Taijigraph: an Out-Of-Core Graph Processing System Enhanced with Computational Storage

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Xinmiao Zhang, Cheng Liu, Shengwen Liang, Hayden Kwok-Hay So, Ying Wang, Lei Zhang, Huawei Li, Xiaowei Li

Published: 01 Jan 2025, Last Modified: 04 Nov 2025IPDPS 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Out-of-core graph processing systems are severely bottlenecked by I/O to the external storage because of the low compute-to-I/O ratio and the substantial amount of irregular data accesses. In order to alleviate the I/O bottleneck, prior works either focus on improving the bandwidth utilization by converting random I/O requests into sequential ones, or improving the data utilization by fetching only the required data to avoid the I/O redundancy. However, the former usually loads massive unused data, while the latter can induce frequent finegrained I/O requests, wasting the parallelism of the I/O channels and leading to under-utilization of the limited I/O bandwidth. Different from prior works, we systematically explore the use of computational storage devices (CSDs), which offer in-storage computing facilities with higher I/O bandwidth, to improve both the bandwidth utilization and data utilization for higher I/O efficiency. Specifically, we first introduce a graph-semanticaware data organization to enable the loading of only active graph partitions at the granularity of a physical page, reducing redundant I/O and enhancing data utilization. Additionally, we propose to coalesce parallel I/O requests of graph partitions distributed across different flash dies to maximize the parallelism of internal I/O channels, thereby fully utilizing the internal I/O bandwidth of CSDs. In addition, we capture the dynamic status of graph processing tasks across the iterations and partitions at runtime to dynamically offload I/O-intensive workloads into the instorage processors with restricted computing resources but higher I/O bandwidth to further improve the I/O efficiency. With the above techniques, we implement an out-of-core graph processing system prototype, namely TaijiGraph, on an open-channel CSD. According to our experiments on a set of representative graph datasets and algorithms, TaijiGraph achieves average speedups of $2.43 \times, 3.81 \times, 2.21 \times$ and $7.89 \times$, respectively, when compared to state-of-the-art out-of-core graph processing systems including GridGraph, LUMOS, Blaze, and GraphSSD.