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Blocks and Fuel: Frameworks for deep learning

Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, MPI, Neural networks, nVidia, Package, Python, Symbolic Computation, Visualization

June 5, 2015 by hgpu

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G. Dreslinski, Jason Mars, Lingjia Tang

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Tags: CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K40

May 25, 2015 by hgpu

U-Net: Convolutional Networks for Biomedical Image Segmentation

Olaf Ronneberger, Philipp Fischer, Thomas Brox

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Tags: CNN, Computer science, CUDA, Deep learning, Image processing, Medicine, Microscopy, Neural networks, nVidia, nVidia GeForce GTX Titan, Package

May 20, 2015 by hgpu

Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

Tobias Domhan, Jost Tobias Springenberg, Frank Hutter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan

May 15, 2015 by hgpu

Age and Gender Classification using Convolutional Neural Networks

Gil Levi, Tal Hassner

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Neural networks, nVidia

May 10, 2015 by hgpu

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

Michael Figurnov, Dmitry Vetrov, Pushmeet Kohli

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Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 980

May 5, 2015 by hgpu

Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

Firas Abuzaid, Stefan Hadjis, Ce Zhang, Christopher Re

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520, Tesla K40

April 20, 2015 by hgpu

A Convolutional Neural Network Cascade for Face Detection

Haoxiang Li, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Gang Hua

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Tags: CNN, Computer science, CUDA, Deep learning, Face detection, Neural networks, nVidia, nVidia GeForce GTX Titan

April 20, 2015 by hgpu

Deep convolutional networks for pancreas segmentation in CT imaging

Holger R. Roth, Amal Farag, Le Lu, Evrim B. Turkbey, Ronald M. Summers

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Tags: Computed tomography, CT, CUDA, Deep learning, Medicine, Neural networks, nVidia, nVidia GeForce GTX Titan Z, Tomography

April 17, 2015 by hgpu

Image Classification with Pyramid Representation and Rotated Data Augmentation on Torch 7

Keven (Kedao) Wang

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GRID K520

April 14, 2015 by hgpu

Multi-Lingual Speech Recognition with Low-Rank Multi-Task Deep Neural Networks

Aanchan Mohan, Richard Rose

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K20

April 7, 2015 by hgpu

Face Retriever: Pre-filtering the Gallery via Deep Neural Net

Dayong Wang, Anil K. Jain

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Tags: Algorithms, Computer science, Computer vision, CUDA, Deep learning, Filtering, Machine learning, Neural networks, nVidia, Tesla K20

March 30, 2015 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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

Blocks and Fuel: Frameworks for deep learning

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

U-Net: Convolutional Networks for Biomedical Image Segmentation

Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

Age and Gender Classification using Convolutional Neural Networks

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions

Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

A Convolutional Neural Network Cascade for Face Detection

Deep convolutional networks for pancreas segmentation in CT imaging

Image Classification with Pyramid Representation and Rotated Data Augmentation on Torch 7

Multi-Lingual Speech Recognition with Low-Rank Multi-Task Deep Neural Networks

Face Retriever: Pre-filtering the Gallery via Deep Neural Net

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

Device Virtual Machine (DVM)

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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