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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 "FPInstructions"
def get_name():
return "FP32/64 Instructions"
def get_description():
return "Floating-point instruction analysis."
def get_section_identifier():
return "InstructionStats"
def apply(handle):
ctx = NvRules.get_context(handle)
action = ctx.range_by_idx(0).action_by_idx(0)
fe = ctx.frontend()
fp_types = {
32 : [ "FADD", "FMUL", "FFMA" ],
64 : [ "DADD", "DMUL", "DFMA" ]
}
# the correlation IDs of sass__inst_executed_per_opcode are the opcode mnemonics
inst_per_opcode = action.metric_by_name("sass__inst_executed_per_opcode")
num_opcodes = inst_per_opcode.num_instances()
opcodes = inst_per_opcode.correlation_ids()
# analyze both 32 and 64 bit
for fp_type in fp_types:
fp_insts = dict()
fp_opcodes = fp_types[fp_type]
# get number of instructions by opcode
for i in range(0,num_opcodes):
op = opcodes.as_string(i).upper()
if op in fp_opcodes:
fp_insts[op] = inst_per_opcode.as_uint64(i)
# calculate the sum of low- and high-throughput instructions
non_fused = 0
for i in range(0, 2):
op = fp_opcodes[i]
if op in fp_insts:
non_fused += fp_insts[op]
fused = 0
op = fp_opcodes[2]
if op in fp_insts:
fused += fp_insts[op]
if non_fused > 0 or fused > 0:
# high-throughput/fused instructions have twice the throughput of non-fused ones
ratio = (non_fused / (non_fused + fused)) / 2
if ratio > 0.1:
message = "This kernel executes {} fused and {} non-fused FP{} instructions.".format(fused, non_fused, fp_type)
message += " By converting pairs of non-fused instructions to their @url:fused:https://docs.nvidia.com/cuda/floating-point/#cuda-and-floating-point@, higher-throughput equivalent, the achieved FP{} performance could be increased by up to {:.0f}%"\
" (relative to its current performance)."\
" Check the Source page to identify where this kernel executes FP{} instructions.".format(fp_type, 100. * ratio, fp_type)
fe.message(NvRules.IFrontend.MsgType_MSG_WARNING, message)
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