high performance computing on graphics processing units: hgpu.org

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Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs

Alessandro Vincenzi, Arvind Sridhar, Martino Ruggiero, David Atienza

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

December 14, 2011 by hgpu

Fast Neural Network Training on General Purpose Computers

Harshit Kharbanda, Roy H. Campbell

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia Quadro FX 1800

December 14, 2011 by hgpu

Evolving Neural Networks on GPUs

Johannes Hofmann

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Finance, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX 480, Tesla C1060

December 7, 2011 by hgpu

GPU Implementation of Spiking Neural Networks for Color Image Segmentation

Ermai Xie, Martin McGinnity, QingXiang Wu, Jianyong Cai, Rontai Cai

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Tags: CUDA, Image processing, Neural networks, nVidia, nVidia Quadro FX 3800

November 27, 2011 by hgpu

Flexible neuronal network simulation framework using code generation for NVidia CUDA

Thomas Nowotny

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Tags: Bioinformatics, Biology, CUDA, Neural networks, Neuroscience, nVidia, nVidia Quadro FX 5800, Poster

October 26, 2011 by hgpu

Evaluation of GPU Architectures Using Spiking Neural Networks

Vivek K. Pallipuram, Mohammad A. Bhuiyan, Melissa C. Smith

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Tags: ATI, ATI Radeon HD 5870, Computer science, Neural networks, nVidia, OpenCL, Optimization, Performance, Presentation, Tesla C2050

October 10, 2011 by hgpu

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Andrew Nere, Atif Hashmi, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Image recognition, Neural networks, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Tesla C2050

October 5, 2011 by hgpu

Flexible, high performance convolutional neural networks for image classification

Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Benchmarking, Computer science, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 580

October 5, 2011 by hgpu

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Li Liang

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Tags: Algorithms, Computer science, CUDA, Neural networks, nVidia, OpenCL, Optimization

October 3, 2011 by hgpu

Simulating Biological-Inspired Spiking Neural Networks with OpenCL

Jorn Hoffmann, Karim El-Laithy, Frank Guttler, Martin Bogdan

Tags: Computer science, Data parallelism, Neural networks, OpenCL

August 18, 2011 by hgpu

Parallel Batch Training of the Self-Organizing Map Using OpenCL

Masahiro Takatsuka, Michael Bui

Tags: Computer science, Neural networks, OpenCL, Self-organizing map

August 18, 2011 by hgpu

Accelerated simulation of spiking neural networks using GPUs

Andreas K. Fidjeland, Murray P. Shanahan

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Tags: Biology, CUDA, Neural networks, nVidia, Tesla C1060

August 6, 2011 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs

Fast Neural Network Training on General Purpose Computers

Evolving Neural Networks on GPUs

GPU Implementation of Spiking Neural Networks for Color Image Segmentation

Flexible neuronal network simulation framework using code generation for NVidia CUDA

Evaluation of GPU Architectures Using Spiking Neural Networks

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Flexible, high performance convolutional neural networks for image classification

Implementation of the optimization algorithms on GPGPU architecture and multi-cores

Simulating Biological-Inspired Spiking Neural Networks with OpenCL

Parallel Batch Training of the Self-Organizing Map Using OpenCL

Accelerated simulation of spiking neural networks using GPUs

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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