395 lines
11 KiB
Python
395 lines
11 KiB
Python
# SPDX-FileCopyrightText: 2021 Guillaume DIDIER
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#
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: MIT
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import os
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import sys
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import argparse
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import warnings
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import itertools
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from multiprocessing import Pool
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from scipy import optimize
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import matplotlib.pyplot as plt
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import matplotlib.style as mplstyle
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mplstyle.use("fast")
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warnings.filterwarnings("ignore")
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print("warnings are filtered, enable them back if you are having some trouble")
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# TODO
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# args.path should be the root
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# with root-result_lite.csv.bz2 the result
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# and .stats.csv
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# root.slices a slice mapping - done
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# root.cores a core + socket mapping - done -> move to analyse csv ?
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#
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# Facet plot with actual dot cloud + plot the linear regression
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# each row is a slice
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# each row is an origin core
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# each column a helper core if applicable
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parser = argparse.ArgumentParser(
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prog=sys.argv[0],
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)
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parser.add_argument("path", help="Path to the experiment files")
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parser.add_argument(
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"--no-plot",
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dest="no_plot",
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action="store_true",
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default=False,
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help="No visible plot (save figures to files)",
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)
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parser.add_argument(
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"--rslice",
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dest="rslice",
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action="store_true",
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default=False,
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help="Create slice{} directories with segmented grid",
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)
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parser.add_argument(
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"--no-slice-remap",
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dest="slice_remap",
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action="store_false",
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default=True,
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help="Don't remap the slices"
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)
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args = parser.parse_args()
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img_dir = os.path.dirname(args.path) + "/figs/"
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os.makedirs(img_dir, exist_ok=True)
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assert os.path.exists(args.path + ".stats.csv")
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assert os.path.exists(args.path + ".cores.csv")
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if args.slice_remap:
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assert os.path.exists(args.path + ".slices.csv")
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stats = pd.read_csv(
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args.path + ".stats.csv",
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dtype={
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"main_core": np.int8,
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"helper_core": np.int8,
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# "address": int,
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"hash": np.int8,
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# "time": np.int16,
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"clflush_remote_hit": np.float64,
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"clflush_shared_hit": np.float64,
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# "clflush_miss_f": np.int32,
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# "clflush_local_hit_f": np.int32,
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"clflush_miss_n": np.float64,
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"clflush_local_hit_n": np.float64,
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# "reload_miss": np.int32,
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# "reload_remote_hit": np.int32,
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# "reload_shared_hit": np.int32,
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# "reload_local_hit": np.int32
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},
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)
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if args.slice_remap:
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slice_mapping = pd.read_csv(args.path + ".slices.csv")
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core_mapping = pd.read_csv(args.path + ".cores.csv")
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# print("core mapping:\n", core_mapping.to_string())
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# print("slice mapping:\n", slice_mapping.to_string())
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# print("core {} is mapped to '{}'".format(4, repr(core_mapping.iloc[4])))
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min_time_miss = stats["clflush_miss_n"].min()
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max_time_miss = stats["clflush_miss_n"].max()
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def plot(filename, g=None):
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if args.no_plot:
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if g is not None:
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g.savefig(img_dir + filename)
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else:
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plt.savefig(img_dir + filename)
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# tikzplotlib.save(
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# img_dir+filename+".tex",
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# axis_width=r'0.175\textwidth',
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# axis_height=r'0.25\textwidth'
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# )
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print(filename, "saved")
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plt.close()
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plt.show()
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def remap_core(key):
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column = core_mapping.columns.get_loc(key)
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def remap(core):
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return core_mapping.iat[core, column]
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return remap
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columns = [
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("main_socket", "main_core", "socket"),
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("main_core_fixed", "main_core", "core"),
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("main_ht", "main_core", "hthread"),
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("helper_socket", "helper_core", "socket"),
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("helper_core_fixed", "helper_core", "core"),
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("helper_ht", "helper_core", "hthread"),
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]
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for (col, icol, key) in columns:
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stats[col] = stats[icol].apply(remap_core(key))
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#! Remove points where helper_core == main_core but main_ht != helper_ht
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stats = stats[
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(stats["main_ht"] == stats["helper_ht"])
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| (stats["main_core_fixed"] != stats["helper_core_fixed"])
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]
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# slice_mapping = {3: 0, 1: 1, 2: 2, 0: 3}
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if args.slice_remap:
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stats["slice_group"] = stats["hash"].apply(
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lambda h: slice_mapping["slice_group"].iloc[h]
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)
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else:
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stats["slice_group"] = stats["hash"]
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graph_lower_miss = int((min_time_miss // 10) * 10)
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graph_upper_miss = int(((max_time_miss + 9) // 10) * 10)
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# print("Graphing from {} to {}".format(graph_lower_miss, graph_upper_miss))
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# also explains remote
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# shared needs some thinking as there is something weird happening there.
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#
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# M 0 1 2 3 4 5 6 7
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#
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# print(stats.head())
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num_core = len(stats["main_core_fixed"].unique())/2
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# print("Found {}".format(num_core))
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def ring_distance(x0, x1):
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"""
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return (a, b) where `a` is the core distance and `b` the larger "ring step"
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"""
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dist = abs(x0-x1)
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if x0 // (num_core/2) != x1 // (num_core/2):
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return min(num_core-1-dist, dist-1), 1
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else:
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return dist, 0
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def slice_msg_distance(x1, x0):
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"""
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Si l'expéditeur est à l'extrémité d'une des lignes, il envoie toujours dans le même sens
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(vers toute sa ligne d'abord), sinon, il prend le chemin le plus court
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"""
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dist = abs(x0-x1)
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if x0 == 3:
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dist = (x0-x1+8)%8
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elif x0 == 4:
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dist = (x1-x0+8)%8
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if x0 in [0, 3, 4, 7]:
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if dist > 3:
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return dist, 1
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return dist, 0
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return ring_distance(x0, x1)
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def miss_topology(main_core, slice_group, C, h, H):
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core, ring = slice_msg_distance(main_core, slice_group)
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return C + h * core + H*ring
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def miss_topology_df(x, C, h, H):
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func = lambda x, C, h, H: miss_topology(x["main_core_fixed"], x["slice_group"], C, h, H)
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return x.apply(func, args=(C, h, H), axis=1)
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def remote_hit_topology(main_core, helper_core, slice_group, C, h, H):
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core0, ring0 = slice_msg_distance(main_core, slice_group)
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core1, ring1 = slice_msg_distance(helper_core, slice_group)
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return C + h*(core0+core1) + H*(ring0+ring1)
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def remote_hit_topology_df(x, C, h, H):
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func = lambda x, C, h, H: remote_hit_topology(x["main_core_fixed"], x["helper_core_fixed"], x["slice_group"], C, h, H)
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return x.apply(func, args=(C, h, H), axis=1)
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def do_predictions(df):
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def plot_predicted_topo(col, row, x_ax, target, pred):
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title_letter = {
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"main_core_fixed": "A",
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"helper_core_fixed": "V",
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"slice_group": "S"
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}.get(col, col[0])
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figure_A0 = sns.FacetGrid(df, col=col, row=row)
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figure_A0.map(sns.scatterplot, x_ax, pred, color="r")
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figure_A0.map(sns.scatterplot, x_ax, target, color="g", marker="+")
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figure_A0.set_titles(col_template="$"+title_letter+"$ = {col_name}")
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plot(f"medians_{pred}_{col}.png")
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df = df[(df["main_socket"] == 0) & (df["helper_socket"] == 0)]
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res_miss = optimize.curve_fit(
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miss_topology_df, df[["main_core_fixed", "slice_group"]], df["clflush_miss_n"]
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)
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print("Miss topology:")
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print(res_miss)
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res_remote_hit = optimize.curve_fit(
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remote_hit_topology_df, df[["main_core_fixed", "helper_core_fixed", "slice_group"]], df["clflush_remote_hit"]
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)
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print("Remote hit topology:")
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print(res_remote_hit)
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df["predicted_miss"] = miss_topology_df(df, *(res_miss[0]))
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plot_predicted_topo("slice_group", None, "main_core_fixed", "clflush_miss_n", "predicted_miss")
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plot_predicted_topo("main_core_fixed", None, "slice_group", "clflush_miss_n", "predicted_miss")
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df["predicted_remote_hit"] = remote_hit_topology_df(df, *(res_remote_hit[0]))
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plot_predicted_topo("slice_group", "helper_core_fixed", "main_core_fixed", "clflush_remote_hit", "predicted_remote_hit")
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plot_predicted_topo("main_core_fixed", "helper_core_fixed", "slice_group", "clflush_remote_hit", "predicted_remote_hit")
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def rslice():
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for core in stats["main_core_fixed"].unique():
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os.makedirs(img_dir + f"slices{core}", exist_ok=True)
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for slice_ in stats["slice_group"].unique():
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df = stats[
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(stats["slice_group"] == slice_) & (stats["main_core_fixed"] == core)
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]
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fig = sns.scatterplot(
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df, x="helper_core_fixed", y="clflush_remote_hit", color="r"
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)
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fig.set(title=f"main_core={core} slice={slice_}")
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plt.savefig(img_dir + f"slices{core}/" + str(slice_) + ".png")
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plt.close()
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def facet_grid(
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df, row, col, third,
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draw_fn=sns.scatterplot,
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shown=[
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"clflush_shared_hit",
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"clflush_remote_hit",
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"clflush_local_hit_n",
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"clflush_miss_n",
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],
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colors=["y", "r", "g", "b"],
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separate_hthreads=False,
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title=None,
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):
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"""
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Creates a facet grid showing all points
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"""
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if separate_hthreads:
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colors=["y", "r", "g", "b"]
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for el in shown:
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for helper, main in itertools.product((0, 1), (0, 1)):
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df[el+f"_m{main}h{helper}"] = df[(df["main_ht"] == main) & (df["helper_ht"] == helper)][el]
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grid = sns.FacetGrid(df, row=row, col=col)
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for i, el in enumerate(shown):
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if separate_hthreads:
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for helper, main in itertools.product((0, 1), (0, 1)):
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kwargs = {"marker": ['x', '+'][helper]} if draw_fn == sns.scatterplot else {}
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grid.map(
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draw_fn,
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third,
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el+f"_m{main}h{helper}",
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color=colors[(helper+2*main) % len(colors)],
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**kwargs
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)
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else:
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grid.map(draw_fn, third, el, color=colors[i % len(colors)])
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if title is not None:
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plot(title, g=grid)
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return grid
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def all_facets(df, pre="", post="", *args, **kwargs):
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"""
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df : panda dataframe
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pre, post: strings to add before and after the filename
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"""
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facet_grid(
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df, "helper_core_fixed", "main_core_fixed", "slice_group",
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title=f"{pre}facet_slice{post}.png", *args, **kwargs
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)
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facet_grid(
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df, "helper_core_fixed", "slice_group", "main_core_fixed",
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title=f"{pre}facet_main{post}.png", *args, **kwargs
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)
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facet_grid(
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df, "main_core_fixed", "slice_group", "helper_core_fixed",
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title=f"{pre}facet_helper{post}.png", *args, **kwargs
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)
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def do_facet(main: int, helper: int, line: bool, metrics: str):
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"""
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- metrics: hit, miss or all
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"""
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df = stats.copy(deep=True)
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print(f"Doing all facets {main}x{helper} {metrics}")
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filtered_df = stats[
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(stats["main_socket"] == main)
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& (stats["helper_socket"] == helper)
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]
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method = "line" if line else "pt"
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shown = []
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colors = []
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if metrics == "hit" or metrics == "all":
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shown.append("clflush_remote_hit")
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colors.append("r")
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if metrics == "miss" or metrics == "all":
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shown.append("clflush_miss_n")
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colors.append("b")
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all_facets(
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filtered_df,
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pre=f"{metrics}_{method}_",
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post=f"_m{main}h{helper}",
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shown=shown,
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colors=colors,
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draw_fn=sns.lineplot if line else sns.scatterplot
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)
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if args.rslice:
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rslice()
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# do_predictions(stats)
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# all_facets(stats, shown=["clflush_remote_hit"], colors=["r"])
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with Pool(8) as pool:
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pool.starmap(
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do_facet,
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itertools.product(
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stats["main_socket"].unique(),
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stats["helper_socket"].unique(),
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(True, False),
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("hit", "miss")
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)
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)
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