Many systems are recently proposed for large-scale iterative graph analytics on a single machine with GPU accelerators. Despite of many research efforts, for iterative directed graph processing over GPUs, existing solutions suffer from slow convergence speed and high data access cost, because many vertices are ineffectively reprocessed for lots of rounds so as to update their states according to other active vertices regardless of their dependencies. In this paper, we propose a novel and efficient iterative directed graph processing system on a machine with the support of multiple GPUs. Compared with existing systems, the unique feature of our system is that it takes advantage of the dependencies between vertices in three novel ways. First, it represents a directed graph into a set of disjoint hot/cold directed paths and takes the path as the basic parallel processing unit, so as to help efficient vertex state propagation along the paths over GPUs for faster convergence speed and higher utilization ratio of the loaded data. Second, it tries to dispatch the paths to GPUs for parallel processing according to the topological order of the dependency graph of them. Many paths then converge along such an order after processing them for exactly once, getting lower reprocessing overhead. Third, a path scheduling strategy is further developed on each streaming multiprocessor to enable the privileged execution of the paths (e.g., the hot paths) with greater impacts on vertex state propagation for shorter convergence time according to vertex dependency. Experimental results show that our approach speeds up iterative directed graph processing by up to 3.54 times in comparison with the state-of-the-art systems.

@inproceedings{paperbot4852,
  title = {DiGraph},
  author = {Yu Zhang and Xiaofei Liao and Hai Jin and Bingsheng He and Haikun Liu and Lin Gu},
  booktitle = {ASPLOS},
  year = {2019},
  doi = {10.1145/3297858.3304029}
}