Add theano.sandbox.cuda.synchronize() to allow to force the sync.

theano/sandbox/cuda/cuda_ndarray.cu

@@ -2451,6 +2451,16 @@ GetDeviceMemInfo(PyObject* _unused, PyObject* dummy)
     return PyTuple_Pack(2, PyLong_FromLong(free), PyLong_FromLong(total));
 }
 
+/*
+ * Returns in *free and *total respectively, the free and total amount of memory available for allocation by the device in bytes.
+ */
+PyObject *
+CudaNdarray_synchronize(PyObject* _unused, PyObject* dummy)
+{
+    cudaThreadSynchronize();
+    Py_INCREF(Py_None);
+    return Py_None;
+}
 #if COMPUTE_GPU_MEM_USED
 /*
  * Return the size in bytes that Theano currently have allocated on the gpu.
@@ -2958,6 +2968,7 @@ static PyMethodDef module_methods[] = {
     {"filter", filter, METH_VARARGS, "filter(obj, broadcastable, strict, storage) returns a CudaNdarray initialized to obj if it matches the constraints of broadcastable.  strict=True prevents any numeric casting. If storage is a CudaNdarray it may be overwritten and used as the return value."},
     {"outstanding_mallocs", outstanding_mallocs, METH_VARARGS, "how many more mallocs have been called than free's"},
     {"from_gpu_pointer", CudaNdarray_from_gpu_pointer, METH_VARARGS, "Used to create a CudaNdarray from already allocated memory on the gpu.(example by pycuda)"},
+    {"synchronize", CudaNdarray_synchronize, METH_NOARGS, "Used to synchronize the device"},
     {NULL, NULL, NULL, NULL}  /* Sentinel */
 };