work in progress

pyccel/ast/cudatypes.py

@@ -42,6 +42,10 @@ def __init__(self, dtype, rank, order, memory_location):
         self._memory_location = memory_location
         super().__init__()
 
+    @property
+    def memory_location(self):
+        return self._memory_location
+
     @lru_cache
     def __add__(self, other):
         test_type = np.zeros(1, dtype = pyccel_type_to_original_type[self.element_type])

pyccel/ast/variable.py

@@ -56,11 +56,6 @@ class Variable(TypedAstNode):
         'stack' if memory should be allocated on the stack, represents stack arrays and scalars.
         'alias' if object allows access to memory stored in another variable.
 
-    memory_location: str, default: 'host'
-        'host' the variable can only be accessed by the CPU.
-        'device' the variable can only be accessed by the GPU.
-        'managed' the variable can be accessed by CPU and GPU and is being managed by the Cuda API (memory transfer is being done implicitly).
-
     is_const : bool, default: False
         Indicates if object is a const argument of a function.
 
@@ -147,10 +142,6 @@ def __init__(
             raise ValueError("memory_handling must be 'heap', 'stack' or 'alias'")
         self._memory_handling = memory_handling
 
-        if memory_location not in ('host', 'device', 'managed'):
-            raise ValueError("memory_location must be 'host', 'device' or 'managed'")
-        self._memory_location = memory_location
-
         if not isinstance(is_const, bool):
             raise TypeError('is_const must be a boolean.')
         self._is_const = is_const
@@ -333,36 +324,6 @@ def cls_base(self):
         """
         return self._cls_base
 
-    @property
-    def memory_location(self):
-        """ Indicates whether a Variable has a dynamic size
-        """
-        return self._memory_location
-
-    @memory_location.setter
-    def memory_location(self, memory_location):
-        if memory_location not in ('host', 'device', 'managed'):
-            raise ValueError("memory_location must be 'host', 'device' or 'managed'")
-        self._memory_location = memory_location
-
-    @property
-    def on_host(self):
-        """  Indicates if memory is only accessible by the CPU
-        """
-        return self.memory_location == 'host'
-
-    @property
-    def on_device(self):
-        """ Indicates if memory is only accessible by the GPU
-        """
-        return self.memory_location == 'device'
-
-    @property
-    def is_managed(self):
-        """ Indicates if memory is being managed by CUDA API
-        """
-        return self.memory_location == 'managed'
-
     @property
     def is_const(self):
         """

pyccel/codegen/printing/cucode.py

@@ -157,7 +157,7 @@ def _print_Allocate(self, expr):
             raise NotImplementedError(f"Don't know how to index {variable.class_type} type")
         shape_Assign = "int64_t shape_Assign [] = {" + shape + "};\n"
         is_view = 'false' if variable.on_heap else 'true'
-        memory_location = expr.variable.memory_location
+        memory_location = variable.class_type.memory_location
         if memory_location in ('device', 'host'):
             memory_location = 'allocateMemoryOn' + str(memory_location).capitalize()
         else:

pyccel/stdlib/cuda_ndarrays/cuda_ndarrays.h

@@ -20,7 +20,6 @@ typedef enum e_types
 
 enum e_memory_locations
 {
-        managedMemory,
         allocateMemoryOnHost,
         allocateMemoryOnDevice
 };
@@ -39,8 +38,6 @@ typedef struct  s_cuda_ndarray
     /* shape 'size of each dimension' */
     int64_t                 *shape;
     /* strides 'number of elements to skip to get the next element' */
-    int64_t                 *strides;
-    /* type of the array elements */
     t_types            type;
     /* type size of the array elements */
     int32_t                 type_size;