arXiv subjects

Lingling Zhang

Publications and source records attributed to Lingling Zhang.

3 recordsLinked to original sources

arXiv2026

Dual-Metric Partitioning with Adaptive Kernel Execution for Efficient GCN Acceleration

Graph Convolutional Networks (GCNs) are widely used for large graph-structured data, including social, citation, and e-commerce networks, but their deployment is constrained by irregular memory access and severe GPU workload imbalance. These challenges arise in two dimensions: width imbalance from power-law degree distributions and depth imbalance from heterogeneous neighborhood connectivity.We present DualGCN, a GPU acceleration framework addressing both dimensions through dual-metric graph partitioning and adaptive kernel execution. DualGCN combines node degree, reflecting aggregation width, with neighborhood density estimated by anonymous random walks, capturing multihop connectivity and access depth. This hybrid workload metric enables connectivity-aware partitioning of large graphs into sparse and dense regions while reducing workload imbalance from linear to logarithmic complexity. DualGCN then selects partition-specific execution strategies: sparse partitions use warp-level parallelism and coalesced memory access, whereas dense partitions exploit instruction-level parallelism to hide latency and improve GPU utilization. Experiments on twelve real-world graph datasets show that DualGCN consistently accelerates GCN computation, achieving average speedups of 2.53x, 3.8x, and 2.13x over cuSPARSE, GNNAdvisor, and ACCEL, respectively. These results demonstrate that jointly optimizing graph partitioning and kernel execution provides an effective solution for processing large-scale graph and socialnetwork workloads.

cs.DB↗

arXiv2026

From Rollouts to Recipes: Self-Contained Post-Training for LLMs

Post-training large language models usually applies a single training recipe to all samples, even though the model's own rollouts reveal different sample-level learning states. We propose Self-Routing, a behavior-conditioned post-training framework that uses rollout correctness and confidence to decide how each sample should be optimized. Depending on its behavior state, a sample is routed to GRPO, on-policy self-distillation, regularization, or skipping, allowing training to adapt without external teachers, extra annotations, or additional sampling. Experiments on mathematical reasoning across Qwen3 and Qwen3.5 backbones show that Self-Routing consistently improves over uniform GRPO, uniform OPSD, fixed mixtures, and simpler routing baselines. Further analyses show that the routing distribution changes over training and reduces unnecessary updates on low-signal or already stable samples.

cs.CL↗

arXiv2026

Self-Specialized Teachers for Domain Post-Training

Target-only post-training can improve performance in a specialized domain while degrading behaviors that a general-purpose base model acquired before adaptation. We study this problem when target-domain data are available but a representative replay corpus is not. We propose self-specialized teacher distillation (SSTD), a two-stage procedure that first trains a copy of the base model into a domain teacher, then distills its token distribution to a student on prefixes sampled from the student itself. Teacher training combines standard target supervision with base-aware key-token weighting and distribution alignment to the frozen base model; on-policy distillation then places domain feedback on states the student can encounter at inference time. On financial numerical reasoning, medical question answering, and legal holding identification, SSTD retains much of the target improvement of direct fine-tuning while improving the mean score on the evaluated general suite by 4.8--5.0 points at the reported operating point. The pattern persists across Qwen3 sizes and on Gemma backbones. SSTD requires neither an external teacher nor general replay data.

cs.AI↗