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# Copyright (c) 2021 The Regents of the University of California.
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"""
Script to run NAS parallel benchmarks with gem5. The script expects the
benchmark program to run. The input is in the format
<benchmark_prog>.<class>.x .The system is fixed with 2 CPU cores, MESI
Two Level system cache and 3 GB DDR4 memory. It uses the x86 board.
This script will count the total number of instructions executed
in the ROI. It also tracks how much wallclock and simulated time.
Usage:
------
```
scons build/X86/gem5.opt
./build/X86/gem5.opt \
configs/example/gem5_library/x86-npb-benchmarks.py \
--benchmark <benchmark_name> \
--size <benchmark_class>
```
"""
import argparse
import time
import m5
from m5.objects import Root
from gem5.utils.requires import requires
from gem5.components.boards.x86_board import X86Board
from gem5.components.memory import DualChannelDDR4_2400
from gem5.components.processors.simple_switchable_processor import(
SimpleSwitchableProcessor,
)
from gem5.components.processors.cpu_types import CPUTypes
from gem5.isas import ISA
from gem5.coherence_protocol import CoherenceProtocol
from gem5.resources.resource import Resource
from m5.stats.gem5stats import get_simstat
from m5.util import warn
requires(
isa_required = ISA.X86,
coherence_protocol_required=CoherenceProtocol.MESI_TWO_LEVEL,
kvm_required=True,
)
# Following are the list of benchmark programs for npb.
benchmark_choices = ["bt", "cg", "ep", "ft", "is", "lu", "mg", "sp"]
# We are restricting classes of NPB to A, B and C as the other classes (D and
# F) require main memory size of more than 3 GB. The X86Board is currently
# limited to 3 GB of memory. This limitation is explained later in line 136.
# The resource disk has binaries for class D. However, only `ep` benchmark
# works with class D in the current configuration. More information on the
# memory footprint for NPB is available at https://arxiv.org/abs/2010.13216
size_choices = ["A", "B", "C"]
parser = argparse.ArgumentParser(
description="An example configuration script to run the npb benchmarks."
)
# The only positional argument accepted is the benchmark name in this script.
parser.add_argument(
"--benchmark",
type = str,
required=True,
help = "Input the benchmark program to execute.",
choices = benchmark_choices,
)
parser.add_argument(
"--size",
type = str,
required=True,
help = "Input the class of the program to simulate.",
choices = size_choices,
)
parser.add_argument(
"--ticks",
type = int,
help = "Optionally put the maximum number of ticks to execute during the "\
"ROI. It accepts an integer value."
)
args = parser.parse_args()
# The simulation may fail in the case of `mg` with class C as it uses 3.3 GB
# of memory (more information is availabe at https://arxiv.org/abs/2010.13216).
# We warn the user here.
if args.benchmark == "mg" and args.size == "C":
warn("mg.C uses 3.3 GB of memory. Currently we are simulating 3 GB\
of main memory in the system.")
# The simulation will fail in the case of `ft` with class C. We warn the user
# here.
elif args.benchmark == "ft" and args.size == "C":
warn("There is not enough memory for ft.C. Currently we are\
simulating 3 GB of main memory in the system.")
# Checking for the maximum number of instructions, if provided by the user.
# Setting up all the fixed system parameters here
# Caches: MESI Two Level Cache Hierarchy
from gem5.components.cachehierarchies.ruby.\
mesi_two_level_cache_hierarchy import(
MESITwoLevelCacheHierarchy,
)
cache_hierarchy = MESITwoLevelCacheHierarchy(
l1d_size = "32kB",
l1d_assoc = 8,
l1i_size="32kB",
l1i_assoc=8,
l2_size="256kB",
l2_assoc=16,
num_l2_banks=2,
)
# Memory: Dual Channel DDR4 2400 DRAM device.
# The X86 board only supports 3 GB of main memory.
memory = DualChannelDDR4_2400(size = "3GB")
# Here we setup the processor. This is a special switchable processor in which
# a starting core type and a switch core type must be specified. Once a
# configuration is instantiated a user may call `processor.switch()` to switch
# from the starting core types to the switch core types. In this simulation
# we start with KVM cores to simulate the OS boot, then switch to the Timing
# cores for the command we wish to run after boot.
processor = SimpleSwitchableProcessor(
starting_core_type=CPUTypes.KVM,
switch_core_type=CPUTypes.TIMING,
isa=ISA.X86,
num_cores=2,
)
# Here we setup the board. The X86Board allows for Full-System X86 simulations
board = X86Board(
clk_freq="3GHz",
processor=processor,
memory=memory,
cache_hierarchy=cache_hierarchy,
)
# Here we set the FS workload, i.e., npb benchmark program
# After simulation has ended you may inspect
# `m5out/system.pc.com_1.device` to the stdout, if any.
# After the system boots, we execute the benchmark program and wait till the
# ROI `workbegin` annotation is reached (m5_work_begin()). We start collecting
# the number of committed instructions till ROI ends (marked by `workend`).
# We then finish executing the rest of the benchmark.
# Also, we sleep the system for some time so that the output is printed
# properly.
command="/home/gem5/NPB3.3-OMP/bin/{}.{}.x;".format(args.benchmark,args.size)\
+ "sleep 5;" \
+ "m5 exit;"
board.set_kernel_disk_workload(
# The x86 linux kernel will be automatically downloaded to the
# `~/.cache/gem5` directory if not already present.
# npb benchamarks was tested with kernel version 4.19.83
kernel=Resource(
"x86-linux-kernel-4.19.83",
),
# The x86-npb image will be automatically downloaded to the
# `~/.cache/gem5` directory if not already present.
disk_image=Resource(
"x86-npb",
),
readfile_contents=command,
)
# We need this for long running processes.
m5.disableAllListeners()
root = Root(full_system = True, system = board)
# sim_quantum must be set when KVM cores are used.
root.sim_quantum = int(1e9)
m5.instantiate()
# We maintain the wall clock time.
globalStart = time.time()
print("Running the simulation")
print("Using KVM cpu")
# We start the simulation.
exit_event = m5.simulate()
# The first exit_event ends with a `workbegin` cause. This means that the
# system started successfully and the execution on the program started.
if exit_event.getCause() == "workbegin":
print("Done booting Linux")
print("Resetting stats at the start of ROI!")
m5.stats.reset()
start_tick = m5.curTick()
# We have completed up to this step using KVM cpu. Now we switch to timing
# cpu for detailed simulation.
processor.switch()
else:
# `workbegin` call was never encountered.
print("Unexpected termination of simulation before ROI was reached!")
print(
"Exiting @ tick {} because {}.".format(
m5.curTick(),
exit_event.getCause()
)
)
exit(-1)
# The next exit_event is to simulate the ROI. It should be exited with a cause
# marked by `workend`.
# Next, we need to check if the user passed a value for --ticks. If yes,
# then we limit out execution to this number of ticks during the ROI.
# Otherwise, we simulate until the ROI ends.
if args.ticks:
exit_event = m5.simulate(args.ticks)
else:
exit_event = m5.simulate()
# Reached the end of ROI.
# We dump the stats here.
# We exepect that ROI ends with `workend` or `simulate() limit reached`.
# Otherwise the simulation ended unexpectedly.
if exit_event.getCause() == "workend":
print("Dump stats at the end of the ROI!")
m5.stats.dump()
end_tick = m5.curTick()
elif exit_event.getCause() == "simulate() limit reached" and \
args.ticks is not None:
print("Dump stats at the end of {} ticks in the ROI".format(args.ticks))
m5.stats.dump()
end_tick = m5.curTick()
else:
print("Unexpected termination of simulation while ROI was being executed!")
print(
"Exiting @ tick {} because {}.".format(
m5.curTick(),
exit_event.getCause()
)
)
exit(-1)
# We need to note that the benchmark is not executed completely till this
# point, but, the ROI has. We collect the essential statistics here before
# resuming the simulation again.
# We get simInsts using get_simstat and output it in the final
# print statement.
gem5stats = get_simstat(root)
# We get the number of committed instructions from the timing
# cores. We then sum and print them at the end.
roi_insts = float(\
gem5stats.to_json()\
["system"]["processor"]["cores2"]["core"]["exec_context.thread_0"]\
["numInsts"]["value"]
) + float(\
gem5stats.to_json()\
["system"]["processor"]["cores3"]["core"]["exec_context.thread_0"]\
["numInsts"]["value"]\
)
# Simulation is over at this point. We acknowledge that all the simulation
# events were successful.
print("All simulation events were successful.")
# We print the final simulation statistics.
print("Done with the simulation")
print()
print("Performance statistics:")
print("Simulated time in ROI: %.2fs" % ((end_tick-start_tick)/1e12))
print("Instructions executed in ROI: %d" % ((roi_insts)))
print("Ran a total of", m5.curTick()/1e12, "simulated seconds")
print("Total wallclock time: %.2fs, %.2f min" % \
(time.time()-globalStart, (time.time()-globalStart)/60))