Abstract
In the Big data and IoT era, graph data processing is widely used. The graph data is a kind of structural data that defined entities as vertices and described dependencies between different entities as edges. Today, a lot of graph computing systems emerge with massive diverse graph applications deployed, evaluating graph computing systems become a challenge work. Existing graph computing benchmarks are constructed with prevalent graph computing applications. However, the graph micro-benchmark is lacking, which is a key for the system fine-grained evaluation and obtaining the upper bound performance of the system. In this paper, we take graph computing applications as the combination of basic operations and user-defined operations. Then, we build the GraphBench benchmark suite with micro-benchmarks (basic operations) and component benchmarks (graph computing applications). At last, we evaluates the current mainstream graph computing frameworks with GraphBench. We found that there is no one-size-fits-all solution for the graph computing system. Using GraphBench, we can evaluate the graph computing system at the fine-grained level and get more insights.
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