high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

GPU Array Access Auto-Tuning

Nicolas Weber

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GT 620, nVidia GeForce GT 730, nVidia GeForce GTX 1080, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 590, nVidia GeForce GTX 680, nVidia GeForce GTX 780, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Performance, performance portability, Tesla C2070, Tesla K20, Thesis

August 8, 2017 by hgpu

AutOMP: An Automatic OpenMP Parallelization Generator for Variable-Oriented High-Performance Scientific Codes

Gal Oren, Yehuda Ganan, Guy Malamud

View

Tags: Code generation, Computer science, Intel Xeon Phi, OpenMP, Performance

August 1, 2017 by hgpu

Directive-Based Partitioning and Pipelining for Graphical Processing Units

X. Cui, T. R. W. Scogland, B. R. de Supinski, W. Feng

View

Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenACC, OpenCL, OpenMP, Performance, Task scheduling, Tesla K40

August 1, 2017 by hgpu

On Simplifying and Optimizing Programs for Heterogeneous Computing Systems

Ivan Grasso

View

Tags: AMD FirePro S9000, ATI, ATI Radeon HD 5870, Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla K20, Thesis

July 25, 2017 by hgpu

Parallelization of an Unsteady ALE Solver with Deforming Mesh Using OpenACC

Wenpeng Ma, Zhonghua Lu, Wu Yuan, Xiaodong Hu

View

Tags: Algorithms, Computer science, nVidia, OpenACC, OpenMP, Performance, Tesla K20, Triangular meshes

July 22, 2017 by hgpu

Automatically Selecting Profitable Thread Block Sizes Using Machine Learning

Tiffany A. Connors

View

Tags: Computer science, CUDA, Machine learning, nVidia, Performance, Tesla K40, Thesis

July 22, 2017 by hgpu

Cooperative Kernels: GPU Multitasking for Blocking Algorithms

Tyler Sorensen, Hugues Evrard, Alastair F. Donaldson

View

Tags: Algorithms, Computer science, OpenCL, Performance, Programming Languages

July 14, 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

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Jiazhen Gu, Huan Liu, Yangfan Zhou, Xin Wang

View

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Suffix trees

July 14, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

View

Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs

Siddharth Samsi, Brian Helfer, Jeremy Kepner, Albert Reuther, Darrell O. Ricke

View

Tags: Algorithms, Computer science, CUDA, Genomics, Linear Algebra, Matrix multiplication, nVidia, Performance, Tesla K80

July 5, 2017 by hgpu

Parle: parallelizing stochastic gradient descent

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

View

Tags: Algorithms, Computer science, CUDA, Deep learning, Lua, Machine learning, nVidia, Package, Performance, Torch

July 5, 2017 by hgpu

Page 1 of 8912 3 4 5...10 20 30...»Last »

Scaling Deep Learning on GPU and Knights Landing clusters

Scaling Deep Learning on GPU and Knights Landing clusters

Gpufit: GPU-accelerated Levenberg-Marquardt curve fitting in CUDA

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

GARDENIA: Graph Analytics Repository for Designing Next-Generation Accelerators

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

GPU-benchmarks

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

bit-reversal-methods

Practically efficient methods for performing bit-reversed permutation in C++11 on the x86-64 architecture

bifrost: A stream processing framework for high-throughput applications

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy

NEMATUS: Open-Source Neural Machine Translation in Theano

Deep Architectures for Neural Machine Translation

ParTeCL: parallel testing using OpenCL

ParTeCL: parallel testing using OpenCL

DenseNet: Densely Connected Convolutional Networks

Memory-Efficient Implementation of DenseNets

SVMbenchmark: CUDA and OpenCL SVM training benchmark

A Comparison of Support Vector Machines Training GPU-Accelerated Open Source Implementations

See all packages

* * *

* * *

2018

February

26-28

Singapore

International Conference on Intelligent Autonomous Systems (ICIAS), 2018

2018

February

21-23

Amsterdam, Netherlands

International Conference on Robotics and Intelligent System (ICRIS), 2018

2018

February

24-26

Hong Kong

International Conference on Image and Graphics Processing (ICIGP), 2018

2018

January

18-20

Tokyo, Japan

8th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB), 2018

2018

January

8-10

Sydney, Australia

The 10th International Conference on Computer Modeling and Simulation (ICCMS), 2018

+

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: