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Charactering and Detecting CUDA Program Bugs

Mingyuan Wu, Husheng Zhou, Lingming Zhang, Cong Liu, Yuqun Zhang

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Tags: Computer science, CUDA, LLVM, nVidia, Package, Software Engineering

May 8, 2019 by hgpu

DLL: A Blazing Fast Deep Neural Network Library

Baptiste Wicht, Jean Hennebert, Andreas Fischer

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 960, Package, Software Engineering

April 15, 2018 by hgpu

Implementing and Evaluating Candidate-Based Invariant Generation

Adam Betts, Nathan Chong, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Software Engineering

December 10, 2016 by hgpu

Integrating a large-scale testing campaign in the CK framework

Andrei Lascu, Alastair F. Donaldson

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Tags: Computer science, OpenCL, Software Engineering

November 11, 2015 by hgpu

Contract-Based General-Purpose GPU Programming

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz, Bertrand Meyer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package, Software Engineering

October 27, 2014 by hgpu

Teaching Parallel Programming Using Java

Aamir Shafi, Aleem Akhtar, Ansar Javed, Bryan Carpenter

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Tags: Computer science, CUDA, Education, Hadoop, Java, MapReduce, MPI, nVidia, Programming techniques, Software Engineering

October 4, 2014 by hgpu

GPGPU Programming for Games and Science

David H. Eberly

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Tags: Algorithms, Book, Computer science, DirectX, Games, HLSL, Package, Software Engineering

August 21, 2014 by hgpu

Numerical Computations with GPUs

Volodymyr Kindratenko

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Tags: Algorithms, ATI, Book, Computer science, CUDA, Differential equations, FFT, Linear Algebra, Mathematics, Numerical simulation, nVidia, OpenCL, Pseudo-random number generators, Software Engineering

August 13, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

GPU hardware acceleration for industrial applications: using computation to push beyond physical limitations

Mohammadhossein Afrasiabi

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Tags: Algorithms, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 580, nVidia GeForce GTX 590, Rendering, Sensing, Software Engineering, Thesis

December 13, 2013 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

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Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

Neural Query Language: A Knowledge Base Query Language for Tensorflow

Neural Query Language: A Knowledge Base Query Language for Tensorflow

NMODL: Code Generation Framework For NEURON MODeling Language

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

cuda-benches: collection of small CUDA benchmarking programs

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Simulee: Charactering and Detecting CUDA Program Bugs

Charactering and Detecting CUDA Program Bugs

TensorNetwork: A TensorFlow library for easy and efficient manipulation of tensor networks

TensorNetwork: A Library for Physics and Machine Learning

jive

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

caffe-mc-opencl: Caffe Deep Learning Software with Model Compression by Sparse Coding (Backend: OpenCL)

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Full-stack Optimization for Accelerating CNNs with FPGA Validation

Full-stack Optimization for Accelerating CNNs with FPGA Validation

AdaNet: Fast and flexible AutoML with learning guarantees

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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