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Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

Le Chen1, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia H200

December 21, 2025 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Songqiao Su, Xiaofei Sun, Xiaoya Li, Albert Wang, Jiwei Li, Chris Shum

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Tags: Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia A100, Package

December 21, 2025 by hgpu

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

Jinwu Chen, Qidie Wu, Bin Li, Lin Ma, Xin Si, Yang Hu, Shouyi Yin, Jun Yang

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Tags: AI, Code generation, Computer science, CUDA, nVidia, nVidia A100, Package, PyTorch

December 21, 2025 by hgpu

ML Inference Scheduling with Predictable Latency

Haidong Zhao, Nikolaos Georgantas

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Tags: Computer science, Machine learning, nVidia, nVidia L4, Task scheduling

December 21, 2025 by hgpu

PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks & Fast Storage

Jigao Luo, Nils Boeschen, Muhammad El-Hindi, Carsten Binnig

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Tags: Computer science, CUDA, Databases, GPU cluster, nVidia, nVidia A100, nVidia DGX-1, PyTorch

December 21, 2025 by hgpu

Accurate Models of NVIDIA Tensor Cores

Faizan A. Khattak, Mantas Mikaitis

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia B200, nVidia H100, nVidia V100, Package

December 15, 2025 by hgpu

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Shruti Dongare, Redwan Ibne Seraj Khan, Hadeel Albahar, Nannan Zhao, Diego Melendez Maita, Ali R. Butt

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Tags: Computer science, Deep learning, GPU cluster, nVidia, nVidia H100, Package, Task scheduling

December 15, 2025 by hgpu

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

Marco Kurzynski, Shaizeen Aga, Di Wu

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Tags: AMD, AMD Radeon Instinct MI300X, ATI, Benchmarking, Computer science, Data parallelism, LLM, ROCm

December 15, 2025 by hgpu

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Haonan Li, Keyu Man, Partha Kanuparthy, Hanning Chen, Wei Sun, Sreen Tallam, Chenguang Zhu, Kevin Zhu, Zhiyun Qian

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Tags: Computer science, CUDA, LLM, Machine learning, nVidia, nVidia H100, Package, PyTorch, Triton

December 15, 2025 by hgpu

Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles

Zitong Zhou, Ben Limpanukorn, Hong Jin Kang, Jiyuan Wang, Yaoxuan Wu, Akos Kiss, Renata Hodovan, Miryung Kim

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Tags: Compilers, Computer science, LLVM, Optimization

December 7, 2025 by hgpu

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

Ryan Swann, Muhammad Osama, Xiaohu Guo, Bryant Nelson, Lixun Zhang, Alex Brown, Yen Ong, Ali Yazdani, Sean Siddens, Ganesh Dasika, Alex Underwood

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Tags: AMD, AMD Radeon Instinct MI300X, AMD Radeon Instinct MI350X, ATI, BLAS, Computer science, HPC, Package, Performance, ROCm, Triton

December 7, 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

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

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high performance computing on graphics processing units: hgpu.org

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

ML Inference Scheduling with Predictable Latency

PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks & Fast Storage

Accurate Models of NVIDIA Tensor Cores

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Chopper: A Multi-Level GPU Characterization Tool & Derived Insights Into LLM Training Inefficiency

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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

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