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Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

Patricia Siwinska,Jie Lei,Adrian Castello,Pedro Alonso-Jord́a,Enrique S. Quintana-Orti

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Heterogeneous systems, LLM, nVidia, nVidia A100, Package, performance portability

November 2, 2025 by hgpu

STARK: Strategic Team of Agents for Refining Kernels

Juncheng Dong, Yang Yang, Tao Liu, Yang Wang, Feng Qi, Vahid Tarokh, Kaushik Rangadurai, Shuang Yang

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Tags: Benchmarking, Code generation, Computer science, LLM, nVidia, nVidia A100

October 26, 2025 by hgpu

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

Leonardo Solis-Vasquez, Andreas F. Tillack, Diogo Santos-Martins, Andreas Koch, Stefano Forli

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Tags: Chemistry, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia A100, nVidia B200, nVidia H100, Package

October 26, 2025 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Matyáš Brabec, Jiří Klepl, Michal Töpfer, Martin Kruliš

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia V100, Package

October 26, 2025 by hgpu

Anonymized Network Sensing using C++26 std::execution on GPUs

Michael Mandulak, Sayan Ghosh, S M Ferdous, Mahantesh Halappanavar, George Slota

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Tags: Computer science, CUDA, Network communication, nVidia, nVidia A100

October 19, 2025 by hgpu

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic

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Tags: AMD Radeon Instinct MI300X, ATI, Computer science, CUDA, Deep learning, nVidia, nVidia A100, nVidia RTX A5000, nVidia RTX A6000, Package, Performance, ROCm

October 19, 2025 by hgpu

High-Performance Computing: from Optimization to Automation

Bérenger Bramas

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Tags: AMD Radeon Instinct MI210, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, nVidia T600, Performance, Thesis

October 12, 2025 by hgpu

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

M. D. Lepinzan, G. Lacopo, D. Goz, G. Taffoni, P. Monaco, P. J. Elahi, U. Varetto, M. Cytowski

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Tags: AMD Radeon Instinct MI250X, Astrophysics, ATI, Benchmarking, Heterogeneous systems, HIP, HPC, Instrumentation and Methods for Astrophysics, nVidia, nVidia A100, OpenMP, Package

October 12, 2025 by hgpu

TRUST: the HPC open-source CFD platform – from CPU to GPU

Elie Saikali∗ , Adrien Bruneton, Pierre Ledac, Remi Bourgeois

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Tags: AMD Radeon Instinct MI250A, AMD Radeon Instinct MI300A, ATI, cfd, CUDA, Fluid dynamics, HIP, MPI, Numerical simulation, nVidia, nVidia A100, OpenMP, Package

September 28, 2025 by hgpu

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software

Siddharth Samsi, Dan Campbell, Emanuel Scoullos, Oded Green

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, nVidia A100, nVidia H100, nVidia H200, Sensing

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

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

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

STARK: Strategic Team of Agents for Refining Kernels

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

Tutoring LLM into a Better CUDA Optimizer

Anonymized Network Sensing using C++26 std::execution on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

High-Performance Computing: from Optimization to Automation

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

TRUST: the HPC open-source CFD platform – from CPU to GPU

Combining Performance and Productivity: Accelerating the Network Sensing Graph Challenge with GPUs and Commodity Data Science Software

Managing Multi Instance GPUs for High Throughput and Energy Savings

Accelerating a Linear Programming Algorithm on AMD GPUs

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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