jan/opencl_fdtd
2
2
Fork 1
Code Issues Pull Requests Releases Wiki Activity

doc updates

Browse Source
This commit is contained in:
jan 2017-08-24 11:28:03 -07:00
parent 0d91f0d43e
commit a85f547749
5 changed files with 59 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -27,10 +27,12 @@ electromagnetic simulations on parallel compute hardware (mainly GPUs).
* numpy * numpy
* pyopencl * pyopencl
* jinja2 * jinja2
* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools)
Optional (used for examples):
* dill (for file output) * dill (for file output)
* [gridlock](https://mpxd.net/gogs/jan/gridlock) * [gridlock](https://mpxd.net/gogs/jan/gridlock)
* [masque](https://mpxd.net/gogs/jan/masque) * [masque](https://mpxd.net/gogs/jan/masque)
* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools)
To get the code, just clone this repository: To get the code, just clone this repository:
```bash ```bash

52
opencl_fdtd/simulation.py
View File

@ -25,6 +25,35 @@ jinja_env = jinja2.Environment(loader=jinja2.PackageLoader(__name__, 'kernels'))
class Simulation(object): class Simulation(object):
""" """
Constructs and holds the basic FDTD operations and related fields Constructs and holds the basic FDTD operations and related fields
After constructing this object, call the (update_E, update_H, update_S) members
to perform FDTD updates on the stored (E, H, S) fields:
sim = Simulation(grid.grids, do_poynting=True, pml_thickness=8)
with open('sources.c', 'w') as f:
f.write('{}'.format(sim.sources))
for t in range(max_t):
sim.update_E([]).wait()
# Find the linear index for the center point, for Ey
ind = numpy.ravel_multi_index(tuple(grid.shape//2), dims=grid.shape, order='C') + \
numpy.prod(grid.shape) * 1
# Perturb the field (i.e., add a soft current source)
sim.E[ind] += numpy.sin(omega * t * sim.dt)
event = sim.update_H([])
if sim.update_S:
event = sim.update_S([event])
event.wait()
with lzma.open('saved_simulation', 'wb') as f:
dill.dump(fdfd_tools.unvec(sim.E.get(), grid.shape), f)
Code in the form
event2 = sim.update_H([event0, event1])
indicates that the update_H operation should be prepared immediately, but wait for
event0 and event1 to occur (i.e. previous operations to finish) before starting execution.
event2 can then be used to prepare further operations to be run after update_H.
""" """
E = None # type: List[pyopencl.array.Array] E = None # type: List[pyopencl.array.Array]
H = None # type: List[pyopencl.array.Array] H = None # type: List[pyopencl.array.Array]
@ -37,9 +66,9 @@ class Simulation(object):
context = None # type: pyopencl.Context context = None # type: pyopencl.Context
queue = None # type: pyopencl.CommandQueue queue = None # type: pyopencl.CommandQueue
update_E = None # type: Callable[[],pyopencl.Event] update_E = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
update_H = None # type: Callable[[],pyopencl.Event] update_H = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
update_S = None # type: Callable[[],pyopencl.Event] update_S = None # type: Callable[[List[pyopencl.Event]], pyopencl.Event]
sources = None # type: Dict[str, str] sources = None # type: Dict[str, str]
def __init__(self, def __init__(self,
@ -50,8 +79,8 @@ class Simulation(object):
context: pyopencl.Context = None, context: pyopencl.Context = None,
queue: pyopencl.CommandQueue = None, queue: pyopencl.CommandQueue = None,
float_type: numpy.float32 or numpy.float64 = numpy.float32, float_type: numpy.float32 or numpy.float64 = numpy.float32,
pml_thickness: int = 10,
pmls: List[List[str]] = None, pmls: List[List[str]] = None,
pml_thickness: int = 10,
do_poynting: bool = True): do_poynting: bool = True):
""" """
Initialize the simulation. Initialize the simulation.
@ -64,6 +93,21 @@ class Simulation(object):
:param context: pyOpenCL context. If not given, pyopencl.create_some_context(False) is called. :param context: pyOpenCL context. If not given, pyopencl.create_some_context(False) is called.
:param queue: pyOpenCL command queue. If not given, pyopencl.CommandQueue(context) is called. :param queue: pyOpenCL command queue. If not given, pyopencl.CommandQueue(context) is called.
:param float_type: numpy.float32 or numpy.float64. Default numpy.float32. :param float_type: numpy.float32 or numpy.float64. Default numpy.float32.
:param pmls: List of [axis, direction] pairs which specify simluation boundaries to be
'coated' with a PML (absorbing layer). Axis should be one of 'x', 'y', 'z', and
direction should be one of 'n', 'p' (i.e., negative, positive).
Default is to apply PMLs to all six boundaries.
:param pml_thickness: Thickness of any PMLs, in number of grid cells. Default 10.
:param do_poynting: If true, enables calculation of the poynting vector, S.
Poynting vector calculation adds the following computational burdens:
* During update_H, ~6 extra additions/cell are performed in order to spatially
average E and temporally average H. These quantities are multiplied
(6 multiplications/cell) and then stored (6 writes/cell, cache-friendly).
* update_S performs a discrete cross product using the precalculated products
from update_H. This is not nice to the cache and similar to e.g. update_E
in complexity.
* GPU memory requirements are approximately doubled, since S and the intermediate
products must be stored.
""" """
if len(epsilon) != 3: if len(epsilon) != 3: