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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 "HighPipeUtilization"
def get_name():
return "High Pipe Utilization"
def get_description():
return "High pipe utilization bottleneck analysis"
def get_section_identifier():
return "ComputeWorkloadAnalysis"
def get_max_pipeline(pipelines, action, metric_names):
max_utilization = 0.0
max_pipe = None
for pipe in pipelines:
metric_name = pipe.metric
if metric_name in metric_names:
value = action.metric_by_name(metric_name).as_double()
if value > max_utilization:
max_utilization = value
max_pipe = pipe
return (max_pipe, max_utilization)
class Pipeline:
def __init__(self, name, metric, description = None):
self.name = name
self.metric = metric + ".avg.pct_of_peak_sustained_active"
self.description = description
def get_description(self, action, metric_names):
return self.description
class CompositePipeline(Pipeline):
def __init__(self, name, metric, description, sub_pipelines):
super().__init__(name, metric, description)
self.sub_pipelines = sub_pipelines
def get_description(self, action, metric_names):
description = self.description
(max_pipe, max_utilization) = get_max_pipeline(self.sub_pipelines, action, metric_names)
if max_pipe is not None:
description += ". It's dominated by its {} sub-pipeline".format(max_pipe.name)
return description
class SharedPipeline(CompositePipeline):
def __init__(self, name, metric, sub_pipelines):
super().__init__(name, metric, None, sub_pipelines)
def get_description(self, action, metric_names):
cc_major = action.metric_by_name("device__attribute_compute_capability_major").as_uint64()
cc_minor = action.metric_by_name("device__attribute_compute_capability_minor").as_uint64()
cc = cc_major * 10 + cc_minor
descriptions = {
70 : ". It executes 64- and 16-bit floating point and tensor operations",
72 : ". It executes 16-bit floating point and tensor operations",
75 : ". It executes 16-bit floating point and tensor operations",
80 : ". It executes 64-bit floating point and tensor operations",
90 : ". It executes 64-bit floating point and tensor operations",
}
description = "is the logical sum of several other pipelines which can't achieve full utilization on their own"
if cc in descriptions:
description += descriptions[cc]
self.description = description
description = super().get_description(action, metric_names)
return description
def apply(handle):
ctx = NvRules.get_context(handle)
action = ctx.range_by_idx(0).action_by_idx(0)
fe = ctx.frontend()
# Active cycles pipelines
# These are based on the number of cycles the pipeline was active.
# They take the rates of different instructions executing on the pipeline into account.
# We use these to categorize the overall compute pipeline utilization.
ac_pipelines = {
Pipeline("ALU", "sm__pipe_alu_cycles_active", "executes integer and logic operations"),
Pipeline("FMA", "sm__pipe_fma_cycles_active", "executes 32-bit floating point (FADD, FMUL, FMAD, ...) and integer (IMUL, IMAD) operations"),
Pipeline("FP64", "sm__pipe_fp64_cycles_active", "executes 64-bit floating point operations"),
SharedPipeline("Shared", "sm__pipe_shared_cycles_active",
[
Pipeline("FP64", "sm__pipe_fp64_cycles_active"),
Pipeline("Tensor (FP)", "sm__pipe_tensor_op_hmma_cycles_active"),
Pipeline("Tensor (INT)", "sm__pipe_tensor_op_imma_cycles_active"),
Pipeline("Tensor (DP)", "sm__pipe_tensor_op_dmma_cycles_active"),
]),
CompositePipeline("Tensor", "sm__pipe_tensor_cycles_active", "is the logical aggregation of individual tensor pipelines",
[
Pipeline("Tensor (FP)", "sm__pipe_tensor_op_hmma_cycles_active"),
Pipeline("Tensor (INT)", "sm__pipe_tensor_op_imma_cycles_active"),
Pipeline("Tensor (DP)", "sm__pipe_tensor_op_dmma_cycles_active"),
]
),
}
# Instruction executed pipelines
# They do not account for any variation in instruction latencies for this pipeline.
# We use these to understand the active cycles results in more detail.
inst_pipelines = {
Pipeline("ADU", "sm__inst_executed_pipe_adu"),
Pipeline("ALU", "sm__inst_executed_pipe_alu", "executes integer and logic operations"),
Pipeline("CBU", "sm__inst_executed_pipe_cbu"),
Pipeline("FMA", "sm__inst_executed_pipe_fma", "executes 32-bit floating point (FADD, FMUL, FMAD, ...) and integer (IMUL, IMAD) operations"),
Pipeline("FP16", "sm__inst_executed_pipe_fp16", "executes 16-bit floating point operations"),
Pipeline("FMA (FP16)", "sm__inst_executed_pipe_fma_type_fp16", "executes 16-bit floating point operations"),
Pipeline("FP64", "sm__inst_executed_pipe_fp64", "executes 64-bit floating point operations"),
Pipeline("FP64 (DMMA)", "sm__inst_executed_pipe_fp64_op_dmma", "executes DMMA operations"),
Pipeline("FP64 (FP64)", "sm__inst_executed_pipe_fp64_op_fp64", "executes non-DMMA 64-bit floating point operations"),
Pipeline("LSU", "sm__inst_executed_pipe_lsu", "executes load/store memory operations"),
Pipeline("Tensor (DP)", "sm__inst_executed_pipe_tensor_op_dmma", "executes 64-bit floating point tensor operations"),
Pipeline("Tensor (FP)", "sm__inst_executed_pipe_tensor_op_hmma", "executes 16-bit floating point tensor operations"),
Pipeline("Tensor (INT)", "sm__inst_executed_pipe_tensor_op_imma", "executes 4/8-bit integer tensor operations"),
Pipeline("TEX", "sm__inst_executed_pipe_tex", "executes texture/surface operations"),
Pipeline("Uniform", "sm__inst_executed_pipe_uniform"),
Pipeline("XU", "sm__inst_executed_pipe_xu"),
}
# several thresholds used to provide guidance
low_utilization_threshold = 20
high_utilization_threshold = 60
bottleneck_utilization_threshold = 80
# set of all collected metric names
metric_names = action.metric_names()
# get the dominant active cycles-based pipeline metric
(max_pipe_ac, max_utilization_ac) = get_max_pipeline(ac_pipelines, action, metric_names)
if max_pipe_ac is not None:
doc_msg = " See the @url:Kernel Profiling Guide:https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-decoder@ or hover over the pipeline name to understand the workloads handled by each pipeline."
inst_section_msg = " The @section:InstructionStats:Instruction Statistics@ section shows the mix of executed instructions in this kernel."
stall_msg = ""
issue_active_name = "smsp__issue_active.avg.per_cycle_active"
if issue_active_name in metric_names:
issue_active = action.metric_by_name(issue_active_name).as_double()
if issue_active < 0.8:
stall_msg = " Check the @section:WarpStateStats:Warp State Statistics@ section for which reasons cause warps to stall."
# compare the active cycles-based pipeline utilization aginst various thresholds to categorize the performance and provide guidance
if max_utilization_ac < low_utilization_threshold:
message = "All compute pipelines are under-utilized. Either this kernel is very small or it doesn't issue enough warps per scheduler."
message += " Check the @section:LaunchStats:Launch Statistics@ and @section:SchedulerStats:Scheduler Statistics@ sections for further details."
msg_id = fe.message(NvRules.IFrontend.MsgType_MSG_WARNING, message, "Low Utilization")
fe.focus_metric(msg_id, "max pipelines utilization", max_utilization_ac, NvRules.IFrontend.Severity_SEVERITY_HIGH, "{:.3f} < {:.2f}".format(max_utilization_ac, low_utilization_threshold))
else:
# descriptive info about the max active cycles pipe
message = "{} is the highest-utilized pipeline ({:.1f}%) based on active cycles, taking into account the rates of its different instructions.".format(max_pipe_ac.name, max_utilization_ac)
pipe_info = max_pipe_ac.get_description(action, metric_names)
if pipe_info is not None:
message += " It " + pipe_info + "."
if max_utilization_ac < high_utilization_threshold:
message_name = "Balanced"
message += " It is well-utilized, but should not be a bottleneck."
fe.message(NvRules.IFrontend.MsgType_MSG_OK, message, message_name)
else:
if max_utilization_ac < bottleneck_utilization_threshold:
message_name = "High Utilization"
message += " The pipeline is well-utilized, but might become a bottleneck if more work is added."
severity = NvRules.IFrontend.Severity_SEVERITY_DEFAULT
threshold = high_utilization_threshold
else:
message_name = "Very High Utilization"
message += " The pipeline is over-utilized and likely a performance bottleneck."
severity = NvRules.IFrontend.Severity_SEVERITY_LOW
threshold = bottleneck_utilization_threshold
# get the dominant instruction executed-based pipeline, too
(max_pipe_inst, max_utilization_inst) = get_max_pipeline(inst_pipelines, action, metric_names)
if max_pipe_inst is not None:
# descriptive info about the max instruction executed pipe
message += " Based on the number of executed instructions, the highest utilized pipeline ({:.1f}%) is {}.".format(max_utilization_inst, max_pipe_inst.name)
pipe_info_inst = max_pipe_inst.get_description(action, metric_names)
if pipe_info_inst is not None:
message += " It " + pipe_info_inst + "."
# compare its utilization to the active cycles metric
utilization_diff = max_utilization_inst / max_utilization_ac
if utilization_diff < 0.3:
message += " Comparing the two, the overall pipeline utilization appears to be caused by high-latency instructions."
elif utilization_diff > 0.7:
message += " Comparing the two, the overall pipeline utilization appears to be caused by frequent, low-latency instructions."
message += doc_msg + inst_section_msg + stall_msg
msg_id = fe.message(NvRules.IFrontend.MsgType_MSG_WARNING, message, message_name)
fe.focus_metric(msg_id, max_pipe_ac.metric, max_utilization_ac, severity, "{:.3f} >= {:.2f}".format(max_utilization_ac, threshold))
if max_pipe_inst is not None:
fe.focus_metric(msg_id, max_pipe_inst.metric, max_utilization_inst, severity, "max inst executed pipeline")
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