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Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

Fabian Tschopp

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Tags: AMD FirePro W9100, Artificial intelligence, ATI, Caffe, CNN, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Thesis

September 15, 2015 by hgpu

Three storage formats for sparse matrices on GPGPUs

Davide Barbieri, Valeria Cardellini, Alessandro Fanfarillo, Salvatore Filippone

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 660, Package, Sparse matrix, Tesla M2070

September 9, 2015 by hgpu

Dissecting GPU Memory Hierarchy through Microbenchmarking

Xinxin Mei, Xiaowen Chu

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Tags: Benchmarking, cache, Computer science, CUDA, Hardware Architecture, nVidia, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Package

September 9, 2015 by hgpu

Advanced Simulation Library: Expanding software ecosystem for the DSP/FPGA/GPU market

Avtech Scientific

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Tags: DSP, FPGA, OpenCL, Package, Physics, Presentation

September 3, 2015 by hgpu

A parallel algorithm for implicit depletant simulations

Jens Glaser, Andrew S. Karas, Sharon C. Glotzer

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Tags: Condensed matter, CUDA, Molecular dynamics, nVidia, Package, Physics, Soft Condensed Matter, Tesla K20, Tesla K80

August 31, 2015 by hgpu

VisPy: Harnessing The GPU For Fast, High-Level Visualization

Luke Campagnola, Almar Kleink, Eric Larson, Cyrille Rossant, Nicolas Rougier

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Tags: Computer science, OpenGL, Package, Python, Visualization

August 28, 2015 by hgpu

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Tayler H. Hetherington, Mike O'Connor, Tor M. Aamodt

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Tags: Computer science, CUDA, Databases, Energy-efficient computing, nVidia, nVidia GeForce GTX 750 Ti, Package, Tesla K20, Tesla K40

August 27, 2015 by hgpu

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

Max Danielsson, Thomas Sievert

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Tags: Android, Computer science, Computer vision, Embedded high-performance computing, nVidia, nVidia GeForce GTX 660, OpenCL, Package, Thesis

August 24, 2015 by hgpu

GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Ursula Iturraran-Viveros, Miguel Molero-Armenta

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Tags: Condensed matter, Instrumentation and measurement, nVidia, OpenCL, Package, Physics, PyOpenCL, Python

August 21, 2015 by hgpu

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

Yunjin Chen, Thomas Pock

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Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 780 Ti, Package, Signal denoising

August 13, 2015 by hgpu

SINGA: Putting Deep Learning in the Hands of Multimedia Users

Wei Wang, Gang Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

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Tags: Computer science, Computer vision, Deep learning, Distributed computing, Package

August 11, 2015 by hgpu

A Parallel Implementation of the Self Organising Map using OpenCL

Gavin Davidson

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Tags: Algorithms, Computer science, Machine learning, nVidia, nVidia GeForce GTX 590, OpenCL, Package, Self-organizing map

August 11, 2015 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

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high performance computing on graphics processing units: hgpu.org

Applications

Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems

Three storage formats for sparse matrices on GPGPUs

Dissecting GPU Memory Hierarchy through Microbenchmarking

Advanced Simulation Library: Expanding software ecosystem for the DSP/FPGA/GPU market

A parallel algorithm for implicit depletant simulations

VisPy: Harnessing The GPU For Fast, High-Level Visualization

MemcachedGPU: Scaling-up Scale-out Key-value Stores

Viability of Feature Detection on Sony Xperia Z3 using OpenCL

GPU computing with OpenCL to model 2D elastic wave propagation: exploring memory usage

Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

SINGA: Putting Deep Learning in the Hands of Multimedia Users

A Parallel Implementation of the Self Organising Map using OpenCL

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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