Abstract
The importance of high-performance graph processing to solve big data problems targeting high-impact applications is greater than ever before. Recent graph processing frameworks target different hardware platforms (e.g., shared memory systems, accelerators such as GPUs, and distributed systems) and differ with respect to the programming model they adopt (e.g., based on linear algebra formulations of graph algorithms or enabling direct access to the graph structure). To better understand the impact of these choices, this paper, presents a comparative study of five state-of-the-art graph processing frameworks: two CPU-only frameworks - GraphMat and Galois, two GPU-based frameworks - Nvgraph and Gunrock; and Totem, a hybrid (CPU+GPU) framework. We use three popular graph algorithms (PageRank, Single Source Shortest Path, and Breadth-First Search), and massive scale graphs with up to billions of edges. Our evaluation focuses on three performance metrics: (i) execution time, (ii) scalability and (iii) energy consumption.
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