d0abd042e0877ca751581a993f5

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Citations in the corpus

HyGCN: A GCN Accelerator with Hybrid Architecture (2020, background): “In this work, we identify that the execution patterns of these two phases are distinct, even almost opposite, which requires separate design requirements.”
The GAP Benchmark Suite (2015, methodology): “To create our benchmark, we learned from the best practices of the community, and we co-developed this benchmark with our graph workload characterization [5].”
Graphicionado: A high-performance and energy-efficient accelerator for graph analytics (2016, methodology): “In order to identify the inefficiencies of off-chip memory bandwidth usage, we measured GraphMat [46] bandwidth consumption on an Intel Xeon server instrumented using Intel VTune Amplifier [21].”
OuterSPACE: An Outer Product Based Sparse Matrix Multiplication Accelerator (2018, methodology): “19) used for Graph500 to generate undirected power-law graphs, which is also employed in recent work in graph analytics [51] [58].”