.gitignore

@@ -21,5 +21,3 @@ tftpboot
 pxelinux.cfg
 serial.out
 memdisk
-**/venv
-cache_utils/results*/

cache_utils/analyse_csv.py

@@ -7,24 +7,13 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 #import tikzplotlib
+from sys import exit
 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
@@ -52,10 +41,6 @@ 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,
@@ -76,8 +61,6 @@ df = pd.read_csv(sys.argv[1] + "-results_lite.csv.bz2",
         converters={'address': convert64, 'hash': convert8},
         )
 
-print(f"Loaded columns : {list(df.keys())}")
-
 sample_columns = [
 "clflush_remote_hit",
 "clflush_shared_hit",
@@ -134,11 +117,13 @@ print(*[bin(a) for a in addresses], sep='\n')
 
 print(df.head())
 
+print(df["hash"].unique())
+
 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]
+q10s = [wq.quantile(df["time"], df[col], 0.1) for col in sample_flush_columns]
+q90s = [wq.quantile(df["time"], df[col], 0.9) for col in sample_flush_columns]
 
 graph_upper = int(((max(q90s) + 19) // 10) * 10)
 graph_lower = int(((min(q10s) - 10) // 10) * 10)
@@ -183,9 +168,6 @@ custom_hist(df_ax_vx_sx["time"], df_ax_vx_sx["clflush_miss_n"], df_ax_vx_sx["clf
 #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)
@@ -212,7 +194,7 @@ g2.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_loc
 #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")
+g#4.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])
@@ -234,7 +216,7 @@ stats.to_csv(sys.argv[1] + ".stats.csv", index=False)
 #print(stats.to_string())
 
 plt.show()
-sys.exit(0)
+exit(0)
 g = sns.FacetGrid(stats, row="Core")
 
 g.map(sns.distplot, 'Miss', bins=range(100, 480), color="r")
@@ -250,4 +232,4 @@ plt.show()
 # plt.figure()
 # sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)
 
-sys.exit(0)
+exit(0)

cache_utils/analyse_medians.py

@@ -10,12 +10,6 @@ from sys import exit
 import numpy as np
 from scipy import optimize
 import sys
-import os
-
-import warnings
-
-warnings.filterwarnings('ignore')
-print("warnings are filtered, enable them back if you are having some trouble")
 
 # TODO
 # sys.argv[1] should be the root
@@ -29,9 +23,6 @@ print("warnings are filtered, enable them back if you are having some trouble")
 # each row is an origin core
 # each column a helper core if applicable
 
-assert os.path.exists(sys.argv[1] + ".stats.csv")
-assert os.path.exists(sys.argv[1] + ".slices.csv")
-assert os.path.exists(sys.argv[1] + ".cores.csv")
 
 stats = pd.read_csv(sys.argv[1] + ".stats.csv",
                     dtype={
@@ -243,9 +234,9 @@ figure_median_I.map(sns.lineplot, 'slice_group', 'predicted_miss', color="b")
 figure_median_I.set_titles(col_template="$A$ = {col_name}")
 figure_median_I.tight_layout()
 
-# import tikzplotlib
+import tikzplotlib
 
-# tikzplotlib.save("fig-median-I.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
+tikzplotlib.save("fig-median-I.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
 plt.show()
 
 #stats["predicted_remote_hit_no_gpu"] = exclusive_hit_topology_nogpu_df(stats, *(res_no_gpu[0]))
@@ -259,7 +250,7 @@ figure_median_E_A0.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit
 figure_median_E_A0.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
 figure_median_E_A0.set_titles(col_template="$S$ = {col_name}")
 
-# tikzplotlib.save("fig-median-E-A0.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
+tikzplotlib.save("fig-median-E-A0.tex", axis_width=r'0.175\textwidth', axis_height=r'0.25\textwidth')
 plt.show()
 
 g = sns.FacetGrid(stats, row="main_core_fixed")

cache_utils/src/bin/two_thread_cal.rs

@@ -111,10 +111,9 @@ fn main() {
 
     let core_per_socket_str = from_utf8(&core_per_socket_out.stdout).unwrap();
 
-    //println!("Number of cores per socket (str): {}", core_per_socket_str);
+    //println!("Number of cores per socket: {}", cps_str);
 
     let core_per_socket: u8 = core_per_socket_str[0..(core_per_socket_str.len() - 1)]
-        .trim()
         .parse()
         .unwrap_or(0);
 
@@ -157,7 +156,7 @@ fn main() {
             display_name: "clflush remote hit",
             t: &(),
         },
-        CalibrateOperation2T {
+        /*        CalibrateOperation2T {
             prepare: maccess::<u8>,
             op: load_and_flush_wrap,
             name: "clflush_shared_hit",
@@ -177,7 +176,7 @@ fn main() {
             name: "clflush_local_hit_f",
             display_name: "clflush local hit - f",
             t: &(),
-        },
+        },*/
         CalibrateOperation2T {
             prepare: noop::<u8>,
             op: only_flush_wrap,
@@ -185,7 +184,7 @@ fn main() {
             display_name: "clflush miss - n",
             t: &(),
         },
-        CalibrateOperation2T {
+        /*        CalibrateOperation2T {
             prepare: noop::<u8>,
             op: load_and_flush_wrap,
             name: "clflush_local_hit_n",
@@ -219,7 +218,7 @@ fn main() {
             name: "reload_local_hit",
             display_name: "reload local hit",
             t: &(),
-        },
+        },*/
     ];
 
     let r = unsafe {

requirements.txt

@@ -4,6 +4,5 @@ wquantiles==0.5
 
 matplotlib~=3.2.1
 numpy~=1.18.2
-#gmpy2~=2.0.8
+gmpy2~=2.0.8
 scipy~=1.4.1
-PyQt5