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Reuse and Refactoring of GPU Kernels to Design Complex Applications

Santonu Sarkar, Sayantan Mitra, Ashok Srinivasan

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Tags: Computer science, CUDA, FFT, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Optimization, Software Engineering, Tesla S1050

February 24, 2012 by hgpu

ParTeCL: parallel testing using OpenCL

Snowflake: A Lightweight Portable Stencil DSL

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

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high performance computing on graphics processing units: hgpu.org

Reuse and Refactoring of GPU Kernels to Design Complex Applications

Recent source codes

ParTeCL: parallel testing using OpenCL

DenseNet: Densely Connected Convolutional Networks

SVMbenchmark: CUDA and OpenCL SVM training benchmark

llvm2KITTeL

shwirl: Meaningful colouring of spectral cube data with volume rendering

QuadricCurvature

entropy-sgd: Lua implementation of Entropy-SGD

LightGBM-GPU: GPU Accelerated LightGBM for Histogram-based GBDT Training

Snowflake: A Lightweight Portable Stencil DSL

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

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International Conference on Intelligent Autonomous Systems (ICIAS), 2018

International Conference on Robotics and Intelligent System (ICRIS), 2018

International Conference on Image and Graphics Processing (ICIGP), 2018

8th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB), 2018

The 10th International Conference on Computer Modeling and Simulation (ICCMS), 2018