high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GTX 750

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

View

Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Zhe Zhou, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, Rui Liu

View

Download (PDF)

Tags: AMD Radeon R7 250, ATI, Cloud, Computer science, nVidia, nVidia GeForce GTX 750, OpenCL, Operating systems, Security

May 26, 2016 by hgpu

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

High Performance Histograms on SIMT and SIMD Architectures

M.E.R. Berger

View

Download (PDF)

Tags: AMD Radeon R9 290X, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 660, nVidia GeForce GTX 750, OpenCL, Performance, Thesis

December 10, 2015 by hgpu

Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Janghaeng Lee, Mehrzad Samadi, Scott Mahlke

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 760, OpenCL

November 29, 2015 by hgpu

Fast OBJ file importing and parsing in CUDA

Aidan L. Possemiers, Ickjai Lee

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenGL

November 10, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

View

Download (PDF)

Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 2015 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

View

Download (PDF)

Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

Implementing Efficient, Portable Computations for Machine Learning

ESPRESO: Highly Parallel Solvers for Engineering Applications

Intel Xeon Phi acceleration of Hybrid Total FETI solver

Parallel and in-process compilation of individuals for genetic programming on GPU

MECCA development in accelerators - KPP Fortran to CUDA source-to-source pre-processor

MEDINA: MECCA Development in Accelerators – KPP Fortran to CUDA source-to-source Preprocessor

Internally-maintained tool for examining GPU scheduling behavior

Inferring the Scheduling Policies of an Embedded CUDA GPU

See all packages

* * *

2017

November

12-14

Seoul, South Korea

4th International Conference on Biomedical and Bioinformatics Engineering (ICBBE), 2017

2017

November

27-30

Auckland, New Zealand

9th International Conference on Signal Processing Systems (ICSPS), 2017

2017

October

11-13

Edmonton, Canada

The 5th International conference on Control, Mechatronics and Automation (ICCMA), 2017

2017

October

11-13

Busan, South Korea

8th International Conference on Biology, Environment and Chemistry (ICBEC), 2017

2017

November

7-9

Wuhan, China

2nd International Conference on Communication and Information Systems (ICCIS), 2017

Add event

See all events

high performance computing on graphics processing units: hgpu.org

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Architecture-Adaptive Code Variant Tuning

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

High Performance Histograms on SIMT and SIMD Architectures

Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Fast OBJ file importing and parsing in CUDA

GPU concurrency: Weak behaviours and programming assumptions

Testing and Exposing Weak Graphics Processing Unit Memory Models

Recent source codes

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

ESPRESO: Highly Parallel Solvers for Engineering Applications

Parallel and in-process compilation of individuals for genetic programming on GPU

MECCA development in accelerators - KPP Fortran to CUDA source-to-source pre-processor

Internally-maintained tool for examining GPU scheduling behavior

Most viewed papers (last 30 days)

Rating

Featured events

4th International Conference on Biomedical and Bioinformatics Engineering (ICBBE), 2017

9th International Conference on Signal Processing Systems (ICSPS), 2017

The 5th International conference on Control, Mechatronics and Automation (ICCMA), 2017

8th International Conference on Biology, Environment and Chemistry (ICBEC), 2017

2nd International Conference on Communication and Information Systems (ICCIS), 2017