This code demonstrates a usage of cuSOLVER sygvj function for using sygvj to compute spectrum of a pair of dense symmetric matrices (A,B) by
Ax = λBx
where A is a 3x3 dense symmetric matrix
A = | 3.5 | 0.5 | 0.0 |
| 0.5 | 3.5 | 0.0 |
| 0.0 | 0.0 | 2.0 |
where B is a 3x3 positive definite matrix
B = | 10.0 | 2.0 | 3.0 |
| 2.0 | 10.0 | 5.0 |
| 3.0 | 5.0 | 10.0 |
The following code uses sygvj to compute eigenvalues and eigenvectors.
All GPUs supported by CUDA Toolkit (https://developer.nvidia.com/cuda-gpus)
Linux
Windows
x86_64
ppc64le
arm64-sbsa
- cusolverDnDsygvj_bufferSize API
- cusolverDnDsygvj API
- cusolverDnXsyevjSetTolerance API
- cusolverDnXsyevjSetMaxSweeps API
- A Linux/Windows system with recent NVIDIA drivers.
- CMake version 3.18 minimum
- Minimum CUDA 9.0 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_sygvj_example
Sample example output:
tol = 1.000000E-07, default value is machine zero
max. sweeps = 15, default value is 100
A = (matlab base-1)
3.50 0.50 0.00
0.50 3.50 0.00
0.00 0.00 2.00
=====
B = (matlab base-1)
10.00 2.00 3.00
2.00 10.00 5.00
3.00 5.00 10.00
=====
sygvj converges
Eigenvalue = (matlab base-1), ascending order
W[1] = 1.586603E-01
W[2] = 3.707515E-01
W[3] = 6.000000E-01
V = (matlab base-1)
0.05 -0.31 -0.12
0.09 0.16 -0.31
0.24 0.02 0.29
=====
|lambda - W| = 3.330669E-16
residual |A - V*W*V**H|_F = 1.135989E-11
number of executed sweeps = 4