Tuning the granularity of parallelism for distributed graph processing

Abstract

Popular distributed graph processing frameworks, such as Pregel and GraphLab, are based on the vertex-centric computation model, where users write their customized Compute function for each vertex to process the data iteratively. Vertices are evenly partitioned among the compute nodes, and the granularity of parallelism of the graph algorithm is normally tuned by adjusting the number of compute nodes. Vertex-centric model splits the computation into phases. Inside one specific phase, the computation proceeds as an embarrassingly parallel process, because no communication between compute nodes incurs. By default, current graph engine only handles one iteration of the algorithm in a phase. However, in this paper, we find that we can also tune the granularity of parallelism, by aggregating the computation of multiple iterations into one phase, which has a significant impact on the performance of the graph algorithm. In the ideal case, if all computations are handled in one phase, the whole algorithm turns into an embarrassingly parallel algorithm and the benefit of parallelism is maximized. Based on this observation, we propose two approaches, a function-based approach and a parameter-based approach, to automatically transform a Pregel algorithm into a new one with tunable granularity of parallelism. We study the cost of such transformation and the trade-off between the granularity of parallelism and the performance. We provide a new direction to tune the performance of parallel algorithms. Finally, the approaches are implemented in our graph processing system, N2, and we illustrate their performance using popular graph algorithms.