high performance computing on graphics processing units: hgpu.org

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Hybrid CPU-GPU Framework for Network Motifs

Ryan A. Rossi, Rong Zhou

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Tags: Algorithms, Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX Titan Black

August 23, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

GPU-accelerated Gibbs Sampling

Alexander Terenin, Shawfeng Dong, David Draper

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Tags: Bayesian, big data, CUDA, nVidia, Statistics, Tesla K20

August 18, 2016 by hgpu

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

August Ernstsson

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Programming techniques, Tesla K20, Thesis

August 18, 2016 by hgpu

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

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Tags: CNN, CUDA, Deep learning, Image processing, Matlab, nVidia, nVidia GeForce GTX Titan X, Package, Signal denoising

August 18, 2016 by hgpu

Automatic Generation of OpenCL Code for ARM Architectures

Sergio Afonso, Alejandro Acosta, Francisco Almeida

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Tags: ARM, Code generation, Compilers, Computer science, OpenCL, RenderScript

August 16, 2016 by hgpu

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Speech recognition

August 16, 2016 by hgpu

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

David Richie, James Ross

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Tags: Algorithms, ARM, Computer science, Matrix multiplication, Memory model, OpenCL, Package

August 16, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

Near Memory Similarity Search on Automata Processors

Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin

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Tags: ARM, Computer science, CUDA, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

August 16, 2016 by hgpu

A Comparison of Potential Interfaces for Batched BLAS Computations

Samuel D. Relton, Pedro Valero-Lara, Mawussi Zounon

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Tags: BLAS, Computer science, CUDA, Linear Algbera, nVidia, Package, Tesla K40

August 11, 2016 by hgpu

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Gopalakrishna Hegde, Siddhartha, Nachiappan Ramasamy, Nachiket Kapre

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Tags: Computer science, CUDA, Deep learning, DSP, FPGA, nVidia, nVidia Tegra TX1

August 11, 2016 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

Hybrid CPU-GPU Framework for Network Motifs

GPU-Acceleration of In-Memory Data Analytics

GPU-accelerated Gibbs Sampling

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Automatic Generation of OpenCL Code for ARM Architectures

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

OpenCL + OpenSHMEM Hybrid Programming Model for the Adapteva Epiphany Architecture

Learning Structured Sparsity in Deep Neural Networks

Near Memory Similarity Search on Automata Processors

A Comparison of Potential Interfaces for Batched BLAS Computations

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

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Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

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