arXiv subjects

Zhengan Yao

Publications and source records attributed to Zhengan Yao.

7 recordsLinked to original sources

arXiv2026

MERBIT: A GPU-Based SpMV Method for Iterative Workloads

Sparse Matrix-Vector Multiplication (SpMV) is the cornerstone in many iterative workloads, including large-scale graph analytics and sparse iterative solvers. Accelerating SpMV on real-world graphs remains challenging due to highly irregular sparsity patterns. In this paper, we propose MERBIT, a GPU SpMV method designed for repeated SpMV on irregular, graph-like sparse matrices, with PageRank as a representative motivating workload. MERBIT combines two key ideas from existing GPU SpMV methods. At the global level, it uses merge-path partitioning to balance work over nonzeros and row boundaries. At the local level, it encodes each merge-path segment using a compact bit-field descriptor. MERBIT improves workload balance and promotes coalesced memory access for both matrix loading and output writes; moreover, three optimization strategies are incorporated to further enhance performance. Experiments on 50 large irregular datasets demonstrate that MERBIT outperforms competitive baselines, including cuSPARSE, Ginkgo, and academic approaches, achieving average speedups of 1.27 and 1.25 over cuSPARSE COO in single and double precision, respectively.

cs.DC↗

arXiv2026

FedFG: Privacy-Preserving and Robust Federated Learning via Flow-Matching Generation

Federated learning (FL) enables distributed clients to collaboratively train a global model using local private data. Nevertheless, recent studies show that conventional FL algorithms still exhibit deficiencies in privacy protection, and the server lacks a reliable and stable aggregation rule for updating the global model. This situation creates opportunities for adversaries: on the one hand, they may eavesdrop on uploaded gradients or model parameters, potentially leaking benign clients' private data; on the other hand, they may compromise clients to launch poisoning attacks that corrupt the global model. To balance accuracy and security, we propose FedFG, a robust FL framework based on flow-matching generation that simultaneously preserves client privacy and resists sophisticated poisoning attacks. On the client side, each local network is decoupled into a private feature extractor and a public classifier. Each client is further equipped with a flow-matching generator that replaces the extractor when interacting with the server, thereby protecting private features while learning an approximation of the underlying data distribution. Complementing the client-side design, the server employs a client-update verification scheme and a novel robust aggregation mechanism driven by synthetic samples produced by the flow-matching generator. Experiments on MNIST, FMNIST, and CIFAR-10 demonstrate that, compared with prior work, our approach adapts to multiple attack strategies and achieves higher accuracy while maintaining strong privacy protection.

cs.CR↗

arXiv2023

Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance.

cs.AI↗

arXiv2023

A Parallel PageRank Algorithm For Undirected Graph

As a measure of vertex importance according to the graph structure, PageRank has been widely applied in various fields. While many PageRank algorithms have been proposed in the past decades, few of them take into account whether the graph under investigation is directed or not. Thus, some important properties of undirected graph\textemdash symmetry on edges, for example\textemdash is ignored. In this paper, we propose a parallel PageRank algorithm specifically designed for undirected graphs that can fully leverage their symmetry. Formally, our algorithm extends the Chebyshev Polynomial approximation from the field of real function to the field of matrix function. Essentially, it reflects the symmetry on edges of undirected graph and the density of diagonalizable matrix. Theoretical analysis indicates that our algorithm has a higher convergence rate and requires less computation than the Power method, with the convergence rate being up to 50\% higher with a damping factor of $c=0.85$. Experiments on six datasets illustrate that our algorithm with 38 parallelism can be up to 39 times faster than the Power method.

cs.NI↗

arXiv2023

An Advanced Parallel PageRank Algorithm

Initially used to rank web pages, PageRank has now been applied in many fields. In general case, there are plenty of special vertices such as dangling vertices and unreferenced vertices in the graph. Existing PageRank algorithms usually consider them as `bad` vertices since they may take troubles. However, in this paper, we propose a parallel PageRank algorithm which can take advantage of these special vertices. For this end, we firstly interpret PageRank from the information transmitting perspective and give a constructive definition of PageRank. Then, based on the information transmitting interpretation, a parallel PageRank algorithm which we call the Information Transmitting Algorithm(ITA) is proposed. We prove that the dangling vertices can increase ITA's convergence rate and the unreferenced vertices and weak unreferenced vertices can decrease ITA's calculations. Compared with the MONTE CARLO method, ITA has lower bandwidth requirement. Compared with the power method, ITA has higher convergence rate and generates less calculations. Finally, experimental results on four data sets demonstrate that ITA is 1.5-4 times faster than the power method and converges more uniformly.

cs.NI↗

arXiv2023

Two Parallel PageRank Algorithms via Improving Forward Push

Initially used to rank web pages, PageRank has now been applied in many fields. With the growing scale of graph, accelerating PageRank computing is urged and designing parallel algorithm is a feasible solution. In this paper, two parallel PageRank algorithms IFP1 and IFP2 are proposed via improving the state-of-the-art Personalized PageRank algorithm, i.e., Forward Push. Theoretical analysis indicates that, IFP1 can take advantage of the DAG structure of the graph, where the dangling vertices improves the convergence rate and the unreferenced vertices decreases the computation amount. As an improvement of IFP1, IFP2 pushes mass to the dangling vertices only once but rather many times, and thus decreases the computation amount further. Experiments on six data sets illustrate that both IFP1 and IFP2 outperform Power method, where IFP2 with 38 parallelism can be at most 50 times as fast as the Power method.

cs.DS↗

arXiv2013

Regularity criteria for the 3D MHD equations in term of velocity

In this paper we consider three-dimensional incompressible magnetohydrodynamics equations. By using interpolation inequalities in anisotropic Lebesgue space, we provide regularity criteria involving the velocity or alternatively involving the fractional derivative of velocity in one direction, which generalize some known results.

math.AP↗