high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Processing Markov Logic Networks with GPUs

Carlos Alberto Martinez-Angeles, Ines Dutra, Vitor Santos Costa, Jorge Buenabad-Chavez

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Tags: Computer science, CUDA, Databases, Machine learning, nVidia, Tesla K40

August 14, 2015 by hgpu

An Introduction to High Performance Computing on AWS

David Pellerin, Dougal Ballantyne, Adam Boeglin

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Tags: Cloud, Cluster computing, Computer science, CUDA, OpenCL

August 13, 2015 by hgpu

Perception of Acoustical Spatial Attributes and Impression in Virtually Rendered Sound Field

Yukio Iwaya, Makoto Otani, Takao Tsuchiya, Yasushi Inoguchi

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Tags: Acoustics, Computer science, CUDA, GPU cluster, nVidia, OpenMPI, Tesla M2075

August 13, 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

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

Blesson Varghese, Javier Prades, Carlos Reano, Federico Silla

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Tags: Computer science, CUDA, Finance, nVidia, Tesla K20, Virtualization

August 12, 2015 by hgpu

GPU Pro 6: Advanced Rendering Techniques

Wolfgang Engel (editor)

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Tags: 3D Graphics and Realism, Book, Computer science, CUDA, DirectCompute, DirectX, Games, nVidia, OpenCL, OpenGL, Rendering, Visualization

August 12, 2015 by hgpu

Optimizing strassen matrix multiply on GPUs

Ayaz ul Hasan Khan, Mayez Al-Mouhamed, Allam Fatayer

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Data parallelism, Matrix multiplication, nVidia, Tesla K20

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

Portable parallelized blowfish via RenderScript

Spencer Davis, Brandon Jones, Hai Jiang

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Tags: Algorithms, Android, Computer science, Java, RenderScript, Security

August 11, 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

GPU-Disasm: A GPU-based x86 Disassembler

Evangelos Ladakis, Giorgos Vasiliadis, Michalis Polychronakis, Sotiris Ioannidis, Georgios Portokalidis

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, Security

August 11, 2015 by hgpu

Places205-VGGNet Models for Scene Recognition

Limin Wang, Sheng Guo, Weilin Huang, Yu Qiao

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

August 10, 2015 by hgpu

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

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

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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high performance computing on graphics processing units: hgpu.org

Applications

Processing Markov Logic Networks with GPUs

Perception of Acoustical Spatial Attributes and Impression in Virtually Rendered Sound Field

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

Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application

GPU Pro 6: Advanced Rendering Techniques

Optimizing strassen matrix multiply on GPUs

A Parallel Implementation of the Self Organising Map using OpenCL

Portable parallelized blowfish via RenderScript

SINGA: Putting Deep Learning in the Hands of Multimedia Users

GPU-Disasm: A GPU-based x86 Disassembler

Places205-VGGNet Models for Scene Recognition

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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