2. Run g-learn Functions on GPU

All functions in g-learn that accept the SLQ method (using method=slq argument) can perform computations on GPU devices. To do so, include gpu=True argument to the function syntax. The following examples show using multi-GPU devices to compute the log-determinant of a large matrix.

2.1. A Simple Example

First, create a sample Toeplitz matrix with ten million in size using imate.toeplitz() function.

>>> # Import toeplitz matrix
>>> from imate import toeplitz

>>> # Generate a sample matrix (a toeplitz matrix)
>>> n = 10000000
>>> A = toeplitz(2, 1, size=n, gram=True)

Next, create an imate.Matrix object from matrix A:

>>> # Import Matrix class
>>> from imate import Matrix

>>> # Create a matrix operator object from matrix A
>>> Aop = Matrix(A)

Compute the log-determinant of the above matrix on GPU by passing gpu=True to imate.logdet() function. Recall GPU can only be employed using SLQ method by passing method=slq argument.

>>> # Import logdet function
>>> from imate import logdet

>>> # Compute log-determinant of Aop
>>> logdet(Aop, method='slq', gpu=True)
13862193.020813728

2.2. Get Process Information

It is useful pass the argument return_info=True to get information about the computation process.

>>> # Compute log-determinant of Aop
>>> ld, info = logdet(Aop, method='slq', gpu=True, return_info=True)

The information about GPU devices used during the computation can be found in info['device'] key:

>>> from pprint import pprint
>>> pprint(info['device'])
{
    'num_cpu_threads': 8,
    'num_gpu_devices': 4,
    'num_gpu_multiprocessors': 28,
    'num_gpu_threads_per_multiprocessor': 2048
}

The processing time can be obtained by info['time'] key:

>>> pprint(info['time'])
{
    'alg_wall_time': 1.7192635536193848,
    'cpu_proc_time': 3.275628339,
    'tot_wall_time': 3.5191736351698637
}

2.3. Verbose Output

Alternatively, to print verbose information, including the information about GPU devices, pass verbose=True to the function argument:

>>> # Compute log-determinant of Aop
>>> logdet(Aop, method='slq', gpu=True, verbose=True)

The above script prints the following table. The last section of the table shows device information.

2.4. Set Number of GPU Devices

By default, g-learn employs the maximum number of available GPU devices. To employ a specific number of GPU devices, set num_gpu-devices in the function arguments. For instance

>>> # Import logdet function
>>> from imate import logdet

>>> # Compute log-determinant of Aop
>>> ld, info = logdet(Aop, method='slq', gpu=True, return_info=True,
...                   num_gpu_devices=2)

>>> # Check how many GPU devices used
>>> pprint(info['device'])
{
    'num_cpu_threads': 8,
    'num_gpu_devices': 2,
    'num_gpu_multiprocessors': 28,
    'num_gpu_threads_per_multiprocessor': 2048
}