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252 lines
10 KiB
252 lines
10 KiB
2 years ago
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import sys
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import os
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import json
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import pathlib
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import argparse
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import subprocess
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import platform
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import re
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import numpy
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import pandas
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from combine_profiles import combine_profiles
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def get_machine_info():
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if platform.system() == "Windows":
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return "Windows machine info not implemented"
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if platform.system() == "Darwin":
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core_count = int(subprocess.run(
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["sysctl", "-n", "machdep.cpu.core_count"],
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capture_output=True, text=True).stdout.strip())
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brand_string = subprocess.run(
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["sysctl", "-n", "machdep.cpu.brand_string"],
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capture_output=True, text=True).stdout.strip()
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memsize = int(subprocess.run(
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["sysctl", "-n", "hw.memsize"],
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capture_output=True, text=True).stdout.strip()) / 1024**3
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return f"{core_count:d}-core {brand_string}, {memsize} GB memory"
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if platform.system() == "Linux":
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lscpu = subprocess.run(
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["lscpu"], capture_output=True, text=True).stdout
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cores_per_socket = int(
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re.search(r"Core\(s\) per socket:\s*(\d*)", lscpu).group(1))
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sockets = int(
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re.search(r"Socket\(s\):\s*(\d*)", lscpu).group(1))
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cpu_freq = (
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float(re.search(r"CPU max MHz:\s*(.+)", lscpu).group(1)) / 1000)
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model_name = re.search(r"Model name:\s*(.+)", lscpu).group(1)
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meminfo = subprocess.run(
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["cat", "/proc/meminfo"], capture_output=True, text=True).stdout
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memsize = (
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int(re.search(r"MemTotal:\s*(\d*) kB", meminfo).group(1)) / 1024**2)
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return ((f"{sockets}x" if sockets > 1 else "")
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+ f"{cores_per_socket}-core {cpu_freq} GHz {model_name}, {memsize:.1f} GB memory")
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return ""
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def get_fixture_dir():
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return (pathlib.Path(__file__).parents[1] / "fixtures").resolve()
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def sim_exe_name():
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return "rigid_ipc_sim"
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def find_sim_exe():
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for build_dir in (pathlib.Path("."), pathlib.Path(__file__).parents[1] / "build"):
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for sub_dir in "", "release", "debug":
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sim_exe = build_dir / sub_dir / sim_exe_name()
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if sim_exe.is_file():
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return sim_exe.resolve()
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return None
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def create_parser():
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parser = argparse.ArgumentParser(
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description="Run all scenes and save a CSV of the results.")
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parser.add_argument(
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"--sim-exe", metavar=f"path/to/{sim_exe_name()}", type=pathlib.Path,
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default=find_sim_exe(), help="path to simulation executable")
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parser.add_argument(
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"--loglevel", default=3, type=int, choices=range(7),
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help="set log level 0=trace, 1=debug, 2=info, 3=warn, 4=error, 5=critical, 6=off")
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return parser
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def parse_arguments():
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parser = create_parser()
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args = parser.parse_args()
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if args.sim_exe is None:
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parser.exit(1, "Simulation executable is required!\n")
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return args
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def append_stem(p, stem_suffix):
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# return p.with_stem(p.stem + stem_suffix)
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return p.parent / (p.stem + stem_suffix + p.suffix)
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def main():
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args = parse_arguments()
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fixture_dir = get_fixture_dir()
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machine_info = get_machine_info()
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weak_1t_df = pandas.DataFrame(columns=[
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"num_threads", "num_bodies", "num_vertices", "num_edges", "num_faces",
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"timestep", "num_timesteps", "dhat", "eps_d", "avg_num_contacts",
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"max_num_contacts", "avg_solver_iterations", "max_solver_iterations",
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"avg_step_time", "max_step_time", "total_runtime (s)", "memory (MB)",
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"machine"])
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max_threads = 64
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"""
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for thread_count in range(1, max_threads + 1):
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scene = (fixture_dir / "3D" / "scalability" /
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"weak" / f"{thread_count:02d}threads.json")
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sim_output_dir = (pathlib.Path("output") / "3D" / "scalability" /
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"weak" / f"{thread_count:02d}threads")
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subprocess.run([str(args.sim_exe), --ngui, str(scene.resolve()),
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str(sim_output_dir),
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"--loglevel", str(args.loglevel),
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"--nthreads", str(1)
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])
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with open(sim_output_dir / "sim.json") as sim_output:
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sim_json = json.load(sim_output)
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sim_args = sim_json["args"]
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sim_stats = sim_json["stats"]
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df_row = {
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"num_threads": thread_count,
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"num_bodies": sim_stats["num_bodies"],
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"num_vertices": sim_stats["num_vertices"],
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"num_edges": sim_stats["num_edges"],
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"num_faces": sim_stats["num_faces"],
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"timestep": sim_args["timestep"],
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"num_timesteps": sim_stats["num_timesteps"],
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"dhat": sim_args["distance_barrier_constraint"]["initial_barrier_activation_distance"],
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"eps_d": sim_args["newton_solver"]["velocity_conv_tol"],
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"avg_num_contacts": numpy.average(sim_stats["num_contacts"]),
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"max_num_contacts": max(sim_stats["num_contacts"]),
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"avg_solver_iterations": numpy.average(sim_stats["solver_iterations"]),
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"max_solver_iterations": max(sim_stats["solver_iterations"]),
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"avg_step_time": numpy.average(sim_stats["step_timings"]),
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"max_step_time": max(sim_stats["step_timings"]),
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"total_runtime (s)": sum(sim_stats["step_timings"]),
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"memory (MB)": sim_stats["memory"] / 1024**2,
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"machine": machine_info,
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}
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weak_1t_df.loc[thread_count] = df_row
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weak_1t_df.to_csv("weak-scaling-single-thread.csv", index=False)
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print(f"Results written to weak-scaling-single-thread.csv")
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"""
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weak_df = pandas.DataFrame(columns=[
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"num_threads", "num_bodies", "num_vertices", "num_edges", "num_faces",
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"timestep", "num_timesteps", "dhat", "eps_d", "avg_num_contacts",
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"max_num_contacts", "avg_solver_iterations", "max_solver_iterations",
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"avg_step_time", "max_step_time", "total_runtime (s)", "memory (MB)",
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"machine"])
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for thread_count in range(1, max_threads + 1):
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scene = (fixture_dir / "3D" / "scalability" /
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"weak" / f"{thread_count:02d}threads.json")
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sim_output_dir = (pathlib.Path("output") / "3D" / "scalability" /
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"weak" / f"{thread_count:02d}threads")
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subprocess.run([str(args.sim_exe), "--ngui", str(scene.resolve()),
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str(sim_output_dir),
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"--loglevel", str(args.loglevel),
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"--nthreads", str(thread_count)
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])
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with open(sim_output_dir / "sim.json") as sim_output:
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sim_json = json.load(sim_output)
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sim_args = sim_json["args"]
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sim_stats = sim_json["stats"]
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df_row = {
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"num_threads": thread_count,
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"num_bodies": sim_stats["num_bodies"],
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"num_vertices": sim_stats["num_vertices"],
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"num_edges": sim_stats["num_edges"],
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"num_faces": sim_stats["num_faces"],
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"timestep": sim_args["timestep"],
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"num_timesteps": sim_stats["num_timesteps"],
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"dhat": sim_args["distance_barrier_constraint"]["initial_barrier_activation_distance"],
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"eps_d": sim_args["newton_solver"]["velocity_conv_tol"],
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"avg_num_contacts": numpy.average(sim_stats["num_contacts"]),
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"max_num_contacts": max(sim_stats["num_contacts"]),
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"avg_solver_iterations": numpy.average(sim_stats["solver_iterations"]),
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"max_solver_iterations": max(sim_stats["solver_iterations"]),
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"avg_step_time": numpy.average(sim_stats["step_timings"]),
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"max_step_time": max(sim_stats["step_timings"]),
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"total_runtime (s)": sum(sim_stats["step_timings"]),
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"memory (MB)": sim_stats["memory"] / 1024**2,
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"machine": machine_info,
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}
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weak_df.loc[thread_count] = df_row
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weak_df.to_csv("weak-scaling.csv", index=False)
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print(f"Results written to weak-scaling.csv")
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exit(0)
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strong_df = pandas.DataFrame(columns=[
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"num_threads", "num_bodies", "num_vertices", "num_edges", "num_faces",
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"timestep", "num_timesteps", "dhat", "eps_d", "avg_num_contacts",
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"max_num_contacts", "avg_solver_iterations", "max_solver_iterations",
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"avg_step_time", "max_step_time", "total_runtime (s)", "memory (MB)",
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"machine"])
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for thread_count in range(1, max_threads + 1):
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scene = fixture_dir / "3D" / "scalability" / "strong.json"
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sim_output_dir = pathlib.Path(
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"output") / "3D" / "scalability" / "strong"
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subprocess.run([str(args.sim_exe), "--ngui", str(scene.resolve()),
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str(sim_output_dir),
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"--loglevel", str(args.loglevel),
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"--nthreads", str(thread_count)
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])
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with open(sim_output_dir / "sim.json") as sim_output:
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sim_json = json.load(sim_output)
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sim_args = sim_json["args"]
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sim_stats = sim_json["stats"]
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df_row = {
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"num_threads": thread_count,
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"num_bodies": sim_stats["num_bodies"],
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"num_vertices": sim_stats["num_vertices"],
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"num_edges": sim_stats["num_edges"],
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"num_faces": sim_stats["num_faces"],
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"timestep": sim_args["timestep"],
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"num_timesteps": sim_stats["num_timesteps"],
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"dhat": sim_args["distance_barrier_constraint"]["initial_barrier_activation_distance"],
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"eps_d": sim_args["newton_solver"]["velocity_conv_tol"],
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"avg_num_contacts": numpy.average(sim_stats["num_contacts"]),
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"max_num_contacts": max(sim_stats["num_contacts"]),
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"avg_solver_iterations": numpy.average(sim_stats["solver_iterations"]),
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"max_solver_iterations": max(sim_stats["solver_iterations"]),
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"avg_step_time": numpy.average(sim_stats["step_timings"]),
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"max_step_time": max(sim_stats["step_timings"]),
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"total_runtime (s)": sum(sim_stats["step_timings"]),
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"memory (MB)": sim_stats["memory"] / 1024**2,
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"machine": machine_info,
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}
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strong_df.loc[thread_count] = df_row
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strong_df.to_csv("strong-scaling.csv", index=False)
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print(f"Results written to strong-scaling.csv")
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if __name__ == "__main__":
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main()
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