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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

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

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

Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials

Manpreet Singh

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Tags: Biology, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia A100, PyTorch, Triton

December 15, 2025 by hgpu

Decoupled Triton: A Block-Level Decoupled Language for Writing and Exploring Efficient Machine-Learning Kernels

Quinn Leo Pham

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Tags: Compilers, Computer science, Machine learning, nVidia, nVidia RTX 5000 Ada, PyTorch, Thesis, Triton

December 7, 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

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

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

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

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

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

BoltzGen:Toward Universal Binder Design

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

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

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

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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

See all packages

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high performance computing on graphics processing units: hgpu.org

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

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

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

Accelerating Molecular Simulations with Triton: Fused GPU Kernels for TensorNet Neural Potentials

Decoupled Triton: A Block-Level Decoupled Language for Writing and Exploring Efficient Machine-Learning Kernels

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

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

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

BoltzGen:Toward Universal Binder Design

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

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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