dendrobates-t-azureus/cache_utils/analyse_csv.py

# SPDX-FileCopyrightText: 2021 Guillaume DIDIER
#
# 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

from functools import partial

import sys
import os

import json
import warnings

warnings.filterwarnings('ignore')
print("warnings are filtered, enable them back if you are having some trouble")

def dict_to_json(d):
    if isinstance(d, dict):
        return json.dumps(d)
    return d

# For cyber cobay sanity check :
# from gmpy2 import popcount
def popcount(x):
    return x.bit_count()

functions_i9_9900 = [
             0b1111111111010101110101010001000000,
             0b0110111110111010110001001000000000,
             0b1111111000011111110010110000000000]


def complex_hash(addr):
    r = 0
    for f in reversed(functions_i9_9900):
        r <<= 1
        r |= (popcount(f & addr) & 1)
    return r


def convert64(x):
    return np.int64(int(x, base=16))

def convert8(x):
    return np.array(int(x, base=16)).astype(np.int64)
    # return np.int8(int(x, base=16))

assert os.path.exists(sys.argv[1] + ".slices.csv")
assert os.path.exists(sys.argv[1] + ".cores.csv")
assert os.path.exists(sys.argv[1] + "-results_lite.csv.bz2")

df = pd.read_csv(sys.argv[1] + "-results_lite.csv.bz2",
        dtype={
            "main_core": np.int8,
            "helper_core": np.int8,
            # "address": int,
            # "hash": np.int8,
            "time": np.int16,
            "clflush_remote_hit": np.int32,
            "clflush_shared_hit": np.int32,
            "clflush_miss_f": np.int32,
            "clflush_local_hit_f": np.int32,
            "clflush_miss_n": np.int32,
            "clflush_local_hit_n": np.int32,
            "reload_miss": np.int32,
            "reload_remote_hit": np.int32,
            "reload_shared_hit": np.int32,
            "reload_local_hit": np.int32},
        converters={'address': convert64, 'hash': convert8},
        )

print(f"Loaded columns : {list(df.keys())}")

sample_columns = [
"clflush_remote_hit",
"clflush_shared_hit",
"clflush_miss_f",
"clflush_local_hit_f",
"clflush_miss_n",
"clflush_local_hit_n",
"reload_miss",
"reload_remote_hit",
"reload_shared_hit",
"reload_local_hit",
]

sample_flush_columns = [
    "clflush_remote_hit",
    "clflush_shared_hit",
    "clflush_miss_f",
    "clflush_local_hit_f",
    "clflush_miss_n",
    "clflush_local_hit_n",
]


slice_mapping = pd.read_csv(sys.argv[1] + ".slices.csv")
core_mapping = pd.read_csv(sys.argv[1] + ".cores.csv")

def remap_core(key):
    def remap(core):
        remapped = core_mapping.iloc[core]
        return remapped[key]

    return remap


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"))

# slice_mapping = {3: 0, 1: 1, 2: 2, 0: 3}

slice_remap = lambda h: slice_mapping["slice_group"].iloc[h]
df["slice_group"] = df["hash"].apply(slice_remap)


print(df.columns)
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)

addresses = df["address"].unique()
print(addresses)
print(*[bin(a) for a in addresses], sep='\n')

print(df.head())

min_time = df["time"].min()
max_time = df["time"].max()

q10s = [wq.quantile(df["time"], df[col], 0.1) for col in sample_flush_columns if col in df]
q90s = [wq.quantile(df["time"], df[col], 0.9) for col in sample_flush_columns if col in df]

graph_upper = int(((max(q90s) + 19) // 10) * 10)
graph_lower = int(((min(q10s) - 10) // 10) * 10)
# graph_lower = (min_time // 10) * 10
# graph_upper = ((max_time + 9) // 10) * 10

print("graphing between {}, {}".format(graph_lower, graph_upper))

df_main_core_0 = df[df["main_core"] == 0]
#df_helper_core_0 = df[df["helper_core"] == 0]

colours = ["b", "r", "g", "y"]

def custom_hist(x, *y, **kwargs):
    for (i, yi) in enumerate(y):
        kwargs["color"] = colours[i]
        sns.distplot(x, range(graph_lower, graph_upper), hist_kws={"weights": yi, "histtype":"step"}, kde=False, **kwargs)


custom_hist(df["time"], df["clflush_miss_n"], df["clflush_remote_hit"])

#tikzplotlib.save("fig-hist-all.tex")#, axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
plt.show()

attacker = 2
victim = 7
slice = 14

df_ax_vx_sx = df[(df["hash"] == slice) & (df["main_core"] == attacker) & (df["helper_core"] == victim)]

custom_hist(df_ax_vx_sx["time"], df_ax_vx_sx["clflush_miss_n"], df_ax_vx_sx["clflush_remote_hit"])
#tikzplotlib.save("fig-hist-good-A{}V{}S{}.tex".format(attacker,victim,slice))#, axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
plt.show()

attacker = 9
victim = 4
slice = 8

df_ax_vx_sx = df[(df["hash"] == slice) & (df["main_core"] == attacker) & (df["helper_core"] == victim)]

custom_hist(df_ax_vx_sx["time"], df_ax_vx_sx["clflush_miss_n"], df_ax_vx_sx["clflush_remote_hit"])
#tikzplotlib.save("fig-hist-bad-A{}V{}S{}.tex".format(attacker,victim,slice))#, axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
plt.show()

# Fix np.darray is unhashable
df_main_core_0.loc[:, ('hash',)] = df_main_core_0['hash'].apply(dict_to_json)
df.loc[:, ('hash',)] = df['hash'].apply(dict_to_json)

g = sns.FacetGrid(df_main_core_0, col="helper_core", row="hash", legend_out=True)
g2 = sns.FacetGrid(df, col="main_core", row="hash", legend_out=True)


# Color convention here :
# Blue = miss
# Red = Remote Hit
# Green = Local Hit
# Yellow = Shared Hit

g.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

g2.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)

#plt.show()
#plt.figure()

#df_mcf6 = df[df["main_core_fixed"] == 6]
#df_mcf6_slg7 = df_mcf6[df_mcf6["slice_group"] == 7]
#g3 = sns.FacetGrid(df_mcf6_slg7, row="helper_core_fixed", col="main_ht")
#g3.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

#g4 = sns.FacetGrid(df_mcf6_slg7, row="helper_core_fixed", col="helper_ht")
#g4.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")

def stat(x, key):
    return wq.median(x["time"], x[key])


miss = df.groupby(["main_core", "helper_core", "hash"]).apply(stat, "clflush_miss_n")
hit_remote = df.groupby(["main_core", "helper_core", "hash"]).apply(stat, "clflush_remote_hit")
hit_local = df.groupby(["main_core", "helper_core", "hash"]).apply(stat, "clflush_local_hit_n")
hit_shared = df.groupby(["main_core", "helper_core", "hash"]).apply(stat, "clflush_shared_hit")

stats = miss.reset_index()
stats.columns = ["main_core", "helper_core", "hash", "clflush_miss_n"]
stats["clflush_remote_hit"] = hit_remote.values
stats["clflush_local_hit_n"] = hit_local.values
stats["clflush_shared_hit"] = hit_shared.values

stats.to_csv(sys.argv[1] + ".stats.csv", index=False)

#print(stats.to_string())

plt.show()
sys.exit(0)
g = sns.FacetGrid(stats, row="Core")

g.map(sns.distplot, 'Miss', bins=range(100, 480), color="r")
g.map(sns.distplot, 'Hit', bins=range(100, 480))
plt.show()

#stats["clflush_miss_med"] = stats[[0]].apply(lambda x: x["miss_med"])
#stats["clflush_hit_med"] = stats[[0]].apply(lambda x: x["hit_med"])
#del df[[0]]
#print(hit.to_string(), miss.to_string())

# test = pd.DataFrame({"value" : [0, 5], "weight": [5, 1]})
# plt.figure()
# sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)

sys.exit(0)