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Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

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Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

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A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

CPU-GPU Layer-Switched Low Latency CNN Inference

EngineCL for Stratix10 MX

FPGA Accelerators on Heterogeneous Systems: An Approach Using High Level Synthesis

Theseus: A Library for Differentiable Nonlinear Optimization

Theseus: A Library for Differentiable Nonlinear Optimization

OmpCluster

The OpenMP Cluster Programming Model

PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

Design and Implementation of CNN-FPGA accelerator based on Open Computing Language

hls4ml: Machine learning on FPGAs using HLS

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

MocCUDA: Prototype to run Pytorch/CUDA on Fugaku

High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs

Rodinia Benchmarks for DPC++

Evaluation of Intel’s DPC++ Compatibility Tool in heterogeneous computing

DCA++: a state of the art implementation of the dynamical cluster approximation

Optimizing the Performance of Parallel and Concurrent Applications Based on Asynchronous Many-Task Runtimes

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