Compare commits
No commits in common. "acc4fb6c9abf7acecdb539737c145e72cf227fb7" and "0714489afcccc8b62669a88aa8eba99241abf521" have entirely different histories.
acc4fb6c9a
...
0714489afc
@ -3,26 +3,24 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
#import tikzplotlib
|
||||
import wquantiles as wq
|
||||
import numpy as np
|
||||
import argparse
|
||||
import warnings
|
||||
import time
|
||||
import json
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
import matplotlib.style as mplstyle
|
||||
import matplotlib.pyplot as plt
|
||||
import wquantiles as wq
|
||||
import seaborn as sns
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
#import tikzplotlib
|
||||
import json
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings('ignore')
|
||||
print("warnings are filtered, enable them back if you are having some trouble")
|
||||
|
||||
t = time.time()
|
||||
def print_timed(*args, **kwargs):
|
||||
print(f"[{round(time.time()-t, 1):>8}]", *args, **kwargs)
|
||||
|
||||
sns.set_theme()
|
||||
|
||||
def dict_to_json(d):
|
||||
if isinstance(d, dict):
|
||||
@ -88,9 +86,6 @@ parser.add_argument(
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
warnings.filterwarnings('ignore')
|
||||
print_timed("warnings are filtered, enable them back if you are having some trouble")
|
||||
|
||||
img_dir = os.path.dirname(args.path)+"/figs/"
|
||||
os.makedirs(img_dir, exist_ok=True)
|
||||
|
||||
@ -119,7 +114,7 @@ df = pd.read_csv(args.path + "-results_lite.csv.bz2",
|
||||
converters={'address': convert64, 'hash': convert8},
|
||||
)
|
||||
|
||||
print_timed(f"Loaded columns : {list(df.keys())}")
|
||||
print(f"Loaded columns : {list(df.keys())}")
|
||||
|
||||
sample_columns = [
|
||||
"clflush_remote_hit",
|
||||
@ -148,30 +143,24 @@ if args.slice_remap:
|
||||
core_mapping = pd.read_csv(args.path + ".cores.csv")
|
||||
|
||||
def remap_core(key):
|
||||
column = core_mapping.columns.get_loc(key)
|
||||
def remap(core):
|
||||
return core_mapping.iat[core, column]
|
||||
remapped = core_mapping.iloc[core]
|
||||
return remapped[key]
|
||||
|
||||
return remap
|
||||
|
||||
|
||||
columns = [
|
||||
("main_socket", "main_core", "socket")
|
||||
("main_core_fixed", "main_core", "core")
|
||||
("main_ht", "main_core", "hthread")
|
||||
("helper_socket", "helper_core", "socket")
|
||||
("helper_core_fixed", "helper_core", "core")
|
||||
("helper_ht", "helper_core", "hthread")
|
||||
]
|
||||
for (col, icol, key) in columns:
|
||||
df[col] = df[icol].apply(remap_core(key))
|
||||
print_timed(f"Column {col} added")
|
||||
df["main_socket"] = df["main_core"].apply(remap_core("socket"))
|
||||
df["main_core_fixed"] = df["main_core"].apply(remap_core("core"))
|
||||
df["main_ht"] = df["main_core"].apply(remap_core("hthread"))
|
||||
df["helper_socket"] = df["helper_core"].apply(remap_core("socket"))
|
||||
df["helper_core_fixed"] = df["helper_core"].apply(remap_core("core"))
|
||||
df["helper_ht"] = df["helper_core"].apply(remap_core("hthread"))
|
||||
|
||||
|
||||
if args.slice_remap:
|
||||
slice_remap = lambda h: slice_mapping["slice_group"].iloc[h]
|
||||
df["slice_group"] = df["hash"].apply(slice_remap)
|
||||
print_timed(f"Column slice_group added")
|
||||
else:
|
||||
df["slice_group"] = df["hash"]
|
||||
|
||||
@ -183,10 +172,9 @@ def get_graphing_bounds():
|
||||
return int(((min(q10s) - 10) // 10) * 10), int(((max(q90s) + 19) // 10) * 10)
|
||||
|
||||
|
||||
mplstyle.use("fast")
|
||||
|
||||
graph_lower, graph_upper = get_graphing_bounds()
|
||||
print_timed(f"graphing between {graph_lower}, {graph_upper}")
|
||||
print("graphing between {}, {}".format(graph_lower, graph_upper))
|
||||
|
||||
|
||||
def plot(filename, g=None):
|
||||
if args.no_plot:
|
||||
@ -194,7 +182,6 @@ def plot(filename, g=None):
|
||||
g.savefig(img_dir+filename)
|
||||
else:
|
||||
plt.savefig(img_dir+filename)
|
||||
print_timed(f"Saved {filename}")
|
||||
plt.close()
|
||||
plt.show()
|
||||
|
||||
@ -246,7 +233,31 @@ def export_stats_csv():
|
||||
return maxi-mini
|
||||
|
||||
def compute_stat(x, key):
|
||||
return wq.median(x["time"], x[key])
|
||||
def compute_median(x):
|
||||
return wq.median(x["time"], x[key])
|
||||
|
||||
filtered_x = x[(x[key] != 0)]
|
||||
mini, maxi = filtered_x["time"].min(), filtered_x["time"].max()
|
||||
|
||||
miss_spread = get_spread(x, "clflush_miss_n")
|
||||
|
||||
if maxi-mini < 3*miss_spread:
|
||||
med = compute_median(x)
|
||||
return [med, med]
|
||||
|
||||
if key == "clflush_remote_hit":
|
||||
"""print(
|
||||
"double for core {}:{}@{}, helper {}:{}@{}".format(
|
||||
x["main_core_fixed"].unique()[0],
|
||||
x["main_ht"].unique()[0],
|
||||
x["main_socket"].unique()[0],
|
||||
x["helper_core_fixed"].unique()[0],
|
||||
x["helper_ht"].unique()[0],
|
||||
x["helper_socket"].unique()[0],
|
||||
)
|
||||
)"""
|
||||
center = mini + (maxi-mini)/2
|
||||
return [compute_median(filtered_x[(filtered_x["time"] < center)]), compute_median(filtered_x[(filtered_x["time"] >= center)])]
|
||||
|
||||
df_grouped = df.groupby(["main_core", "helper_core", "hash"])
|
||||
|
||||
@ -265,6 +276,8 @@ def export_stats_csv():
|
||||
"clflush_shared_hit": hit_shared.values
|
||||
})
|
||||
|
||||
stats = stats.explode(['clflush_miss_n', 'clflush_remote_hit', 'clflush_local_hit_n', 'clflush_shared_hit'])
|
||||
|
||||
stats.to_csv(args.path + ".stats.csv", index=False)
|
||||
|
||||
|
||||
@ -295,4 +308,4 @@ if not args.stats:
|
||||
if not os.path.exists(args.path + ".stats.csv") or args.stats:
|
||||
export_stats_csv()
|
||||
else:
|
||||
print_timed("Skipping .stats.csv export")
|
||||
print("Skipping .stats.csv export")
|
||||
|
||||
@ -15,9 +15,7 @@ import pandas as pd
|
||||
import seaborn as sns
|
||||
from scipy import optimize
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.style as mplstyle
|
||||
|
||||
mplstyle.use("fast")
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
print("warnings are filtered, enable them back if you are having some trouble")
|
||||
@ -56,23 +54,14 @@ parser.add_argument(
|
||||
help="Create slice{} directories with segmented grid",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--no-slice-remap",
|
||||
dest="slice_remap",
|
||||
action="store_false",
|
||||
default=True,
|
||||
help="Don't remap the slices"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
img_dir = os.path.dirname(args.path) + "/figs/"
|
||||
os.makedirs(img_dir, exist_ok=True)
|
||||
|
||||
assert os.path.exists(args.path + ".stats.csv")
|
||||
assert os.path.exists(args.path + ".slices.csv")
|
||||
assert os.path.exists(args.path + ".cores.csv")
|
||||
if args.slice_remap:
|
||||
assert os.path.exists(args.path + ".slices.csv")
|
||||
|
||||
stats = pd.read_csv(
|
||||
args.path + ".stats.csv",
|
||||
@ -95,8 +84,7 @@ stats = pd.read_csv(
|
||||
},
|
||||
)
|
||||
|
||||
if args.slice_remap:
|
||||
slice_mapping = pd.read_csv(args.path + ".slices.csv")
|
||||
slice_mapping = pd.read_csv(args.path + ".slices.csv")
|
||||
core_mapping = pd.read_csv(args.path + ".cores.csv")
|
||||
|
||||
# print("core mapping:\n", core_mapping.to_string())
|
||||
@ -141,12 +129,9 @@ stats["helper_ht"] = stats["helper_core"].apply(remap_core("hthread"))
|
||||
|
||||
# slice_mapping = {3: 0, 1: 1, 2: 2, 0: 3}
|
||||
|
||||
if args.slice_remap:
|
||||
stats["slice_group"] = stats["hash"].apply(
|
||||
lambda h: slice_mapping["slice_group"].iloc[h]
|
||||
)
|
||||
else:
|
||||
stats["slice_group"] = stats["hash"]
|
||||
stats["slice_group"] = stats["hash"].apply(
|
||||
lambda h: slice_mapping["slice_group"].iloc[h]
|
||||
)
|
||||
|
||||
graph_lower_miss = int((min_time_miss // 10) * 10)
|
||||
graph_upper_miss = int(((max_time_miss + 9) // 10) * 10)
|
||||
@ -401,26 +386,15 @@ def facet_grid(
|
||||
"clflush_miss_n",
|
||||
],
|
||||
colors=["y", "r", "g", "b"],
|
||||
separate_hthreads=False,
|
||||
title=None,
|
||||
):
|
||||
"""
|
||||
Creates a facet grid showing all points
|
||||
"""
|
||||
if separate_hthreads:
|
||||
colors=["y", "r", "g", "b"]
|
||||
for el in shown:
|
||||
for helper, main in itertools.product((0, 1), (0, 1)):
|
||||
df[el+f"_m{main}h{helper}"] = df[(df["main_ht"] == main) & (df["helper_ht"] == helper)][el]
|
||||
|
||||
grid = sns.FacetGrid(df, row=row, col=col)
|
||||
|
||||
for i, el in enumerate(shown):
|
||||
if separate_hthreads:
|
||||
for helper, main in itertools.product((0, 1), (0, 1)):
|
||||
grid.map(draw_fn, third, el+f"_m{main}h{helper}", color=colors[(helper+2*main) % len(colors)])# marker=['+', 'x'][helper])
|
||||
else:
|
||||
grid.map(draw_fn, third, el, color=colors[i % len(colors)])
|
||||
grid.map(draw_fn, third, el, color=colors[i % len(colors)])
|
||||
|
||||
if title is not None:
|
||||
plot(title, g=grid)
|
||||
@ -434,7 +408,7 @@ def all_facets(df, pre="", post="", *args, **kwargs):
|
||||
"""
|
||||
|
||||
facet_grid(
|
||||
df, "helper_core_fixed", "main_core_fixed", "slice_group",
|
||||
df, "main_core_fixed", "helper_core_fixed", "slice_group",
|
||||
title=f"{pre}facet_slice{post}.png", *args, **kwargs
|
||||
)
|
||||
facet_grid(
|
||||
@ -442,58 +416,47 @@ def all_facets(df, pre="", post="", *args, **kwargs):
|
||||
title=f"{pre}facet_main{post}.png", *args, **kwargs
|
||||
)
|
||||
facet_grid(
|
||||
df, "main_core_fixed", "slice_group", "helper_core_fixed",
|
||||
df, "slice_group", "main_core_fixed", "helper_core_fixed",
|
||||
title=f"{pre}facet_helper{post}.png", *args, **kwargs
|
||||
)
|
||||
|
||||
|
||||
def do_facet(main: int, helper: int, line: bool, metrics: str):
|
||||
"""
|
||||
- metrics: hit, miss or all
|
||||
"""
|
||||
def do_facet(main: int, helper: int, line: bool):
|
||||
df = stats.copy(deep=True)
|
||||
|
||||
print(f"Doing all facets {main}x{helper} {metrics}")
|
||||
print(f"Doing all facets {main}x{helper}")
|
||||
filtered_df = stats[
|
||||
(stats["main_socket"] == main)
|
||||
& (stats["helper_socket"] == helper)
|
||||
(stats["main_core_fixed"] // (num_core / 2) == main)
|
||||
& (stats["helper_core_fixed"] // (num_core / 2) == helper)
|
||||
]
|
||||
method = "line" if line else "pt"
|
||||
shown = []
|
||||
colors = []
|
||||
if metrics == "hit" or metrics == "all":
|
||||
shown.append("clflush_remote_hit")
|
||||
colors.append("r")
|
||||
if metrics == "miss" or metrics == "all":
|
||||
shown.append("clflush_miss_n")
|
||||
colors.append("b")
|
||||
|
||||
all_facets(
|
||||
filtered_df,
|
||||
pre=f"{metrics}_{method}_",
|
||||
pre=f"hit_{method}_",
|
||||
post=f"_m{main}h{helper}",
|
||||
shown=shown,
|
||||
colors=colors,
|
||||
shown=["clflush_remote_hit"],
|
||||
colors=["r"],
|
||||
draw_fn=sns.lineplot if line else sns.scatterplot
|
||||
)
|
||||
all_facets(
|
||||
filtered_df,
|
||||
pre=f"miss_{method}_",
|
||||
post=f"_m{main}h{helper}",
|
||||
shown=["clflush_miss_n"],
|
||||
colors=["b"],
|
||||
draw_fn=sns.lineplot if line else sns.scatterplot
|
||||
)
|
||||
|
||||
|
||||
|
||||
if args.rslice:
|
||||
rslice()
|
||||
|
||||
# do_predictions(stats)
|
||||
# all_facets(stats, shown=["clflush_remote_hit"], colors=["r"])
|
||||
# all_facets(stats, "")
|
||||
|
||||
|
||||
|
||||
with Pool(8) as pool:
|
||||
pool.starmap(
|
||||
do_facet,
|
||||
itertools.product(
|
||||
stats["main_socket"].unique(),
|
||||
stats["helper_socket"].unique(),
|
||||
(True, False),
|
||||
("hit", "miss")
|
||||
)
|
||||
)
|
||||
pool.starmap(do_facet, itertools.product((0, 1), (0, 1), (True, False)))
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user