8 papers
SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System
Shiyang Li, Guangyan Sun, Jinwei Tang +3
Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning. Their GPU performance depends strongly on the input sparsity pattern and execu…
CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging
Shiyang Li, Haoyang Chen, Mattia Fazzini +1
Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, memory hierarchy, and asynchron…
CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
Shiyang Li, Zijian Zhang, Guangyan Sun +5
Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short of expert-level, architectu…
FPTC: A Fast Parallel Transform-based Codec for Efficient Asymmetric Signal Compression
Ben Mechels, Ryan Billmeyer, Alexander Chen +2
Modern high-performance computing and Internet-of-Things deployments increasingly generate large volumes of signal data that must be compressed efficiently on resource-constrained…
GSR-GNN: Training Acceleration and Memory-Saving Framework of Deep GNNs on Circuit Graph
Yuebo Luo, Shiyang Li, Yifei Feng +3
Graph Neural Networks (GNNs) show strong promise for circuit analysis, but scaling to modern large-scale circuit graphs is limited by GPU memory and training cost, especially for d…
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning
Shiyang Li, Zijian Zhang, Winson Chen +3
Modern machine learning (ML) workloads increasingly rely on GPUs, yet achieving high end-to-end performance remains challenging due to dependencies on both GPU kernel efficiency an…