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Learning Sparse Recurrent Neural Networks in Language Modeling

Yuanlong Shao

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan, Thesis

May 7, 2014 by hgpu

Cellular GPU Models to Euclidean Optimization Problems

Naiyu Zhang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 570, Optimization, Self-organizing map, Thesis

April 27, 2014 by hgpu

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Sergio Orts-Escolano, Jose Garcia-Rodriguez, Vicente Morell, Miguel Cazorla, Jorge Azorin, Juan Manuel Garcia-Chamizo

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Tags: Computer science, Computer vision, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce GTX 480

April 24, 2014 by hgpu

Fast Efficient Artificial Neural Network for Handwritten Digit Recognition

Viragkumar N. Jagtap, Shailendra K. Mishra

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Tags: Computer science, CUDA, Image recognition, Neural networks, nVidia, nVidia GeForce GT 630

April 21, 2014 by hgpu

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

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Source codes

Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 by hgpu

Intrusion Detection using Spiking Neural Networks

Gaurav Budjade

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Tags: ATI, ATI Radeon HD 7970, Computer science, Neural networks, OpenCL, Python, Security

April 13, 2014 by hgpu

Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

Michael Anderson, Forrest Iandola, Kurt Keutzer

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Neural networks, OpenCL, Optical flow

April 7, 2014 by hgpu

RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Simon Wiesler, Alexander Richard, Pavel Golik, Ralf Schluter, Hermann Ney

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, Speech recognition, Tesla K20

March 9, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Implementing Neural Networks Efficiently

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla M2090

December 29, 2013 by hgpu

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang

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Tags: Computer science, Computer vision, CUDA, Data parallelism, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K20

December 24, 2013 by hgpu

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

See all packages

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high performance computing on graphics processing units: hgpu.org

Learning Sparse Recurrent Neural Networks in Language Modeling

Cellular GPU Models to Euclidean Optimization Problems

Parallel Computational Intelligence-Based Multi-Camera Surveillance System

Fast Efficient Artificial Neural Network for Handwritten Digit Recognition

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Intrusion Detection using Spiking Neural Networks

Exploring the power of GPU’s for training Deep Belief Networks

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

RASR/NN: The RWTH Neural Network Toolkit for Speech Recognition

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Implementing Neural Networks Efficiently

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

Recent source codes

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

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