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Scaling Deep Learning on GPU and Knights Landing clusters

Yang You, Aydin Buluc, James Demmel

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Tags: Cloud, Computer science, Deep learning, GPU cluster, Intel Xeon Phi, Machine learning, Neural networks, nVidia, Package, PC cluster, Tesla K80, Tesla M100

August 17, 2017 by hgpu

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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

tntorch - Tensor Network Learning with PyTorch

tntorch: Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

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