3 papers
cs.DC2026
Efficient Training on Multiple Consumer GPUs with RoundPipe
Yibin Luo, Shiwei Gao, Huichuan Zheng +2
Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe interconnects. Pipeline parallelism co…
cs.DC2025
AES-SpMM: Balancing Accuracy and Speed by Adaptive Edge Sampling Strategy to Accelerate SpMM in GNNs
Yingchen Song, Yaobin Wang, Yi Luo +2
Coordinating the design of sampling and sparse-dense matrix multiplication (SpMM) is crucial for accelerating graph neural networks (GNNs). However, due to irrational sampling stra…
cs.AR2025
DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System
Yi Luo, Yaobin Wang, Qi Wang +4
Graph Neural Networks (GNNs) are powerful tools for processing graph-structured data, increasingly used for large-scale real-world graphs via sampling-based inference methods. Howe…