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-rw-r--r--ldc_2d/opencl/simulation.py105
1 files changed, 105 insertions, 0 deletions
diff --git a/ldc_2d/opencl/simulation.py b/ldc_2d/opencl/simulation.py
new file mode 100644
index 0000000..7625609
--- /dev/null
+++ b/ldc_2d/opencl/simulation.py
@@ -0,0 +1,105 @@
+import pyopencl as cl
+mf = cl.mem_flags
+
+import numpy
+
+class Memory:
+ def __init__(self, descriptor, geometry, context, float_type):
+ self.context = context
+ self.float_type = float_type
+
+ self.size_x = geometry.size_x
+ self.size_y = geometry.size_y
+ self.size_z = geometry.size_z
+ self.volume = self.size_x * self.size_y * self.size_z
+
+ self.pop_size = descriptor.q * self.volume * self.float_type(0).nbytes
+ self.moments_size = 3 * self.volume * self.float_type(0).nbytes
+
+ self.cl_pop_a = cl.Buffer(self.context, mf.READ_WRITE, size=self.pop_size)
+ self.cl_pop_b = cl.Buffer(self.context, mf.READ_WRITE, size=self.pop_size)
+
+ self.cl_moments = cl.Buffer(self.context, mf.WRITE_ONLY, size=self.moments_size)
+
+ def gid(self, x, y, z = 0):
+ return z * (self.size_x*self.size_y) + y * self.size_x + x;
+
+class CellList:
+ def __init__(self, context, queue, float_type, cells):
+ self.cl_cells = cl.Buffer(context, mf.READ_ONLY, size=len(cells) * numpy.uint32(0).nbytes)
+ self.np_cells = numpy.ndarray(shape=(len(cells), 1), dtype=numpy.uint32)
+ self.np_cells[:,0] = cells[:]
+
+ cl.enqueue_copy(queue, self.cl_cells, self.np_cells).wait();
+
+ def get(self):
+ return self.cl_cells
+
+ def size(self):
+ return (len(self.np_cells), 1, 1)
+
+class Lattice:
+ def __init__(self, geometry, kernel_src, descriptor, platform = 0, precision = 'single'):
+ self.geometry = geometry
+ self.descriptor = descriptor
+
+ self.float_type = {
+ 'single': (numpy.float32, 'float'),
+ 'double': (numpy.float64, 'double'),
+ }.get(precision, None)
+
+ self.platform = cl.get_platforms()[platform]
+ self.layout = None
+
+ self.context = cl.Context(
+ properties=[(cl.context_properties.PLATFORM, self.platform)])
+
+ self.queue = cl.CommandQueue(self.context)
+
+ self.memory = Memory(descriptor, self.geometry, self.context, self.float_type[0])
+ self.tick = False
+
+ self.compiler_args = {
+ 'single': '-cl-single-precision-constant -cl-fast-relaxed-math',
+ 'double': '-cl-fast-relaxed-math'
+ }.get(precision, None)
+
+ self.build_kernel(kernel_src)
+
+ self.program.equilibrilize(
+ self.queue, self.geometry.size(), self.layout, self.memory.cl_pop_a, self.memory.cl_pop_b).wait()
+
+ self.tasks = []
+
+ def build_kernel(self, src):
+ self.program = cl.Program(self.context, src).build(self.compiler_args)
+
+ def schedule(self, f, cells, *params):
+ self.tasks += [ (eval("self.program.%s" % f), cells, params) ]
+
+ def evolve(self):
+ if self.tick:
+ self.tick = False
+ for f, cells, params in self.tasks:
+ f(self.queue, cells.size(), self.layout, self.memory.cl_pop_a, self.memory.cl_pop_b, cells.get(), *params)
+ else:
+ self.tick = True
+ for f, cells, params in self.tasks:
+ f(self.queue, cells.size(), self.layout, self.memory.cl_pop_b, self.memory.cl_pop_a, cells.get(), *params)
+
+ def sync(self):
+ self.queue.finish()
+
+ def get_moments(self):
+ moments = numpy.ndarray(shape=(self.memory.volume*(self.descriptor.d+1),1), dtype=self.float_type[0])
+
+ if self.tick:
+ self.program.collect_moments(
+ self.queue, self.geometry.size(), self.layout, self.memory.cl_pop_b, self.memory.cl_moments)
+ else:
+ self.program.collect_moments(
+ self.queue, self.geometry.size(), self.layout, self.memory.cl_pop_a, self.memory.cl_moments)
+
+ cl.enqueue_copy(self.queue, moments, self.memory.cl_moments).wait();
+
+ return moments