3 citations · 3 across the 5 of their papers we have counts for
5 papers
Optimizing Long-context LLM Serving via Fine-grained Sequence Parallelism
Cong Li, Yuzhe Yang, Xuegui Zheng +7
With the advancement of large language models (LLMs), their context windows have rapidly expanded. To meet diverse demands from varying-length requests in online services, existing…
Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio
Zhaoyang Du, Yijin Guan, Tianchan Guan +7
Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structur…
OpSparse: a Highly Optimized Framework for Sparse General Matrix Multiplication on GPUs
Zhaoyang Du, Yijin Guan, Tianchan Guan +4
Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM's inherent irregularity and the vas…
Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging
Zhaoyang Du, Yijin Guan, Tianchan Guan +3
Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast an…
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
Zhe Zhou, Bizhao Shi, Zhe Zhang +3
In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level repre…