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hgpu.org » Programming » CUDA

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

Ye Fang, Yun Ding, Wei P. Feinstein, David M. Koppelman, Juana Moreno, Mark Jarrell, J. Ramanujam, Michal Brylinski

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Tags: Algorithms, Chemistry, CUDA, Heterogeneous systems, Intel Xeon Phi, Medicine, Molecular dynamics, nVidia, nVidia GeForce GTX 980, OpenMP, Package

July 16, 2016 by hgpu

Accelerating Eulerian Fluid Simulation With Convolutional Networks

Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken Perlin

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Tags: 3D Graphics and Realism, CNN, Computational Complexity, CUDA, Deep learning, Fluid dynamics, Fluid simulation, Machine learning, nVidia, nVidia GeForce GTX Titan X

July 16, 2016 by hgpu

OpenFace: A general-purpose face recognition library with mobile applications

Brandon Amos, Bartosz Ludwiczuk, Mahadev Satyanarayanan

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Tags: Computer science, Computer vision, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, Python, Tesla K40

July 13, 2016 by hgpu

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

Matthew Spellings, Ryan L. Marson, Joshua A. Anderson, Sharon C. Glotzer

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Tags: Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia Quadro M 6000, Package, Particle simulation, Physics

July 13, 2016 by hgpu

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Markus Hohnerbach, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Materials Science, Molecular dynamics, MPI, nVidia, Package, performance portability, Tesla K20, Tesla K40

July 13, 2016 by hgpu

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Tobias Graf, Marco Platzner

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Tags: Algorithms, CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, Search, Tesla K20

July 11, 2016 by hgpu

Large Scale GPU Accelerated PPMLR-MHD Simulations for Space Weather Forecast

Xiangyu Guo, Binbin Tang, Jian Tao, Zhaohui Huang, Zhihui Du

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Tags: CUDA, Earth and Space Sciences, Magnetohydrodynamics, MPI, nVidia, Tesla K20, Weather prediction

July 11, 2016 by hgpu

Deep Learning for Mortgage Risk

Justin Sirignano, Apaar Sadhwani, Kay Giesecke

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Tags: CUDA, Deep learning, Finance, Machine learning, Neural networks, nVidia, Statistical Finance

July 11, 2016 by hgpu

Fast Predictive Image Registration

Xiao Yang, Roland Kwitt, Marc Niethammer

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Tags: Bayesian, Computer science, Computer vision, CUDA, Deep learning, Image registration, nVidia, nVidia GeForce GTX Titan X, Package, Python

July 11, 2016 by hgpu

Torchnet: An Open-Source Platform for (Deep) Learning Research

Ronan Collobert, Laurens van der Maaten, Armand Joulin

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Tags: Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Torch

July 8, 2016 by hgpu

Using the pyMIC Offload Module in PyFR

Michael Klemm, Freddie Witherden, Peter Vincent

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Tags: Computer science, CUDA, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, OpenCL, OpenMP, Package, Python

July 8, 2016 by hgpu

TTC: A Tensor Transposition Compiler for Multiple Architectures

Paul Springer, Aravind Sankaran, Paolo Bientinesi

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Tags: BLAS, Compilers, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce 840 M, Package, Performance, Tesla K40

July 8, 2016 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Programming

GeauxDock: Accelerating Structure-Based Virtual Screening with Heterogeneous Computing

Accelerating Eulerian Fluid Simulation With Convolutional Networks

OpenFace: A general-purpose face recognition library with mobile applications

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability

Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

Large Scale GPU Accelerated PPMLR-MHD Simulations for Space Weather Forecast

Deep Learning for Mortgage Risk

Fast Predictive Image Registration

Torchnet: An Open-Source Platform for (Deep) Learning Research

Using the pyMIC Offload Module in PyFR

TTC: A Tensor Transposition Compiler for Multiple Architectures

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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