Remove double median method
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0714489afc
commit
051db5fbeb
@ -3,24 +3,26 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-License-Identifier: MIT
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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#import tikzplotlib
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import wquantiles as wq
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import numpy as np
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import argparse
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import warnings
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import time
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import json
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import sys
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import os
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import json
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import warnings
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import matplotlib.style as mplstyle
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import matplotlib.pyplot as plt
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import wquantiles as wq
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import seaborn as sns
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import pandas as pd
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import numpy as np
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#import tikzplotlib
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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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sns.set_theme()
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t = time.time()
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def print_timed(*args, **kwargs):
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print(f"[{round(time.time()-t, 1):>8}]", *args, **kwargs)
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def dict_to_json(d):
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if isinstance(d, dict):
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@ -86,6 +88,9 @@ parser.add_argument(
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args = parser.parse_args()
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warnings.filterwarnings('ignore')
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print_timed("warnings are filtered, enable them back if you are having some trouble")
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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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@ -114,7 +119,7 @@ df = pd.read_csv(args.path + "-results_lite.csv.bz2",
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converters={'address': convert64, 'hash': convert8},
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)
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print(f"Loaded columns : {list(df.keys())}")
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print_timed(f"Loaded columns : {list(df.keys())}")
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sample_columns = [
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"clflush_remote_hit",
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@ -143,24 +148,30 @@ if args.slice_remap:
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core_mapping = pd.read_csv(args.path + ".cores.csv")
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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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remapped = core_mapping.iloc[core]
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return remapped[key]
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return core_mapping.iat[core, column]
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return remap
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df["main_socket"] = df["main_core"].apply(remap_core("socket"))
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df["main_core_fixed"] = df["main_core"].apply(remap_core("core"))
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df["main_ht"] = df["main_core"].apply(remap_core("hthread"))
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df["helper_socket"] = df["helper_core"].apply(remap_core("socket"))
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df["helper_core_fixed"] = df["helper_core"].apply(remap_core("core"))
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df["helper_ht"] = df["helper_core"].apply(remap_core("hthread"))
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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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df[col] = df[icol].apply(remap_core(key))
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print_timed(f"Column {col} added")
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if args.slice_remap:
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slice_remap = lambda h: slice_mapping["slice_group"].iloc[h]
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df["slice_group"] = df["hash"].apply(slice_remap)
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print_timed(f"Column slice_group added")
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else:
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df["slice_group"] = df["hash"]
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@ -172,9 +183,10 @@ def get_graphing_bounds():
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return int(((min(q10s) - 10) // 10) * 10), int(((max(q90s) + 19) // 10) * 10)
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graph_lower, graph_upper = get_graphing_bounds()
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print("graphing between {}, {}".format(graph_lower, graph_upper))
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mplstyle.use("fast")
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graph_lower, graph_upper = get_graphing_bounds()
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print_timed(f"graphing between {graph_lower}, {graph_upper}")
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def plot(filename, g=None):
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if args.no_plot:
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@ -182,6 +194,7 @@ def plot(filename, g=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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print_timed(f"Saved {filename}")
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plt.close()
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plt.show()
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@ -233,31 +246,7 @@ def export_stats_csv():
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return maxi-mini
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def compute_stat(x, key):
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def compute_median(x):
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return wq.median(x["time"], x[key])
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filtered_x = x[(x[key] != 0)]
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mini, maxi = filtered_x["time"].min(), filtered_x["time"].max()
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miss_spread = get_spread(x, "clflush_miss_n")
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if maxi-mini < 3*miss_spread:
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med = compute_median(x)
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return [med, med]
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if key == "clflush_remote_hit":
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"""print(
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"double for core {}:{}@{}, helper {}:{}@{}".format(
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x["main_core_fixed"].unique()[0],
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x["main_ht"].unique()[0],
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x["main_socket"].unique()[0],
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x["helper_core_fixed"].unique()[0],
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x["helper_ht"].unique()[0],
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x["helper_socket"].unique()[0],
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)
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)"""
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center = mini + (maxi-mini)/2
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return [compute_median(filtered_x[(filtered_x["time"] < center)]), compute_median(filtered_x[(filtered_x["time"] >= center)])]
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return wq.median(x["time"], x[key])
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df_grouped = df.groupby(["main_core", "helper_core", "hash"])
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@ -276,8 +265,6 @@ def export_stats_csv():
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"clflush_shared_hit": hit_shared.values
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})
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stats = stats.explode(['clflush_miss_n', 'clflush_remote_hit', 'clflush_local_hit_n', 'clflush_shared_hit'])
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stats.to_csv(args.path + ".stats.csv", index=False)
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@ -308,4 +295,4 @@ if not args.stats:
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if not os.path.exists(args.path + ".stats.csv") or args.stats:
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export_stats_csv()
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else:
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print("Skipping .stats.csv export")
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print_timed("Skipping .stats.csv export")
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