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PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Genghan Zhang, Yixin Dong, Chengze Fan, Zhichen Zeng, Yueming Yuan, Shaowei Zhu, Kunle Olukotun

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Tags: Benchmarking, Computer science, LLM, nVidia, nVidia B200, nVidia H100, Package, PTX

August 30, 2026 by hgpu

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

Matt J. Borowski, Blazej Osinski

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Tags: Computer science, nVidia, nVidia L4, Package, PTX

August 17, 2026 by hgpu

CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution

Zihao Ye, Yingyi Huang, Hongyi Jin, Bohan Hou, Junru Shao, Zhongming Yu, Jinqi Chen, Meghan Cowan, Shiyi Cao, Shanli Xing, Hanfeng Chen, Vinod Grover, Tianqi Chen, Luis Ceze

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Tags: Compilers, Computer science, CUDA, nVidia, PTX

August 17, 2026 by hgpu

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra

Teng-Ruei Chen

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Tags: Computer science, nVidia, nVidia B300, PTX, Triton

August 17, 2026 by hgpu

Real FP4 Tensor-Core Code in Pure Rust on a Gaming GPU – with NVIDIA’s Own Compiler

Carter Richardson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 5070 Ti, PTX, Rust

July 13, 2026 by hgpu

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

Yanbo Zhao, Zhaonan Meng, Sai Krishna Teja Varma Manthena, Xu Liu, Ajay Panyala, Jiajia Li

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Tags: AMD, AMD Radeon Instinct MI300X, Computer science, DSL, HPC, nVidia, nVidia GeForce RTX 4090, nVidia H100, PTX, Python, ROCm, Triton

June 17, 2026 by hgpu

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

Yee Hin Chong, Jiaming Wu, Youhui Zhang, Peng Qu

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Tags: Computer science, CUDA, Heterogeneous systems, LLM, nVidia, Package, PTX

June 8, 2026 by hgpu

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

Taras Sereda, Burak Bartan, Ankita Nayak, Tom St.John, Natalie Serrino, Zain Asgar

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Intel, Intel Arc B580, nVidia, nVidia B200, PTX, Triton

June 8, 2026 by hgpu

Equivalence Checking of ML GPU Kernels

Kshitij Dubey, Benjamin Driscoll, Anjiang Wei, Neeraj Kayal, Rahul Sharma, Alex Aiken

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Tags: Computer science, CUDA, Deep learning, LLM, Machine learning, Matrix multiplication, nVidia, PTX

January 12, 2026 by hgpu

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Nathan Yan, Xiayu Yu, Zhou Fang, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu

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Tags: AI, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, nVidia H100, PTX, ROCm, Triton

January 4, 2026 by hgpu

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Yaoyao Ding, Bohan Hou, Xiao Zhang, Allan Lin, Tianqi Chen, Cody Hao Yu, Yida Wang, Gennady Pekhimenko

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia H100, nVidia L40s, Package, Programming Languages, PTX, Triton

December 29, 2025 by hgpu

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

Aaron Jarmusch, Sunita Chandrasekaran

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Tags: Benchmarking, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia B200, nVidia H200, PTX

December 7, 2025 by hgpu

high performance computing on graphics processing units: hgpu.org

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4

CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra

Real FP4 Tensor-Core Code in Pure Rust on a Gaming GPU – with NVIDIA’s Own Compiler

Leveraging AI Ecosystem for Portable and Sustainable GPU Kernels in HPC

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation

KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators

Equivalence Checking of ML GPU Kernels

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Microbenchmarking NVIDIA’s Blackwell Architecture: An in-depth Architectural Analysis

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