diff --git a/loopy/statistics.py b/loopy/statistics.py
index 91d15e7e079d250ec879fb84be45e7bb40fe4320..07f29d8ab5c39da02b3267464f2a0fa462b570ba 100755
--- a/loopy/statistics.py
+++ b/loopy/statistics.py
@@ -269,7 +269,7 @@ class ToCountMap(object):
params = {'n': 512, 'm': 256, 'l': 128}
mem_map = lp.get_mem_access_map(knl)
def filter_func(key):
- return key.stride > 1 and key.stride <= 4:
+ return key.lid_strides[0] > 1 and key.lid_strides[0] <= 4:
filtered_map = mem_map.filter_by_func(filter_func)
tot = filtered_map.eval_and_sum(params)
@@ -374,16 +374,16 @@ class ToCountMap(object):
params = {'n': 512, 'm': 256, 'l': 128}
s1_g_ld_byt = bytes_map.filter_by(
- mtype=['global'], stride=[1],
+ mtype=['global'], lid_strides={0: 1},
direction=['load']).eval_and_sum(params)
s2_g_ld_byt = bytes_map.filter_by(
- mtype=['global'], stride=[2],
+ mtype=['global'], lid_strides={0: 2},
direction=['load']).eval_and_sum(params)
s1_g_st_byt = bytes_map.filter_by(
- mtype=['global'], stride=[1],
+ mtype=['global'], lid_strides={0: 1},
direction=['store']).eval_and_sum(params)
s2_g_st_byt = bytes_map.filter_by(
- mtype=['global'], stride=[2],
+ mtype=['global'], lid_strides={0: 2},
direction=['store']).eval_and_sum(params)
# (now use these counts to, e.g., predict performance)
@@ -440,7 +440,7 @@ class ToCountMap(object):
params = {'n': 512, 'm': 256, 'l': 128}
mem_map = lp.get_mem_access_map(knl)
filtered_map = mem_map.filter_by(direction=['load'],
- variable=['a','g'])
+ variable=['a', 'g'])
tot_loads_a_g = filtered_map.eval_and_sum(params)
# (now use these counts to, e.g., predict performance)
@@ -553,10 +553,16 @@ class MemAccess(Record):
A :class:`loopy.LoopyType` or :class:`numpy.dtype` that specifies the
data type accessed.
- .. attribute:: stride
+ .. attribute:: lid_strides
- An :class:`int` that specifies stride of the memory access. A stride of
- 0 indicates a uniform access (i.e. all work-items access the same item).
+ An :class:`dict` of **{** :class:`int` **:**
+ :class:`pymbolic.primitives.Variable` or :class:`int` **}** that
+ specifies local strides for each local id in the memory access index.
+ Local ids not found will not be present in ``lid_strides.keys()``.
+ Uniform access (i.e. work-items within a sub-group access the same
+ item) is indicated by setting ``lid_strides[0]=0``, but may also occur
+ when no local id 0 is found, in which case the 0 key will not be
+ present in lid_strides.
.. attribute:: direction
@@ -966,11 +972,6 @@ class GlobalMemAccessCounter(MemAccessCounter):
ltag_stride += stride*coeff_lid
lid_strides[ltag] = ltag_stride
- # insert 0s for coeffs of missing *lesser* lids
- #for i in range(max(lid_strides.keys())+1):
- # if i not in lid_strides.keys():
- # lid_strides[i] = 0
-
count_granularity = CountGranularity.WORKITEM if (
0 in lid_strides and lid_strides[0] != 0
) else CountGranularity.SUBGROUP
@@ -1389,7 +1390,7 @@ def get_mem_access_map(knl, numpy_types=True, count_redundant_work=False,
f32_s1_g_ld_a = mem_map[MemAccess(
mtype='global',
dtype=np.float32,
- stride=1,
+ lid_strides={0: 1},
direction='load',
variable='a',
count_granularity=CountGranularity.WORKITEM)
@@ -1397,7 +1398,7 @@ def get_mem_access_map(knl, numpy_types=True, count_redundant_work=False,
f32_s1_g_st_a = mem_map[MemAccess(
mtype='global',
dtype=np.float32,
- stride=1,
+ lid_strides={0: 1},
direction='store',
variable='a',
count_granularity=CountGranularity.WORKITEM)
@@ -1405,7 +1406,7 @@ def get_mem_access_map(knl, numpy_types=True, count_redundant_work=False,
f32_s1_l_ld_x = mem_map[MemAccess(
mtype='local',
dtype=np.float32,
- stride=1,
+ lid_strides={0: 1},
direction='load',
variable='x',
count_granularity=CountGranularity.WORKITEM)
@@ -1413,7 +1414,7 @@ def get_mem_access_map(knl, numpy_types=True, count_redundant_work=False,
f32_s1_l_st_x = mem_map[MemAccess(
mtype='local',
dtype=np.float32,
- stride=1,
+ lid_strides={0: 1},
direction='store',
variable='x',
count_granularity=CountGranularity.WORKITEM)