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FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error

Fengjuan Wang, Zhiyi Su, Xingzhu Hu, Cheng Wang, Mou Sun

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Tags: Artificial intelligence, Computer science, Machine learning, nVidia, nVidia H100

November 9, 2025 by hgpu

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

Marcin Spoczynski, Marcela S. Melara

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Tags: Computer science, Machine learning, nVidia, oneAPI, Package, PyTorch, Rust, Security, SYCL

November 2, 2025 by hgpu

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

Mengzhao Chen, Meng Wu, Hui Jin, Zhihang Yuan, Jing Liu, Chaoyi Zhang, Yunshui Li, Jie Huang, Jin Ma, Zeyue Xue, Zhiheng Liu, Xingyan Bin, Ping Luo

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Tags: Computer science, CUDA, Machine learning, nVidia, Package

November 2, 2025 by hgpu

Compiler and Runtime Systems for Generative AI Models

Zihao Ye

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 3070, nVidia H100, Package, Tesla V100

October 19, 2025 by hgpu

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

Lingcheng Kong, Jiateng Wei, Hanzhang Shen, Huan Wang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, Machine learning, nVidia, Package

October 12, 2025 by hgpu

Cost-Performance Analysis: A Comparative Study of CPU-Based Serverless and GPU-Based Training Architectures

Amine Barrak, Fabio Petrillo, Fehmi Jaafar

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Tags: Computer science, Databases, Machine learning, nVidia, Tesla T4

September 28, 2025 by hgpu

High Performance GPU Implementation of KNN Algorithm: A Review

Pooja Bidye, Pradnya Borkar, Nitin Rakesh

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Tags: Computer science, Machine learning, Nearest neighbour, nVidia, Review

September 21, 2025 by hgpu

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Yongin Kwon, Joohyoung Cha, Sehyeon Oh, Misun Yu, Jeman Park, Jemin Lee

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Tags: ARM, Computer science, Deep learning, Machine learning, Package

August 17, 2025 by hgpu

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Chris S. Lin, Joyce Qu, Gururaj Saileshwar

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, Package, Security

August 17, 2025 by hgpu

Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision

Evelyne Ringoot, Rabab Alomairy, Valentin Churavy, Alan Edelman

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Tags: AMD Radeon Instinct MI250, Apple M1 Pro, ATI, Computer science, HIP, Intel, Intel Ponte Vecchio Max 1100, Kokkos, Linear Algebra, Machine learning, nVidia, nVidia A100, nVidia GeForce RTX 4060, nVidia H100, OpenCL, SYCL

August 17, 2025 by hgpu

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers

Sarunas Kalade, Graham Schelle

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Tags: Code generation, Computer science, Machine learning, Programming Languages

August 3, 2025 by hgpu

Performance Portable Gradient Computations Using Source Transformation

Kim Liegeois, Brian Kelley, Eric Phipps, Sivasankaran Rajamanickam, Vassil Vassilev

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, HIP, Kokkos, Machine learning, Mathematical Software, nVidia, nVidia H100

August 3, 2025 by hgpu

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

FP8-Flow-MoE: A Casting-Free FP8 Recipe without Double Quantization Error

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

Compiler and Runtime Systems for Generative AI Models

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

Cost-Performance Analysis: A Comparative Study of CPU-Based Serverless and GPU-Based Training Architectures

High Performance GPU Implementation of KNN Algorithm: A Review

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Performant Unified GPU Kernels for Portable Singular Value Computation Across Hardware and Precision

NPUEval: Optimizing NPU Kernels with LLMs and Open Source Compilers

Performance Portable Gradient Computations Using Source Transformation

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CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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