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With the huge demand for graph analytics in many real-world applications, massive iterative graph processing jobs are concurrently performed on the same graphs and suffer from significant high data access cost. To lower the data access cost toward high performance, several out-of-core concurrent graph processing solutions are recently designed to handle concurrent jobs by enabling these jobs to share the accesses of the same graph data. However, the set of active vertices in each partition are usually different for various concurrent jobs and also evolve with time, where some high-degree ones (or called hub-vertices) of these active vertices require more iterations to converge due to the power-law property of real-world graphs. In consequence, existing solutions still suffer from much unnecessary I/O traffic, because they have to entirely load each partition into the memory for concurrent jobs even if most vertices in this partition are inactive and may be shared by a few jobs. In this paper, we propose an efficient structure-aware storage system, called GraphSO, for higher throughput of the execution of concurrent graph processing jobs. It can be integrated into existing out-of-core graph processing systems to promote the execution efficiency of concurrent jobs with lower I/O overhead. The key design of GraphSO is a fine-grained storage management scheme. Specifically, it logically divides the partitions of existing graph processing systems into a series of small same-sized chunks. At runtime, these small chunks with active vertices are judiciously loaded by GraphSO to construct new logical partitions (i.e., each logical partition is a subset of active chunks) for existing graph processing systems to handle, where the most-frequently-used chunks are preferentially loaded to construct the logical partitions and the other ones are delayed to wait to be required by more jobs. In this way, it can effectively spare the cost of loading the graph data associated with the inactive vertices with low repartitioning overhead and can also enable the loaded graph data to be fully shared by concurrent jobs. Moreover, GraphSO also designs a buffering strategy to efficiently cache the most-frequently-used chunks in the main memory to further minimize the I/O traffic by avoiding repeated load of them. Experimental results show that GraphSO improves the throughput of GridGraph, GraphChi, X-Stream, DynamicShards, LUMOS, Graphene, and Wonderland by 1.4-3.5 times, 2.1-4.3 times, 1.9-4.1 times, 1.9-2.9 times, 1.5-3.1 times, 1.3-1.5 times, and 1.3-2.7 times after integrating with them, respectively. [ABSTRACT FROM AUTHOR]

For large-scale graph analysis on a single PC, asynchronous processing methods are known to converge more quickly than the synchronous approach, because of more efficient propagation of vertices state. However, current asynchronous methods are still very suboptimal in propagating state across different graph partitions. This presents a bottleneck for cross-partition state update and slows down the convergence of the processing task. To tackle this problem, we propose a new method, named the HotGraph, to faster graph processing by extracting a backbone structure, called hot graph, that spans all the partitions of the original graph. With this approach, most cross-partition state propagations in traditional solutions now take place within only a few hot graph partitions, thus removing the cross-partition bottleneck. We also develop a partition scheduling algorithm to maximize the hot graph’s effectiveness by keeping it in memory and assigning it the highest priority for processing as much as possible. A forward and backward sweeping execution strategy is then proposed to further accelerate the convergence. Experimental results show that HotGraph can reduce the number of vertex state updates processed by 51.5 percent, compared with state-of-the-art schemes. Applying our optimizations further reduces this number by 72.6 percent and the execution time by 80.8 percent. [ABSTRACT FROM AUTHOR]