Graph pattern mining is essential for analyzing dynamic networks, where graphs evolve over time. To accommodate these changes, existing solutions update match sets incrementally, avoiding the need to re-mine the entire graph and achieving significant performance improvements. However, these methods suffer from inefficiencies due to redundant set intersection operations across subgraph instances, causing performance degradation.

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency improvements. In this article, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains . These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63×.

Graph Convolutional Networks (GCNs) hold great promise in facilitating machine learning on graph-structured data. However, the sparsity of graphs often results in a significant number of irregular memory accesses, leading to inefficient data movement for existing GCNs accelerators. With the advancement of 3D stacked technology, the processing-in-memory (PIM) architecture has emerged as a promising solution for graph processing. Nevertheless, existing PIM accelerators are confronted with the challenges of irregular remote access in the aggregation phase of GCNs and dynamic workload variations between phases. In this paper, we present GCNim, a PIM accelerator based on 3D stacked memory, which features two key innovations in terms of the computation model and hardware designs. First, we present a PIM-based hybrid computation model, which employs a remote merging strategy to achieve the outer product in aggregation and the row-wise product in combination. Second, GCNim builds a three-stage aggregation and combination pipeline and integrates unified processing elements (PEs) supporting these three stages at the bank level, achieving load balance among PEs through a lightweight data placement algorithm. Compared with the state-of-the-art software frameworks running on CPUs and GPUs, GCNim achieves an average speedup of 3,736.06× and 76.56×, respectively. Moreover, GCNim outperforms the state-of-the-art GCN hardware accelerators, I-GCN, PEDAL, FlowGNN, and GCIM, with average speedups of 3.35×, 8.97×, 2.24×, and 5.58×, respectively.

The memory-intensive embedding layers of the personalized recommendation systems are the performance bottleneck as they demand large memory bandwidth and exhibit irregular and sparse memory access patterns. Recent studies propose near memory processing (NMP) to accelerate memory-bound embedding operations. However, due to the load imbalance caused by the skewed access frequency of the embedding data, existing NMP solutions that exploit fine-grained memory parallelism fail to translate the increasingly massive internal bandwidth to performance improvements, leading to resource underutilization and hardware overhead.

Beyond ordinary graphs, hypergraphs are a graph representation to flexibly express complex multilateral relationships between entities. Hypergraph processing can be used to solve many real-world problems, e.g., machine learning, VLSI design, and image retrieval. Existing hypergraph processing systems handle a hypergraph in order of its hyperedge and vertex indices. This makes processing hypergraphs on generalpurpose architectures suffer significantly from excessive offchip memory accesses, most of which however are redundant in frequently accessing overlapped hyperedges and vertices, but the index-ordered scheduling destroys this potential locality.In this paper, we propose a novel Generate-Load-Apply (GLA) execution model to improve locality in hypergraph processing. The key insight of GLA is to use a concept of chain to characterize the overlapped feature of a hypergraph, exposing data reuse opportunities missed in existing hypergraph systems. The precondition of driving GLA model is to generate expected chains on the fly, but the software solution is so expensive that its overheads may outweigh the benefits achieved from the chain-driven scheduling. We further present ChGraph, the first hardware-accelerated hypergraph processing engine near each core. ChGraph is specialized in accelerating the chain generation and the chain-guided data loading (to hide memory access latency) while the general-purpose cores are responsible only for handling the apply operations of GLA. We evaluate ChGraph against a state-of-the-art hypergraph processing system Hygra on six hypergraph algorithms using five large real-world hypergraphs. Results on a simulated 16core system show that ChGraph reduces the number of offchip memory accesses by up to 4.56× and achieves up to 4.73× speedup while introducing only 0.26% area overhead.