Heterogeneous Graph Neural Networks (HGNNs) are pivotal for extracting semantic and structural information from heterogeneous graphs. Traditional HGNN implementations often grapple with the challenges of excessive metapath instances, requiring substantial storage or incurring high instance-matching overhead. These methods typically suffer from redundant instance encoding and costly semantic graph construction. Addressing these issues, we introduce an advanced Metapath Graph (MG) abstraction that encapsulates the structural information of all metapath instances within a compact representation. This approach significantly reduces storage demands, eliminates redundant instance encodings, and foregoes the need for constructing semantic graphs, thereby facilitating rapid HGNN inference. Our software-based system, MetaHG, leverages layerwise encoding and aggregation to avoid redundancies without the necessity of semantic graphs. It incorporates a fast, lightweight partitioning method to efficiently manage large graphs. Distinctively, MetaHG seamlessly integrates with both dynamic HGNNs and homogeneous GNNs, unlike conventional systems. Comparative evaluations demonstrate that MetaHG surpasses the state-of-the-art BFS- and DFS-based HGNN systems, MAGNN and the software implementation of MetaNMP, by 42.5× and 4.53×, respectively, on average.

The emerging Mixture-of-Experts (MoE) model mitigates the high compute cost of large-scale LLMs by sparsely activating a subset of experts during inference. However, MoE requires storing massive expert parameters, creating a severe memory bottleneck on resource-constrained GPUs. Existing approaches offload parameters to host memory and prefetch activated experts to GPU memory with sophisticated policies, but these solutions are tailored to single-batch inference and suffer from communication bottlenecks at larger batch sizes, limiting throughput. We identify two forms of locality in expert activation: a small set of experts are frequently invoked across inference (global locality), while others recur within short decoding bursts (temporal locality). To exploit this, we propose Diff-MoE, which introduces a differential cache hierarchy in GPU memory. Globally hot experts reside in per-layer high-priority caches, locally hot ones are dynamically managed in per-layer medium-priority caches under a priority-driven replacement policy, and the remaining cold experts are cached temporarily and evicted on demand. Moreover, Diff-MoE incorporates a lightweight predictor that prefetches experts likely needed in the next MoE layer, overlapping migration with computation to further reduce latency. Our evaluation shows that Diff-MoE improves inference throughput by 2.74 ×, 2.22 ×, and 1.55 × over DeepSpeed, Pre-gated MoE, and MoE-Infinity, respectively.

In the era of big data, data mining algorithms usually handle plenty of data and the data may involve sensitive information about people in some scenarios, so privacy protection becomes an important task. Support Vector Machine (SVM) is a popular data mining algorithm widely used in various fields. In this paper, we mainly study the problem of privacy protection during SVM classification. Nowadays, lots of privacy-preserving SVM approaches have been proposed, but many of them have some flaws. Negative database (NDB) is a new type of data representation, which stores the complementary information of data. It is considered as a promising technique for privacy-preserving data mining, since reversing NDB has been proven as an NP-hard problem. Therefore, we propose a privacy-preserving SVM method based on NDBs. We also design a model for estimating the results of the dot product from NDBs, which is the basis of SVM kernel functions. Private data are converted into NDBs before being transmitted to other parties, and thus privacy is protected. Then SVM classification can be effectively performed using the proposed estimating model. We conduct several experiments on five public datasets, and the results demonstrate the effectiveness of the proposed method.