This code demonstrates a usage of cuSOLVER Xgetrf/Xgetrs 64-bit functions for using dense LU factorization to solve a linear system
Ax = b
All matrices Ai are small perturbations of
A = | 1.0 | 2.0 | 3.0 |
| 4.0 | 5.0 | 6.0 |
| 7.0 | 8.0 | 10.0 |
The code uses getrf to do LU factorization and getrs to do backward and forward solve. The parameter pivot_on decides whether partial pivoting is performed or not.
All GPUs supported by CUDA Toolkit (https://developer.nvidia.com/cuda-gpus)
Linux
Windows
x86_64
ppc64le
arm64-sbsa
- A Linux/Windows system with recent NVIDIA drivers.
- CMake version 3.18 minimum
- Minimum CUDA 11.1 toolkit is required.
$ 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.
$ mkdir build
$ cd build
$ cmake -DCMAKE_GENERATOR_PLATFORM=x64 ..
$ Open cusolver_examples.sln project in Visual Studio and build
$ ./cusolver_Xgetrf_example
Sample example output:
pivot is on : compute P*A = L*U
A = (matlab base-1)
1.00 2.00 3.00
4.00 5.00 6.00
7.00 8.00 10.00
=====
B = (matlab base-1)
1.00
2.00
3.00
=====
Using New Algo
after Xgetrf: info = 0
pivoting sequence, matlab base-1
Ipiv(1) = 3
Ipiv(2) = 3
Ipiv(3) = 3
L and U = (matlab base-1)
7.00 8.00 10.00
0.14 0.86 1.57
0.57 0.50 -0.50
=====
X = (matlab base-1)
-0.33
0.67
0.00
=
pivot is off: compute A = L*U (not numerically stable)
A = (matlab base-1)
1.00 2.00 3.00
4.00 5.00 6.00
7.00 8.00 10.00
=====
B = (matlab base-1)
1.00
2.00
3.00
=====
Using New Algo
after Xgetrf: info = 0
L and U = (matlab base-1)
1.00 2.00 3.00
4.00 -3.00 -6.00
7.00 2.00 1.00
=====
X = (matlab base-1)
-0.33
0.67
0.00
=====