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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

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Zhongzhen Wen, Hongyu Liu, Tingwei Zhu, Minxue Pan, Shaohua Wang, Yuanyi Lin, Kairui Liu, Tian Zhang, Xuandong Li

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Tags: Computer science, CUDA, Deep learning, nVidia, Package, PyTorch

November 2, 2025 by hgpu

EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models

Ping Guo, Chenyu Zhu, Siyuan Chen, Fei Liu, Xi Lin, Zhichao Lu, Qingfu Zhang

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Tags: AI, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia GeForce RTX 4090, nVidia H100, PyTorch

October 12, 2025 by hgpu

Home-made Diffusion Model from Scratch to Hatch

Shih-Ying Yeh

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce RTX 5090, Package, Python, PyTorch

September 14, 2025 by hgpu

Managing Multi Instance GPUs for High Throughput and Energy Savings

Abhijeet Saraha, Yuanbo Li, Chris Porter, Santosh Pande

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Tags: Computer science, CUDA, Energy-efficient computing, LLM, nVidia, nVidia A100, PyTorch, Security

September 7, 2025 by hgpu

Accelerating a Linear Programming Algorithm on AMD GPUs

Xiyan Hu, Titus Parker, Connor Phillips, Yifa Yu

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Tags: AMD Radeon Instinct MI210, AMD Radeon Instinct MI325X, ATI, Computer science, HIP, nVidia, nVidia A100, Package, Performance, PyTorch, ROCm, Thesis

August 31, 2025 by hgpu

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Jiarong Xing, Yifan Qiao, Simon Mo, Xingqi Cui, Gur-Eyal Sela, Yang Zhou, Joseph Gonzalez, Ion Stoica

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Performance, PyTorch

August 24, 2025 by hgpu

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, FPGA, HLS, LLM, Machine learning, Neural networks, PyTorch

May 4, 2025 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

Weijie Lv, Xuan Xia, Sheng-Jun Huang

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

April 27, 2025 by hgpu

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

Dimitar Mileski, Nikola Petrovski, Marjan Gusev

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Tags: Computer science, CUDA, Deep learning, HPC, LLM, nVidia, nVidia A100, PTX, PyTorch

April 13, 2025 by hgpu

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

Abhishek Ghosh, Ajay Nayak, Ashish Panwar, Arkaprava Basu

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia RTX A6000, PyTorch

March 30, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

HipKittens: Fast and Furious AMD Kernels

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

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

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high performance computing on graphics processing units: hgpu.org

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models

Home-made Diffusion Model from Scratch to Hatch

Managing Multi Instance GPUs for High Throughput and Energy Savings

Accelerating a Linear Programming Algorithm on AMD GPUs

Towards Efficient and Practical GPU Multitasking in the Era of LLM

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Data-efficient LLM Fine-tuning for Code Generation

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch

KernelBench: Can LLMs Write Efficient GPU Kernels?

Recent source codes

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

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

LC Framework

pplx-garden: Perplexity open source garden for inference technology

OpScanner

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

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