Opencl datahandling

Since the target_dh_refactoring was never merged, we decided to do the refactoring at smaller steps and recover the original OpenCL datahandling PR.

Refactoring to merge target and backend can be done later.

pystencils/datahandling/serial_datahandling.py

@@ -16,6 +16,9 @@ from pystencils.utils import DotDict
 
 class SerialDataHandling(DataHandling):
 
+    _GPU_LIKE_TARGETS = ['gpu', 'opencl']
+    _GPU_LIKE_BACKENDS = ['gpucuda', 'opencl']
+
     def __init__(self,
                  domain_size: Sequence[int],
                  default_ghost_layers: int = 1,
@@ -48,17 +51,16 @@ class SerialDataHandling(DataHandling):
         self._opencl_queue = opencl_queue
         self._opencl_ctx = opencl_ctx
 
-        if array_handler:
-            self.array_handler = array_handler
-        else:
+        if not array_handler:
             try:
                 self.array_handler = PyCudaArrayHandler()
             except Exception:
                 self.array_handler = None
 
             if default_target == 'opencl' or opencl_queue:
-                default_target = 'gpu'
                 self.array_handler = PyOpenClArrayHandler(opencl_queue)
+        else:
+            self.array_handler = array_handler
 
         if periodicity is None or periodicity is False:
             periodicity = [False] * self.dim
@@ -99,7 +101,7 @@ class SerialDataHandling(DataHandling):
         if layout is None:
             layout = self.default_layout
         if gpu is None:
-            gpu = self.default_target == 'gpu'
+            gpu = self.default_target in self._GPU_LIKE_TARGETS
 
         kwargs = {
             'shape': tuple(s + 2 * ghost_layers for s in self._domainSize),
@@ -239,13 +241,12 @@ class SerialDataHandling(DataHandling):
             self.to_gpu(name)
 
     def run_kernel(self, kernel_function, **kwargs):
-        arrays = self.gpu_arrays if kernel_function.ast.backend == 'gpucuda'  \
-            or kernel_function.ast.backend == 'opencl' else self.cpu_arrays
+        arrays = self.gpu_arrays if kernel_function.ast.backend in self._GPU_LIKE_BACKENDS else self.cpu_arrays
         kernel_function(**arrays, **kwargs)
 
     def get_kernel_kwargs(self, kernel_function, **kwargs):
         result = {}
-        result.update(self.gpu_arrays if kernel_function.ast.backend == 'gpucuda' else self.cpu_arrays)
+        result.update(self.gpu_arrays if kernel_function.ast.backend in self._GPU_LIKE_BACKENDS else self.cpu_arrays)
         result.update(kwargs)
         return [result]