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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"]["switch0"]["core"][
        "exec_context.thread_0"
    ]["numInsts"]["value"]
) + float(
    gem5stats.to_json()["system"]["processor"]["switch1"]["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)
)