Congra: Towards Efficient Processing of Concurrent Graph Queries on Shared-Memory Machines

Parallel graph processing has been routinely used to solve problems in a wide range of real life applications today. Efficiently handling many concurrent graph processing queries in a multi-user environment is highly desirable as we enter a world full of edge device oriented services. Existing research, however, primarily focuses on processing a single, large graph dataset and leaves the efficient handling of multiple mid-sized graph queries an interesting yet challenging open problem. In this work, we investigate the management of multiple graph processing queries on shared-memory machines. We carefully analyze the performance of several graph algorithms and find some of them do not scale well with more CPU cores (more threads) while others have diminishing returns as the number of CPU cores grow. Further, we identify the trade-off relationship between the increasing cost of atomic operations and the growing available memory bandwidth with more CPU cores. Motivated by the above observations, we propose Congra, a dynamic graph scheduler that intelligently manages multiple concurrent graph queries for better system throughput and resource efficiency. Congra collects the memory bandwidth consumption and atomic operations characteristics of graph queries via offline profiling and decides which subset of CPU cores for the query to run, as long as the query is not blocked due to heavy resources contention. We implement Congra in C++ on top of the Ligra graph processing framework and test it with judiciously selected graph processing query combinations. Our results show the Congra improves query throughput by 60% compared to existing designs. It exhibits much better quality of service and readily supports a scale-out computing environment.