cuSOLVER QR Factorization example

Description

This code demonstrates a usage of cuSOLVER Xgetrf/Xgetrs 64-bit functions for using QR factorization of a m×n matrix

A = Q * R

where A is a m×n matrix, Q is a m×n matrix, and R is a n×n upper triangular matrix using the generic API interface.

A = | 1.0 | 2.0 | 3.0 |
    | 2.0 | 5.0 | 5.0 |
    | 3.0 | 5.0 | 12.0 |

Supported SM Architectures

All GPUs supported by CUDA Toolkit (https://developer.nvidia.com/cuda-gpus)

Supported OSes

Linux
Windows

Supported CPU Architecture

x86_64
ppc64le
arm64-sbsa

CUDA APIs involved

• cusolverDnXgeqrf API

Building (make)

Prerequisites

• A Linux/Windows system with recent NVIDIA drivers.
• CMake version 3.18 minimum
• Minimum CUDA 11.1 toolkit is required.

Build command on Linux

$ mkdir build
$ cd build
$ cmake ..
$ make

Make sure that CMake finds expected CUDA Toolkit. If that is not the case you can add argument -DCMAKE_CUDA_COMPILER=/path/to/cuda/bin/nvcc to cmake command.

Build command on Windows

$ mkdir build
$ cd build
$ cmake -DCMAKE_GENERATOR_PLATFORM=x64 ..
$ Open cusolver_examples.sln project in Visual Studio and build

Usage

$  ./cusolver_Xgeqrf_example

Sample example output:

A = (matlab base-1)
1.00 2.00 3.00
2.00 5.00 5.00
3.00 5.00 12.00
=====
B = (matlab base-1)
1.00
2.00
3.00
=====
after Xgeqrf: info = 0
tau = (matlab base-1)
1.27
1.80
0.00
=====
A = (matlab base-1)
-3.74 -7.22 -13.10
0.42 -1.39 2.52
0.63 -0.33 0.38
=====