high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Mohammadreza Soltaniyeh, Richard P. Martin, Santosh Nagarakatte

View

Download (PDF)

Tags: Computer science, Factorization, FPGA, HLS, Linear Algebra, Matrix multiplication, OpenCL, Sparse

May 10, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

View

Download (PDF)

Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

Jianhua Gao, Weixing Ji, Zhaonian Tan, Yueyan Zhao

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 1080 Ti, Sparse matrix, survey

March 1, 2020 by hgpu

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, Tesla K40, Tesla M40, Tesla P100

February 16, 2020 by hgpu

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Zoran Jaksic, Nicola Cadenelli, David Buchaca Prats, Jorda Polo, Josep Lluis Berral Garcia, David Carrera Perez

View

Download (PDF)

Tags: Computer science, FPGA, HLS, Linear Algebra, Matrix multiplication, Neural networks, OpenCL

November 17, 2019 by hgpu

Code Optimization on GPUs

Changwan Hong

View

Download (PDF)

Tags: Chemistry, Code generation, Computational chemistry, Computer science, CUDA, Intel Xeon Phi, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla P100, Thesis

November 3, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

Philippe Tillet, H. T. Kung, David Cox

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, CUDA, Deep learning, High-level Languages, LLVM, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1070, Package

June 30, 2019 by hgpu

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Zhen Xie, Guangming Tan, Weifeng Liu, Ninghui Sun

View

Download (PDF)

Source codes

Tags: Algorithms, Auto-Tuning, Computer science, CUDA, Deep learning, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

June 23, 2019 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

View

Download (PDF)

Source codes

Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

James D. Stevens

View

Download (PDF)

Tags: AMD Radeon R9 Fury, ATI, Code generation, Computer science, Finite difference, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 28, 2019 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

A Systematic Survey of General Sparse Matrix-Matrix Multiplication

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

A Highly Parameterizable Framework for Conditional Restricted Boltzmann Machine Based Workloads Accelerated With FPGAs and OpenCL

Code Optimization on GPUs

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

A mechanism for balancing accuracy and scope in cross-machine black-box GPU performance modeling

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)