high performance computing on graphics processing units: hgpu.org

hgpu.org » Caffe

Early Results of Deep Learning on the Stampede2 Supercomputer

Zhao Zhang, Weijia Xu, Niall Gaffney, Daniel Stanzione

View

Tags: Caffe, Computer science, Deep learning, Intel Xeon Phi, TensorFlow

October 29, 2017 by hgpu

Meta Networks for Neural Style Transfer

Falong Shen, Shuicheng Yan, Gang Zeng

View

Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package

September 16, 2017 by hgpu

Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor

Chansup Byun, Jeremy Kepner, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Anna Klein, Peter Michaleas, Lauren Milechin, Julie Mullen, Andrew Prout, Antonio Rosa, Siddharth Samsi, Charles Yee, Albert Reuther

View

Tags: Benchmarking, Caffe, Computer science, Deep learning, Intel Xeon Phi, Machine learning, Performance

July 14, 2017 by hgpu

Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

Siyang Qin, Roberto Manduchi

View

Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan X

April 7, 2017 by hgpu

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

View

Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 by hgpu

Deep, Dense, and Low-Rank Gaussian Conditional Random Fields

Siddhartha Chandra, Iasonas Kokkinos

View

Tags: Caffe, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1080

November 30, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

View

Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Bingchen Gong, Brendan Jou, Felix Yu, Shih-Fu Chang

View

Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, TensorFlow

November 8, 2016 by hgpu

Implementation of Frequency Domain Convolution for the Caffe-Framework

Philipp Harzig

View

Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Thesis

October 8, 2016 by hgpu

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi

View

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

October 4, 2016 by hgpu

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Janis Keuper

View

Tags: Caffe, Computer science, CUDA, Deep learning, Intel Xeon Phi, Neural networks, nVidia, Tesla K80

September 30, 2016 by hgpu

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

R. Tapiador, A. Rios-Navarro, A. Linares-Barranco, Minkyu Kim, Deepak Kadetotad, Jae-sun Seo

View

Tags: Artificial intelligence, Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

September 30, 2016 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: