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Programming in CUDA for Kepler and Maxwell Architecture

Esteban G. W. Clua, Marcelo Zamith

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Tags: Computer science, CUDA, nVidia, Performance

December 1, 2015 by hgpu

Auxiliary Image Regularization for Deep CNNs with Noisy Labels

Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia

December 1, 2015 by hgpu

A General Framework for Constrained Bayesian Optimization using Information-based Search

Jose Miguel Hernandez-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

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Source codes

Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Package, Python

December 1, 2015 by hgpu

Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

Max Grossman, Mauricio Araya-Polo

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K10

December 1, 2015 by hgpu

Neural GPUs Learn Algorithms

Lukasz Kaiser, Ilya Sutskever

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

December 1, 2015 by hgpu

Reordering GPU Kernel Launches to Enable Efficient Concurrent Execution

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580

November 29, 2015 by hgpu

Design, Implementation and Performance Evaluation of a Stochastic Gradient Descent Algorithm on CUDA

Emanuele De Falco

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GT 540 M

November 29, 2015 by hgpu

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Philipp Kainz, Michael Pfeiffer, Martin Urschler

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Tags: Algorithms, CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black

November 29, 2015 by hgpu

Acceleration of Agent-Based Pandemic Modeling on Multiple GPUs

Barzan Shekh

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

November 25, 2015 by hgpu

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Tesla C2070, Virtualization

November 25, 2015 by hgpu

Pulsar Acceleration Searches on the GPU for the Square Kilometre Array

Sofia Dimoudi, Wesley Armour

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX Titan X, Tesla K80

November 25, 2015 by hgpu

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Liang-Chieh Chen

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 650 M, OpenCV, Tesla K40, Thesis

November 24, 2015 by hgpu

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Programming in CUDA for Kepler and Maxwell Architecture

Auxiliary Image Regularization for Deep CNNs with Noisy Labels

A General Framework for Constrained Bayesian Optimization using Information-based Search

Efficient Static and Dynamic Memory Management Techniques for Multi-GPU Systems

Neural GPUs Learn Algorithms

Reordering GPU Kernel Launches to Enable Efficient Concurrent Execution

Design, Implementation and Performance Evaluation of a Stochastic Gradient Descent Algorithm on CUDA

Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

Acceleration of Agent-Based Pandemic Modeling on Multiple GPUs

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Pulsar Acceleration Searches on the GPU for the Square Kilometre Array

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Recent source codes

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

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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