high performance computing on graphics processing units: hgpu.org

hgpu.org » Neural networks

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

Filippo Vicentini, Damian Hofmann, Attila Szabo, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderon, Nikita Astrakhantsev, Giuseppe Carleo

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Tags: Linear Algebra, Machine learning, Neural networks, nVidia, nVidia GeForce RTX 3090, Package, Physics, Python, Quantum Physics, Tesla A100

December 26, 2021 by hgpu

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

Seung-Hun Chung

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Tags: Computer science, FPGA, Neural networks, nVidia, nVidia GeForce GTX 1060, OpenCL, Performance, Thesis

December 19, 2021 by hgpu

TCUDB: Accelerating Database with Tensor Processors

Yu-Ching Hu, Yuliang Li, Hung-Wei Tseng

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Tags: Computer science, CUDA, Databases, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 3090

December 19, 2021 by hgpu

Manas: Mining Software Repositories to Assist AutoML

Giang Nguyen, Johir Islam, Rangeet Pan, Hridesh Rajan

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Tags: Computer science, Deep learning, Neural networks, nVidia, Package, Python, Software Engineering, Tesla V100

December 12, 2021 by hgpu

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

Christopher Noel Hesse

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Tags: Benchmarking, Computer science, Deep learning, FPGA, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce RTX 2070, nVidia Jetson Nano, Performance, Tesla V100

December 5, 2021 by hgpu

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Ji Liu, Zhihua Wu, Dianhai Yu, Yanjun Ma, Danlei Feng, Minxu Zhang, Xinxuan Wu, Xuefeng Yao, Dejing Dou

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Tags: Computer science, Deep learning, Distributed computing, Heterogeneous systems, Neural networks, nVidia, Package, Tesla V100

November 28, 2021 by hgpu

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Yuke Wang, Boyuan Feng, Yufei Ding

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, TPU

November 21, 2021 by hgpu

Mixed precision in Graphics Processing Unit

Quentin Gallouédec

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Tags: Computer science, Machine learning, Mixed precision, Neural networks, nVidia, Tesla V100

October 31, 2021 by hgpu

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, Tesla T4

October 31, 2021 by hgpu

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Yue Niu, Ramy E. Ali, Salman Avestimehr

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 5000, Security

October 10, 2021 by hgpu

IgNet. A Super-precise Convolutional Neural Network

Igor Mackarov

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Tags: Computer science, Machine learning, Neural networks, OpenCL

September 26, 2021 by hgpu

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

Wei Niu, Zhengang Li, Xiaolong Ma, Peiyan Dong, Gang Zhou, Xuehai Qian, Xue Lin, Yanzhi Wang, Bin Ren

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Tags: Artificial intelligence, Code generation, Computer science, Deep learning, DSL, Neural networks, nVidia, nVidia Titan RTX, OpenCL

August 29, 2021 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

Optimization of Compiler-generated OpenCL CNN Kernels and Runtime for FPGAs

TCUDB: Accelerating Database with Tensor Processors

Manas: Mining Software Repositories to Assist AutoML

Analysis and Comparison of Performance and Power Consumption of Neural Networks on CPU, GPU, TPU and FPGA

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Mixed precision in Graphics Processing Unit

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

IgNet. A Super-precise Convolutional Neural Network

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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