high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

Performance Evaluation and Tuning of An OpenCL based Matrix Multiplier

Yiyu Tan, Toshiyuki Imamura

View

Download (PDF)

Tags: Computer science, FPGA, Linear Algebra, Matrix multiplication, OpenCL

September 2, 2018 by hgpu

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Tesla V100

September 2, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

View

Download (PDF)

Source codes

Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 by hgpu

Implementing general matrix-matrix multiplication algorithm on the Intel Xeon Phi Knights Landing Processor

Raehyun Kim

View

Download (PDF)

Tags: Algorithms, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Optimization

June 13, 2018 by hgpu

Learning to Optimize Tensor Programs

Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Download (PDF)

Tags: ARM, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X

May 26, 2018 by hgpu

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad, Aydin Buluc

View

Download (PDF)

Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, Sparse matrix

April 7, 2018 by hgpu

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Mehmet Deveci, Simon D. Hammond, Michael M. Wolf, Sivasankaran Rajamanickam

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

April 7, 2018 by hgpu

Design Principles for Sparse Matrix Multiplication on the GPU

Carl Yang, Aydin Buluc, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla K40

March 31, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

View

Download (PDF)

Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 2018 by hgpu

Rubus: A compiler for seamless and extensible parallelism

Muhammad Adnan, Faisal Aslam, Zubair Nawaz, Syed Mansoor Sarwar

View

Download (PDF)

Source codes

Tags: Aparapi, Benchmarking, Computer science, CUDA, Java, Matrix multiplication, nVidia, nVidia GeForce GT 630 M, OpenCL, OpenGL, Package

January 6, 2018 by hgpu

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

Gaurav Mitra

View

Download (PDF)

Tags: ARM, Benchmarking, Computer science, DSP, Heterogeneous systems, Matrix multiplication, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCL, SoC, Thesis

November 12, 2017 by hgpu

How to distribute most efficiently a computation intensive calculation on an Android device to external compute units with an Android API

Lander Beckers, Henning Lakiere

View

Download (PDF)

Tags: Android, Computer science, FPGA, Java, Matrix multiplication, OpenCL, SoC, Thesis

October 21, 2017 by hgpu

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Efficient and Scalable k-Means on GPUs

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Integer sorting on multicore

A study of integer sorting on multicores

See all packages

* * *

2019

February

25-28

Shanghai, China

International Conference on Image, Video and Signal Processing (IVSP), 2019

2018

November

20-22

Khalifa University, Abu Dhabi, UAE

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

2018

December

13-15

Wellington, New Zealand

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

2018

December

10-12

Wellington, New Zealand

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

2018

December

13-15

Wellington, New Zealand

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018

Add event

See all events

high performance computing on graphics processing units: hgpu.org

Performance Evaluation and Tuning of An OpenCL based Matrix Multiplier

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Implementing general matrix-matrix multiplication algorithm on the Intel Xeon Phi Knights Landing Processor

Learning to Optimize Tensor Programs

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Design Principles for Sparse Matrix Multiplication on the GPU

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Rubus: A compiler for seamless and extensible parallelism

Low-power System-on-Chip Processors for Energy Efficient High Performance Computing: The Texas Instruments Keystone II

How to distribute most efficiently a computation intensive calculation on an Android device to external compute units with an Android API

Recent source codes

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

ZUCL: A ZYNQ UltraScale+ Framework for OpenCL HLS Applications

CL-kmeans: Efficient k-Means in OpenCL

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

pyJac: a Python package that generates source code used to analytically calculate chemical kinetics Jacobian matrices

CUTLASS: CUDA Templates for Linear Algebra Subroutines

Integer sorting on multicore

Most viewed papers (last 30 days)

Featured events

International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018