high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

View

Download (PDF)

Tags: Computer science, GPU cluster, Machine learning, nVidia, Task scheduling, Tesla K80, Tesla M60

July 4, 2019 by hgpu

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

Newsha Ardalani, Urmish Thakker, Aws Albarghouthi, Karu Sankaralingam

View

Download (PDF)

Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX 660 Ti, nVidia GeForce GTX 750, Performance

June 23, 2019 by hgpu

TensorNetwork for Machine Learning

Stavros Efthymiou, Jack Hidary, Stefan Leichenauer

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, nVidia, Package, Tesla P100

June 20, 2019 by hgpu

High-Performance Deep Learning via a Single Building Block

Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar, Sasikanth Avancha, Anand Venkat, Michael Anderson, Greg Henry, Hans Pabst, Alexander Heinecke

View

Download (PDF)

Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, OpenCL, Performance

June 20, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

View

Download (PDF)

Source codes

Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

Dynamic Distribution Pruning for Efficient Network Architecture Search

Xiawu Zheng, Rongrong Ji, Lang Tang, Yan Wan, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla V100

June 5, 2019 by hgpu

Classify QCD phase transition with deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stoecker, Xin-Nian Wang

View

Download (PDF)

Source codes

Tags: Deep learning, HEP, Machine learning, Neural networks, OpenCL, Package, Pattern recognition, Phase transition, Physics, QCD

June 2, 2019 by hgpu

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Yunfei Teng, Wenbo Gao, Francois Chalus, Anna Choromanska, Donald Goldfarb, Adrian Weller

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080

June 2, 2019 by hgpu

Performance Analysis of Deep Learning Workloads on Leading-edge Systems

Yihui Ren, Shinjae Yoo, Adolfy Hoisie

View

Download (PDF)

Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, Performance, Tesla V100

May 23, 2019 by hgpu

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Muhammad Mohid Nabil

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, FPGA, HLS, Image recognition, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Precision, Thesis

May 12, 2019 by hgpu

TensorNetwork: A Library for Physics and Machine Learning

Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, Stefan Leichenauer

View

Download (PDF)

Source codes

Tags: Machine learning, Package, Physics, TensorFlow

May 8, 2019 by hgpu

Full-stack Optimization for Accelerating CNNs with FPGA Validation

Bradley McDanel, Sai Qian Zhang, H. T. Kung, Xin Dong

View

Download (PDF)

Source codes

Tags: CNN, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, Package

May 5, 2019 by hgpu

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning

TensorNetwork for Machine Learning

High-Performance Deep Learning via a Single Building Block

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Dynamic Distribution Pruning for Efficient Network Architecture Search

Classify QCD phase transition with deep learning

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

FPGA Implementation of Reduced Precision Convolutional Neural Networks

TensorNetwork: A Library for Physics and Machine Learning

Full-stack Optimization for Accelerating CNNs with FPGA Validation

Recent source codes

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

LC Framework

pplx-garden: Perplexity open source garden for inference technology

Most viewed papers (last 30 days)