high performance computing on graphics processing units: hgpu.org

hgpu.org » Deep learning

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Manolis Loukadakis, Jose Cano, Michael O'Boyle

View

Tags: ARM, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

February 10, 2018 by hgpu

MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster

Xin Chen, Hua Zhou, Yuxiang Gao, Yu Zhu, Dongyan Wang

View

Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Heterogeneous systems, nVidia, RDMA, Tesla P100

February 9, 2018 by hgpu

Accelerating recurrent neural network language model based online speech recognition system

Kyungmin Lee, Chiyoun Park, Namhoon Kim, Jaewon Lee

View

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, RNN, Speech recognition, Tesla K80

February 3, 2018 by hgpu

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

View

Tags: Computer science, Deep learning, FPGA, Neural networks, nVidia, Package, PTX

February 3, 2018 by hgpu

SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks

Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

View

Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

January 20, 2018 by hgpu

Evaluation of Machine Learning Fameworks on Finis Terrae II

Andres Gomez Tato

View

Tags: Algorithms, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Performance, Tesla K80

January 20, 2018 by hgpu

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W Lee

View

Tags: Benchmarking, Computer science, Deep learning, Intel Xeon Phi, Neural networks, Performance, TensorFlow

January 6, 2018 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

View

Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

View

Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

Tactics to Directly Map CNN graphs on Embedded FPGAs

Kamel Abdelouahab, Maxime Pelcat, Cedric Bourrasset, Francois Berry, Jocelyn Serot

View

Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, Package

December 19, 2017 by hgpu

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Jose Marques, Gabriel Falcao, Luis A. Alexandre

View

Tags: ATI, ATI Radeon HD 7500 M, CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Matlab, Neural networks, nVidia, nVidia GeForce GTX 840 M, nVidia GeForce GTX 940 M, nVidia GeForce GTX 950 M, OpenCL, Tesla K20

December 10, 2017 by hgpu

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

See all packages

* * *

* * *

HGPU group © 2010-2025 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us:

contact@hpgu.org