Changeset b0de252 in sasmodels for sasmodels/kernelcl.py

Timestamp:

Oct 12, 2018 5:31:24 PM (6 years ago)

Author:

pkienzle

Branches:

master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

74e9b5f

Parents:

47fb816

Message:

improve control over cuda context

File:

• sasmodels/kernelcl.py (modified) (4 diffs)

sasmodels/kernelcl.py

@@ -1 +1 @@
 """
 GPU driver for C kernels
+
+TODO: docs are out of date
 
 There should be a single GPU environment running on the system.  This
@@ -59 +61 @@
 
 
-# Attempt to setup opencl. This may fail if the opencl package is not
+# Attempt to setup opencl. This may fail if the pyopencl package is not
 # installed or if it is installed but there are no devices available.
 try:
@@ -131 +133 @@
 
 def use_opencl():
-    return HAVE_OPENCL and os.environ.get("SAS_OPENCL", "").lower() != "none"
+    env = os.environ.get("SAS_OPENCL", "").lower()
+    return HAVE_OPENCL and env != "none" and not env.startswith("cuda")
 
 ENV = None
@@ -179 +182 @@
         cl.kernel_work_group_info.PREFERRED_WORK_GROUP_SIZE_MULTIPLE,
         queue.device)
-
-def _stretch_input(vector, dtype, extra=1e-3, boundary=32):
-    # type: (np.ndarray, np.dtype, float, int) -> np.ndarray
-    """
-    Stretch an input vector to the correct boundary.
-
-    Performance on the kernels can drop by a factor of two or more if the
-    number of values to compute does not fall on a nice power of two
-    boundary.   The trailing additional vector elements are given a
-    value of *extra*, and so f(*extra*) will be computed for each of
-    them.  The returned array will thus be a subset of the computed array.
-
-    *boundary* should be a power of 2 which is at least 32 for good
-    performance on current platforms (as of Jan 2015).  It should
-    probably be the max of get_warp(kernel,queue) and
-    device.min_data_type_align_size//4.
-    """
-    remainder = vector.size % boundary
-    if remainder != 0:
-        size = vector.size + (boundary - remainder)
-        vector = np.hstack((vector, [extra] * (size - vector.size)))
-    return np.ascontiguousarray(vector, dtype=dtype)
-
 
 def compile_model(context, source, dtype, fast=False):