8 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
PTXBench: Benchmarking and Adapting LLMs for GPU Kernel Optimization with Architecture-specific PTX
Genghan Zhang, Yixin Dong, Chengze Fan +4
We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures function…
Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel
Hongyi Jin, Bohan Hou, Guanjie Wang +18
Modern GPU workloads, especially large language model (LLM) inference, suffer from kernel launch overheads and coarse synchronization that limit inter-kernel parallelism. Recent me…
AdaExplore: Failure-Driven Adaptation and Diversity-Preserving Search for Efficient Kernel Generation
Weihua Du, Jingming Zhuo, Yixin Dong +9
Recent large language model (LLM) agents have shown promise in using execution feedback for test-time adaptation. However, robust self-improvement remains far from solved: most app…
XGrammar-2: Dynamic and Efficient Structured Generation Engine for Agentic LLMs
Linzhang Li, Yixin Dong, Guanjie Wang +3
Modern LLM agents increasingly rely on dynamic structured generation, such as tool calling and response protocols. Unlike traditional structured generation with static structures,…
FlashInfer-Bench: Building the Virtuous Cycle for AI-driven LLM Systems
Shanli Xing, Yiyan Zhai, Alexander Jiang +10
Recent advances show that large language models (LLMs) can act as autonomous agents capable of generating GPU kernels, but integrating these AI-generated kernels into real-world in…