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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import NvRules
def get_identifier():
return "Occupancy"
def get_name():
return "Occupancy"
def get_description():
return "Occupancy section results analysis"
def get_section_identifier():
return "Occupancy"
def apply(handle):
ctx = NvRules.get_context(handle)
action = ctx.range_by_idx(0).action_by_idx(0)
fe = ctx.frontend()
theoretical_occupancy = action.metric_by_name("sm__maximum_warps_per_active_cycle_pct").as_double()
achieved_occupancy = action.metric_by_name("sm__warps_active.avg.pct_of_peak_sustained_active").as_double()
occupancy_difference = theoretical_occupancy - achieved_occupancy
messages = []
msg_type = NvRules.IFrontend.MsgType_MSG_OK
if theoretical_occupancy == 100:
messages.append("This kernel's theoretical occupancy is not impacted by any block limit.")
else:
msg_type = NvRules.IFrontend.MsgType_MSG_WARNING
limit_types = {
"blocks" : "the number of blocks that can fit on the SM",
"registers" : "the number of required registers",
"shared_mem" : "the required amount of shared memory",
"warps" : "the number of warps within each block"
}
limiters = []
for limiter in limit_types:
limit_value = action.metric_by_name("launch__occupancy_limit_{}".format(limiter)).as_uint64()
limit_msg = limit_types[limiter]
limiters.append((limiter, limit_value, limit_msg))
sorted_limiters = sorted(limiters, key=lambda limit: limit[1])
last_limiter = -1
for limiter in sorted_limiters:
value = limiter[1]
if last_limiter == -1 or value == last_limiter:
messages.append("This kernel's theoretical occupancy ({:.1f}%) is limited by {}".format(theoretical_occupancy, limiter[2]))
last_limiter = value
if occupancy_difference > 10:
msg_type = NvRules.IFrontend.MsgType_MSG_WARNING
messages.append("The difference between calculated theoretical ({:.1f}%) and measured achieved occupancy ({:.1f}%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution.".format(theoretical_occupancy, achieved_occupancy))
messages.append("Load imbalances can occur between warps within a block as well as across blocks of the same kernel.")
if len(messages) > 0:
if msg_type == NvRules.IFrontend.MsgType_MSG_WARNING:
messages.append("See the @url:CUDA Best Practices Guide:https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy@ for more details on optimizing occupancy.")
fe.message(msg_type, " ".join(messages), "Occupancy Limiters")
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