hysop.backend.device.opencl.operator.directional.stretching_dir module

class hysop.backend.device.opencl.operator.directional.stretching_dir.OpenClDirectionalStretching(velocity, vorticity, vorticity_out, variables, **kwds)

Bases: OpenClDirectionalOperator

Directionnal stretching of vorticity in a given direction on opencl backend.

OpenCL kernels are build once per dimension in order to handle directional splitting with resolution non uniform in directions.

Parameters:

• velocity (Field) – Continuous velocity field (all components)

• vorticity (Field) – Input vorticity field.

• vorticity_out (Field, optional) – Output vorticity field.

• variables (dict) – Dictionary of continuous fields as keys and topologies as values.

• kwds – Extra parameters passed to generated directional operators.

velocity

Continuous velocity field (all components)

Type:

Field

vorticity_in

Input vorticity field.

Type:

list

vorticity_out

Output vorticity field.

Type:

list

apply(**kwds)

Abstract method that should be implemented. Applies this node (operator, computational graph operator…).

classmethod available_methods()

Returns the available methods of this node. This should return a dictionary of method as keys and possible values as a scalar or an iterable. See hysop.types.InstanceOf to support specific class types. This is used to check user method input.

classmethod default_method()

Returns the default method of this node. Default methods should be compatible with available_methods. If the user provided method dictionnaty misses some method keys, a default value for this key will be extracted from the default one.

discretize()

By default, an operator discretize all its variables. For each input continuous field that is also an output field, input topology may be different from the output topology.

After this call, one can access self.input_discrete_fields and self.output_discrete_fields, which contains input and output dicretised fields mapped by continuous fields.

self.discrete_fields will be a tuple containing all input and output discrete fields.

Discrete tensor fields are built back from discretized scalar fields and are accessible from self.input_tensor_fields, self.output_tensor_fields and self.discrete_tensor_fields like their scalar counterpart.

get_field_requirements()

Called just after handle_method(), ie self.method has been set. topology requirements are:

1. min and max ghosts for each input and output variables

2. allowed splitting directions for cartesian topologies

3) required local and global transposition state, if any. and more

they are stored in self.input_field_requirements and self.output_field_requirements.

keys are continuous fields and values are of type hysop.fields.field_requirement.discretefieldrequirements

default is backend.opencl, no min or max ghosts and no specific transposition state for each input and output variables.

handle_method(method)

Extract device configuration and precision from OpenClKernelConfig.

setup(work)

Setup temporary buffer that have been requested in get_work_properties(). This function may be used to execute post allocation routines. This sets self.ready flag to True. Once this flag is set one may call ComputationalGraphNode.apply() and ComputationalGraphNode.finalize().

Automatically honour temporary field memory requests.

classmethod supported_dimensions()

classmethod supports_mpi()

Return True if this operator was implemented to support multiple mpi processes.

classmethod supports_multiple_topologies()

Should return True if this node supports multiple topologies.

classmethod supports_multiscale()