high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, NLP, nVidia, nVidia A100, Package, Python, Tesla P100

February 7, 2021 by hgpu

Learning Massive Graph Embeddings on a Single Machine

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

View

Download (PDF)

Tags: Computer science, CUDA, Databases, Graph theory, Machine learning, nVidia, Tesla V100

January 24, 2021 by hgpu

Advances in Electron Microscopy with Deep Learning

Jeffrey M. Ede

View

Download (PDF)

Source codes

Tags: Deep learning, Electron microscopy, Image processing, Machine learning, Microscopy, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

January 10, 2021 by hgpu

When Machine Learning Meets Quantum Computers: A Case Study

Weiwen Jiang, Jinjun Xiong, Yiyu Shi

View

Download (PDF)

Tags: Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, Quantum computing

December 27, 2020 by hgpu

Solving Mixed Integer Programs Using Neural Networks

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, Ravichandra Addanki, Tharindi Hapuarachchi, Thomas Keck, James Keeling, Pushmeet Kohli, Ira Ktena, Yujia Li, Oriol Vinyals, Yori Zwols

View

Download (PDF)

Tags: Computer science, Deep learning, Machine learning, Mathematics, Neural networks, nVidia

December 27, 2020 by hgpu

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Maurizio Capra, Beatrice Bussolino, Alberto Marchisio, Guido Masera, Maurizio Martina, Muhammad Shafique

View

Download (PDF)

Tags: Artificial intelligence, Benchmarking, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Medicine, Neural networks, nVidia, Tesla V100

December 27, 2020 by hgpu

CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Alireza Ghaffari, Yvon Savaria

View

Download (PDF)

Tags: Computer science, Design space exploration, FPGA, Machine learning, Neural networks, OpenCL

December 27, 2020 by hgpu

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

Xuewen Cui

View

Download (PDF)

Tags: Artificial intelligence, Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, OpenMP, Performance, Tesla K40, Tesla P100, Tesla V100, Thesis

December 20, 2020 by hgpu

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Max Rietmann, Jose del Aguila Ferrandis, Wonmin Byeon, Zhiwei Fang, Sanjay Choudhry

View

Download (PDF)

Tags: cfd, CUDA, Fluid dynamics, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia A100, Partial differential equations, PDEs, Physics, Tesla V100, Video

December 20, 2020 by hgpu

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Chuan-Chi Wang, Ying-Chiao Liao, Ming-Chang Kao, Wen-Yew Liang, Shih-Hao Hung

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 6, 2020 by hgpu

Python Workflows on HPC Systems

Dominik Strassel, Philipp Reusch, Janis Keuper

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, HPC, Machine learning, nVidia, PC cluster, Python

December 6, 2020 by hgpu

Efficient Deep Neural Network Inference for Embedded Systems: A Mixture of Experts Approach

Benjamin David Taylor

View

Download (PDF)

Tags: Algorithms, Cloud, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TX2, Thesis

November 29, 2020 by hgpu

* * *

high performance computing on graphics processing units: hgpu.org

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Learning Massive Graph Embeddings on a Single Machine

Advances in Electron Microscopy with Deep Learning

When Machine Learning Meets Quantum Computers: A Case Study

Solving Mixed Integer Programs Using Neural Networks

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration

Directive-Based Data Partitioning and Pipelining and Auto-Tuning for High-Performance GPU Computing

NVIDIA SimNet: an AI-accelerated multi-physics simulation framework

Toward Accurate Platform-Aware Performance Modeling for Deep Neural Networks

Python Workflows on HPC Systems

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Most viewed papers (last 30 days)