Graph Pattern Matching (GPM) is a critical task in a wide range of graph analytics applications, such as social network analysis and cybersecurity.Despite its importance, GPM remains challenging to accelerate due to its inherently irregular control flow and heavy reliance on set operations, which dominate execution time and introduce data dependencies that limit parallelism.While recent GPM accelerators attempt to improve performance, they often overlook the ordered nature of input data, resulting in redundant computations and inefficient hardware utilization.This paper presents X-SET, a GPM accelerator that overcomes these limitations by introducing two key innovations.First, we propose an Order-Aware Set Intersection Unit (SIU), which exploits input ordering to reduce the hardware complexity of parallel set intersection from O (𝑁 2 ) to O (𝑁 log 𝑁 ), achieving high throughput and significant area savings by avoiding unnecessary comparisons.Second, we develop a barrier-free task scheduler that breaks traditional DFS scheduling constraints by enabling asynchronous, outof-order task execution across different levels of the GPM search tree.X-SET is integrated into a RISC-V SoC, supporting end-to-end acceleration.Extensive experimental results show that X-SET outperforms state-of-the-art GPM accelerators, achieving 4.6×-142.9×improvements in compute density, with a geometric mean of 13.7×, and delivering 6.4× geometric mean and 42.9× maximum speedup in end-to-end performance.X-SET is open-sourced at github 1 .

Graph mining is of critical use in a number of fields such as social networks, knowledge graphs, and fraud detection. As an NP-complete problem, accelerating computation performance is the main target for current optimizations. Due to excellent performance, state-of-the-art graph mining systems mainly rely on pattern-aware algorithms. Despite previous efforts, complex control flows introduced by pattern-aware algorithms bring significant overhead and also impede further acceleration on heterogeneous hardware.

Large-scale pretrained AI models have shown state-of-the-art accuracy in a series of important applications. As the size of pretrained AI models grows dramatically each year in an effort to achieve higher accuracy, training such models requires massive computing and memory capabilities, which accelerates the convergence of AI and HPC. However, there are still gaps in deploying AI applications on HPC systems, which need application and system co-design based on specific hardware features.

Graph pattern matching, which aims to discover structural patterns in graphs, is considered one of the most fundamental graph mining problems in many real applications. Despite previous efforts, existing systems face two main challenges. First, inherent symmetry existing in patterns can introduce a large amount of redundant computation. Second, different matching orders for a pattern have significant performance differences and are quite hard to predict. When these factors are mixed, this problem becomes extremely complicated. High efficient pattern matching remains an open problem currently. To address these challenges, we propose GraphPi, a high performance distributed pattern matching system. GraphPi utilizes a new algorithm based on 2-cycles in group theory to generate multiple sets of asymmetric restrictions, where each set can eliminate redundant computation completely. We further design an accurate performance model to determine the optimal matching order and asymmetric restriction set for efficient pattern matching. We evaluate GraphPi on Tianhe-2A supercomputer. Results show that GraphPi outperforms the state-of-the-art system, by up to $ 105\times$ for 6 real-world graph datasets on a single node. We also scale GraphPi to 1,024 computing nodes (24,576 cores).