| Summary: | © 2020 Owner/Author. In this demonstration paper, we present the Graph Based Benchmark Suite (GBBS), a suite of scalable, provably-efficient implementations of over 20 fundamental graph problems for shared-memory multicore machines. Our results are obtained using a graph processing interface written in C++, extending the Ligra interface with additional functional primitives that have clearly defined cost bounds. Our approach enables writing high-level codes that are simultaneously simple and high-performance by virtue of using highly-optimized primitives. Another benefit is that optimizations, such as graph compression, are implemented transparently to high-level user code, and can thus be utilized without changing the implementation. Our approach enables our codes to scale to the largest publicly-available real-world graph containing over 200 billion edges on a single multicore machine. We show how to use GBBS to process and perform a variety of tasks on real-world graphs. We present the high-level C++ APIs that enable us to write concise, high-performance implementations. We also introduce a Python interface to GBBS, which lets users easily prototype algorithms and pipelines in Python that significantly outperform NetworkX, a mature Python-based graph processing solution.
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