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High performance computing on graphics processing units | hgpu.org
GPU,ATI,AMD,nVidia,OpenCL,CUDA,Algorithms,Science,Applications,Computing,Performance,Implementation
High performance computing on graphics processing units (GPU): AMD, ATI and nVidia; OpenCL, CUDA; algorithms, science and applications; GPGPU
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Данные linkpad ( 16 Июня 2017 ) | |
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Внешние ссылки главной страницы ( 14 ) | |
pytorch.org/ nofollow | PyTorch: An Imperative Style, High-Performance Deep Learning Library |
github.com/jpruiz84/masivo nofollow | Masivo: Parallel public transport routes simulation |
github.com/spcl/FBLAS nofollow | FBLAS: BLAS implementation for Intel FPGA |
github.com/pulp-platform/fann-on-mcu nofollow | FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things |
github.com/ja3067/Titan nofollow | Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications |
github.com/lucaspbordignon/onYourMarks nofollow | onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2 |
github.com/Kayzaks/HackingNeuralNetworks nofollow | Hacking Neural Networks: A small course on exploiting and defending neural networks |
github.com/brycexu/BNN_Kernel nofollow | A Computing Kernel for Network Binarization on PyTorch |
github.com/LuisJalabert/Deep-Learning-Based-FPGA-CPU-Acceler... nofollow | Deep Learning Based FPGA-CPU Acceleration |
github.com/Fillo7/KTT nofollow | KTT: Kernel Tuning Toolkit |
iwocl.org/ | <img> |
twitter.com/hgpu nofollow | |
facebook.com/HGPUgroup nofollow | |
plus.google.com/+HgpuOrg | Google+ |
Внутренние ссылки главной страницы ( 84 ) | |
hgpu.org/ nofollow | hgpu.org |
hgpu.org/?page_id=3306 | sign in |
hgpu.org/?page_id=9093 nofollow | register |
hgpu.org/?cat=65 | •Applications •Where it'sused |
hgpu.org/?cat=10 | Biology |
hgpu.org/?cat=66 | Chemistry |
hgpu.org/?cat=11 | Computer science |
hgpu.org/?cat=303 | Earth and Space sciences |
hgpu.org/?cat=73 | Computer vision |
hgpu.org/?cat=576 | Finance |
hgpu.org/?cat=157 | Mathematics |
hgpu.org/?cat=38 | Medicine |
hgpu.org/?cat=12 | Physics |
hgpu.org/?cat=32 | •Hardware •Specs andreviews |
hgpu.org/?cat=17 | nVidia hardware |
hgpu.org/?cat=4 | ATI/AMD hardware |
hgpu.org/?cat=1450 | Intel hardware |
hgpu.org/?cat=87 | •Programming •Algorithms andtechniques |
hgpu.org/?cat=36 | Algorithms |
hgpu.org/?tag=CUDA | CUDA |
hgpu.org/?tag=OpenCL | OpenCL |
hgpu.org/?tag=OpenGL | OpenGL |
hgpu.org/?tag=ATI-Stream | ATI Stream |
hgpu.org/?tag=OpenMP | OpenMP |
hgpu.org/?page_id=3529 | Statistics |
hgpu.org/?cat=86 | Events |
hgpu.org/?cat=65&tag=Package | Source codes |
hgpu.org/?tag=Tutorial | Tutorials |
hgpu.org/?page_id=6714 | Books |
hgpu.org/?cat=65&tag=Thesis | Theses |
hgpu.org/?page_id=3314 | Dashboard |
hgpu.org/?page_id=3316 | User home |
hgpu.org/?page_id=1109 | Tags list |
hgpu.org/?page_id=3310 | Add paper |
hgpu.org/?page_id=10702 | Add event |
hgpu.org/?page_id=1173 | Paper titles list |
hgpu.org/?p=19222 | The BondMachine toolkit: Enabling Machine Learning on FPGA |
hgpu.org/?p=19223 | PyTorch: An Imperative Style, High-Performance Deep Learning Library |
hgpu.org/?tag=cuda | CUDA |
hgpu.org/?p=19221 | GGNN: Graph-based GPU Nearest Neighbor Search |
hgpu.org/?p=19220 | Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes |
hgpu.org/?tag=opencl | OpenCL |
hgpu.org/?p=19219 | GPU Computing with Python: Performance, Energy Efficiency and Usability |
hgpu.org/?p=19214 | FBLAS: Streaming Linear Algebra Kernels on FPGA |
hgpu.org/?p=19215 | Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework |
hgpu.org/?p=19213 | FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things |
hgpu.org/?p=19211 | Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA |
hgpu.org/?p=19212 | FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads |
hgpu.org/?p=19207 | Understanding the Performance of HPC Applications |
hgpu.org/?p=19208 | Benchmarking Deep Learning Models on Jetson TX2 |
hgpu.org/?paged=2 | » |
hgpu.org/?paged=3 | 3 |
hgpu.org/?paged=4 | 4 |
hgpu.org/?paged=5 | 5 |
hgpu.org/?paged=10 | 10 |
hgpu.org/?paged=20 | 20 |
hgpu.org/?paged=30 | 30 |
hgpu.org/?paged=989 | Last » |
hgpu.org/?p=19205 | Hacking Neural Networks: A Short Introduction |
hgpu.org/?p=19201 | A Computing Kernel for Network Binarization on PyTorch |
hgpu.org/?p=19198 | Deep Learning Based FPGA-CPU Acceleration |
hgpu.org/?p=19196 | Framework for Parallel Kernels Auto-tuning |
hgpu.org/?cat=65&tag=package nofollow | See all packages |
hgpu.org/?p=19194 | Study of OpenCL Processing Models for FPGA Devices |
hgpu.org/?p=19204 | FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10 |
hgpu.org/?p=19192 | KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs |
hgpu.org/?p=19193 | Accelerating Stochastic Simulations on GPUs Using OpenCL |
hgpu.org/?p=19181 | 8th International Workshop on OpenCL, including SYCLCon, 2019 |
hgpu.org/?p=18679 | 7th International Workshop on OpenCL, 2019 |
hgpu.org/?p=18670 | Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019 |
hgpu.org/?p=18648 | 3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019 |
hgpu.org/?p=18589 | International Conference on Frontiers of Neural Networks (ICFNN), 2019 |
/?page_id=10702 | event |
hgpu.org/?cat=86 nofollow | See all events |
/?page_id=10698 nofollow | How to submit info |
/?page_id=3310 | paper |
hgpu.org/?feed=rss2 nofollow | RSS |
hgpu.org/?page_id=2 | HGPU group |
/?page_id=3306 nofollow | Login |
/?page_id=10375 nofollow | Sitemap |
/?page_id=10371 nofollow | Feedback |
/?page_id=10377 nofollow | Policy |
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hgpu.org/?page_id=10377 | Privacy policy |
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