2 citations · 4 across the 10 of their papers we have counts for
14 papers
DB-SpMSpV: Dual-View Blocked Sparse Matrix-Sparse Vector Multiplication for Dynamic GPU Workloads
Xing Cong, Chenhao Xie, Rui Wang +3
Sparse Matrix-Sparse Vector Multiplication (SpMSpV) is a core primitive in graph traversal, sparse linear algebra, and sparse model inference. Its input vector is often dynamically…
Toward Auto-Research: Mining Falsifiable Research Ideas from Paper Knowledge Graphs with Categorical Structure
Yuchen Wang, Zhongzhi Luan
Automated research-idea generation systems built on large language models (LLMs) share a structural weakness: they reduce ideation to free-text recombination, random paper pairing,…
Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning
Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang +5
Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented u…
RATrain: A Resource-Aware Training Runtime for Large Language Models on Bandwidth-Constrained Heterogeneous Supercomputing Platforms
Yao Lu, Shiqing Ma, Zhongzhi Luan +5
Production heterogeneous supercomputing platforms are increasingly used to host large language model (LLM) training workloads. However, existing GPU-oriented training runtimes typi…
PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization
Kelun Lei, Hailong Yang, Huaitao Zhang +5
Designing high-performance kernels requires expert-level tuning and a deep understanding of hardware characteristics. Recent advances in large language models (LLMs) have enabled a…
LOw-cOst yet High-Performant Sparse Matrix-Matrix Multiplication on Arm SME Architectures
Kelun Lei, Hailong Yang, Kaige Zhang +8
Sparse matrix-dense matrix multiplication (SpMM) is a critical kernel in both scientific computing and emerging graph learning workloads. The recent Armv9 architecture introduces S…