12 papers
GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks
Yice Luo, Yun Zhu, Xi Chen +7
Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not meet the interpretability and d…
RVANNS: Mixed-Precision Indexing and Locality-Aware Graph Traversal on RISC-V
Chengying Huan, Yudong Liu, Jianguo Wang +9
Approximate nearest neighbor search (ANNS) on CPUs is increasingly constrained by candidate-vector movement and decoding rather than peak arithmetic throughput. Although the RISC-V…
Bole: Efficient Tree Speculation for Hybrid-Attention Language Models
Li Wang, Yi Su, Xiabao Wu +9
Hybrid-attention large language models combine full attention with recurrent linear attention to reduce long-context inference costs, yet their autoregressive decoding remains memo…
TIDE-MC: Two-Sided Interpolative Decomposition for Billion-Scale GPU Matrix Completion
Chengying Huan, Yubo Wang, Pinhuan Wang +11
Matrix completion supports large-scale recommendation and scientific computing, yet existing GPU solvers commonly assume that the observed matrix or its dense factors fit in device…
GNStor: Design of GPU-Native High-Performance Remote All-Flash Array
Shushu Yi, Wenbo Wu, Guoci Chen +6
GPU has become the leading computing device for a wide range of data-intensive applications, which tightly collaborates with remote all-flash array (AFA) to accommodate ever-expand…
GPU Acceleration of TFHE-Based High-Precision Nonlinear Layers for Encrypted LLM Inference
Guoci Chen, Xiurui Pan, Qiao Li +5
Deploying large language models (LLMs) as cloud services raises privacy concerns as inference may leak sensitive data. Fully Homomorphic Encryption (FHE) allows computation on encr…
OrchANN: Hierarchical Orchestration for Skewed Out-of-Core Vector Search
Lizheng Chen, Pinhuan Wang, Shaonan Ma +9
At billion scale, approximate nearest neighbor search (ANNS) often becomes an out-of-core problem: the full vector collection and index structures exceed memory capacity, making qu…
Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Serving
Chengying Huan, Ziheng Meng, Yongchao Liu +11
Graph Chain-of-Thought (Graph-CoT) enables large language models (LLMs) to perform step-by-step reasoning over graph-structured knowledge, but existing pipelines suffer from low ac…
STAR: Decode-Phase Rescheduling for LLM Inference
Zhibin Wang, Zetao Hong, Xue Li +8
Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly f…
Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding
Zhibin Wang, Zhonghui Zhang, Yuhang Zhou +8
Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been pr…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
Tango: rethinking quantization for graph neural network training on GPUs
Shiyang Chen, Da Zheng, Caiwen Ding +3
Graph Neural Networks (GNNs) are becoming increasingly popular due to their superior performance in critical graph-related tasks. While quantization is widely used to accelerate GN…