high performance computing on graphics processing units: hgpu.org

Chengfei Lv, Chaoyue Niu, Renjie Gu, Xiaotang Jiang, Zhaode Wang, Bin Liu, Ziqi Wu, Qiulin Yao, Congyu Huang, Panos Huang, Tao Huang, Hui Shu, Jinde Song, Bin Zou, Peng Lan, Guohuan Xu, Fei Wu, Shaojie Tang, Fan Wu, Guihai Chen

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Tags: Cloud, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 2080 Ti, Python

June 5, 2022 by hgpu

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

Ali Mokhtari, Pooyan Jamshidi, Mohsen Amini Salehi

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Tags: Computer science, FPGA, Heterogeneous systems, Machine learning, nVidia, Package, Tesla M60

June 5, 2022 by hgpu

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

MapReduce for Counting Word Frequencies with MPI and GPUs

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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high performance computing on graphics processing units: hgpu.org

Applications

Securing GPU via Region-based Bounds Checking

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

CuPBoP: CUDA for Parallelized and Broad-range Processors

Modeling GPU Dynamic Parallelism for Self Similar Density Workloads

On the Compilation Performance of Current SYCL Implementations

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

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

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Portable, Scalable Approaches for Improving Asynchronous Many-Task Runtime Node Use

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

Recent source codes

Rodinia Benchmarks for DPC++

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

tntorch - Tensor Network Learning with PyTorch

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

MapReduce for Counting Word Frequencies with MPI and GPUs

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

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

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