Cache utils updates
This commit is contained in:
root
parent
33df427053
commit
654b9b2cdb
15
cache_utils/2T-opt-2020-07-21-9b96280/analyse.sh
Executable file
15
cache_utils/2T-opt-2020-07-21-9b96280/analyse.sh
Executable file
@ -0,0 +1,15 @@
|
||||
#!/bin/sh
|
||||
|
||||
NAME=`basename "$1" .txt.bz2`
|
||||
echo $NAME
|
||||
|
||||
#bzcat $1 | awk '/^Iteration [:digit:]*[.]*/ ' > "${NAME}-iterations.txt"
|
||||
#rm "${NAME}-results.csv.bz2"
|
||||
#TODO forward NAME to awk script
|
||||
#awk -v logname="${NAME}" -f `dirname $0`/analyse_iterations.awk < "${NAME}-iterations.txt" | bzip2 -c > "${NAME}-results.csv.bz2" # This uses system to split off awk scripts doing the analysis
|
||||
|
||||
bzgrep "RESULT:" "$1" | cut -b 8- | bzip2 -c > "${NAME}-results.csv.bz2"
|
||||
|
||||
# remove line with no data points
|
||||
bzgrep -v -e "0,0,0,0,0,0,0,0,0,0$" "${NAME}-results.csv.bz2" | bzip2 -c > "${NAME}-results_lite.csv.bz2"
|
||||
#paste -d"," *.csv > combined.csv
|
||||
139
cache_utils/2T-opt-2020-07-21-9b96280/analyse_csv.py
Normal file
139
cache_utils/2T-opt-2020-07-21-9b96280/analyse_csv.py
Normal file
@ -0,0 +1,139 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sys import exit
|
||||
import wquantiles as wq
|
||||
import numpy as np
|
||||
|
||||
from functools import partial
|
||||
|
||||
import sys
|
||||
|
||||
def convert64(x):
|
||||
return np.int64(int(x, base=16))
|
||||
|
||||
def convert8(x):
|
||||
return np.int8(int(x, base=16))
|
||||
|
||||
df = pd.read_csv(sys.argv[1],
|
||||
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},
|
||||
)
|
||||
|
||||
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",
|
||||
]
|
||||
print(df.columns)
|
||||
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
|
||||
|
||||
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]
|
||||
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)
|
||||
# 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]
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
# 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()
|
||||
|
||||
|
||||
exit(0)
|
||||
|
||||
def stat(x, key):
|
||||
return wq.median(x["Time"], x[key])
|
||||
|
||||
|
||||
miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
|
||||
stats = miss.reset_index()
|
||||
stats.columns = ["Core", "Hash", "Miss"]
|
||||
hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
|
||||
stats["Hit"] = hit.values
|
||||
|
||||
|
||||
print(stats.to_string())
|
||||
|
||||
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)
|
||||
|
||||
exit(0)
|
||||
15
cache_utils/2T-opt-2020-07-23-15907ef/analyse.sh
Executable file
15
cache_utils/2T-opt-2020-07-23-15907ef/analyse.sh
Executable file
@ -0,0 +1,15 @@
|
||||
#!/bin/sh
|
||||
|
||||
NAME=`basename "$1" .txt.bz2`
|
||||
echo $NAME
|
||||
|
||||
#bzcat $1 | awk '/^Iteration [:digit:]*[.]*/ ' > "${NAME}-iterations.txt"
|
||||
#rm "${NAME}-results.csv.bz2"
|
||||
#TODO forward NAME to awk script
|
||||
#awk -v logname="${NAME}" -f `dirname $0`/analyse_iterations.awk < "${NAME}-iterations.txt" | bzip2 -c > "${NAME}-results.csv.bz2" # This uses system to split off awk scripts doing the analysis
|
||||
|
||||
bzgrep "RESULT:" "$1" | cut -b 8- | bzip2 -c > "${NAME}-results.csv.bz2"
|
||||
|
||||
# remove line with no data points
|
||||
bzgrep -v -e "0,0,0,0,0,0,0,0,0,0$" "${NAME}-results.csv.bz2" | bzip2 -c > "${NAME}-results_lite.csv.bz2"
|
||||
#paste -d"," *.csv > combined.csv
|
||||
139
cache_utils/2T-opt-2020-07-23-15907ef/analyse_csv.py
Normal file
139
cache_utils/2T-opt-2020-07-23-15907ef/analyse_csv.py
Normal file
@ -0,0 +1,139 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sys import exit
|
||||
import wquantiles as wq
|
||||
import numpy as np
|
||||
|
||||
from functools import partial
|
||||
|
||||
import sys
|
||||
|
||||
def convert64(x):
|
||||
return np.int64(int(x, base=16))
|
||||
|
||||
def convert8(x):
|
||||
return np.int8(int(x, base=16))
|
||||
|
||||
df = pd.read_csv(sys.argv[1],
|
||||
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},
|
||||
)
|
||||
|
||||
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",
|
||||
]
|
||||
print(df.columns)
|
||||
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
|
||||
|
||||
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]
|
||||
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)
|
||||
# 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]
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
# 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()
|
||||
|
||||
|
||||
exit(0)
|
||||
|
||||
def stat(x, key):
|
||||
return wq.median(x["Time"], x[key])
|
||||
|
||||
|
||||
miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
|
||||
stats = miss.reset_index()
|
||||
stats.columns = ["Core", "Hash", "Miss"]
|
||||
hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
|
||||
stats["Hit"] = hit.values
|
||||
|
||||
|
||||
print(stats.to_string())
|
||||
|
||||
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)
|
||||
|
||||
exit(0)
|
||||
@ -72,6 +72,31 @@ sample_flush_columns = [
|
||||
"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}
|
||||
|
||||
df["slice_group"] = df["hash"].apply(lambda h: slice_mapping["slice_group"].iloc[h])
|
||||
|
||||
|
||||
print(df.columns)
|
||||
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
|
||||
|
||||
@ -125,7 +150,13 @@ g2.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_loc
|
||||
#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])
|
||||
|
||||
@ -68,7 +68,7 @@ stats["helper_ht"] = stats["helper_core"].apply(remap_core("hthread"))
|
||||
|
||||
# slice_mapping = {3: 0, 1: 1, 2: 2, 0: 3}
|
||||
|
||||
stats["slice_group"] = stats["hash"].apply(lambda h: slice_mapping.iloc[h])
|
||||
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)
|
||||
@ -86,14 +86,28 @@ g = sns.FacetGrid(stats, row="main_core_fixed")
|
||||
g.map(sns.scatterplot, 'slice_group', 'clflush_miss_n', color="b")
|
||||
g.map(sns.scatterplot, 'slice_group', 'clflush_local_hit_n', color="g")
|
||||
|
||||
g2 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g2.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
|
||||
g0 = sns.FacetGrid(stats, row="slice_group")
|
||||
|
||||
g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_miss_n', color="b")
|
||||
g0.map(sns.scatterplot, 'main_core_fixed', 'clflush_local_hit_n', color="g") # this gives away the trick I think !
|
||||
# possibility of sending a general please discard this everyone around one of the ring + wait for ACK - direction depends on the core.
|
||||
|
||||
#
|
||||
# M 0 1 2 3 4 5 6 7
|
||||
#
|
||||
|
||||
# also explains remote
|
||||
# shared needs some thinking as there is something weird happening there.
|
||||
|
||||
|
||||
|
||||
|
||||
g3 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g3.map(sns.scatterplot, 'helper_core_fixed', 'clflush_shared_hit', color="y")
|
||||
|
||||
print(stats.head())
|
||||
|
||||
num_core = len(stats["main_core_fixed"].unique())
|
||||
print("Found {}".format(num_core))
|
||||
|
||||
|
||||
def miss_topology(x, C, h):
|
||||
main_core = x["main_core_fixed"]
|
||||
@ -102,21 +116,93 @@ def miss_topology(x, C, h):
|
||||
|
||||
|
||||
res = optimize.curve_fit(miss_topology, stats[["main_core_fixed", "slice_group"]], stats["clflush_miss_n"])
|
||||
print("Miss topology:")
|
||||
print(res)
|
||||
|
||||
|
||||
def local_hit_topology(x, C, h):
|
||||
main_core = x["main_core_fixed"]
|
||||
slice_group = x["slice_group"]
|
||||
return C + h * abs(main_core - slice_group)
|
||||
memory = -1
|
||||
gpu_if_any = num_core
|
||||
|
||||
|
||||
def exclusive_hit_topology_gpu(main_core, slice_group, helper_core, C, h1, h2):
|
||||
round_trip = gpu_if_any - memory
|
||||
|
||||
if slice_group <= num_core/2:
|
||||
# send message towards higher cores first
|
||||
if helper_core < slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
else:
|
||||
# send message toward lower cores first
|
||||
if helper_core > slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
return r
|
||||
|
||||
|
||||
def exclusive_hit_topology_gpu_df(x, C, h1, h2):
|
||||
return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_gpu(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
|
||||
|
||||
|
||||
def exclusive_hit_topology_gpu2(main_core, slice_group, helper_core, C, h1, h2):
|
||||
round_trip = gpu_if_any + 1 - memory
|
||||
|
||||
if slice_group <= num_core/2:
|
||||
# send message towards higher cores first
|
||||
if helper_core < slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
else:
|
||||
# send message toward lower cores first
|
||||
if helper_core > slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
return r
|
||||
|
||||
|
||||
def exclusive_hit_topology_gpu2_df(x, C, h1, h2):
|
||||
return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_gpu2(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
|
||||
|
||||
|
||||
# unlikely
|
||||
def exclusive_hit_topology_nogpu(main_core, slice_group, helper_core, C, h1, h2):
|
||||
round_trip = (num_core-1) - memory
|
||||
|
||||
if slice_group <= num_core/2:
|
||||
# send message towards higher cores first
|
||||
if helper_core < slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(round_trip - (helper_core - memory))
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
else:
|
||||
# send message toward lower cores first
|
||||
if helper_core > slice_group:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - memory)
|
||||
else:
|
||||
r = C + h1 * abs(main_core - slice_group) + h2 * abs(helper_core - slice_group)
|
||||
return r
|
||||
|
||||
|
||||
def exclusive_hit_topology_nogpu_df(x, C, h1, h2):
|
||||
return x.apply(lambda x, C, h1, h2: exclusive_hit_topology_nogpu(x["main_core_fixed"], x["slice_group"], x["helper_core_fixed"], C, h1, h2), args=(C, h1, h2), axis=1)
|
||||
|
||||
|
||||
#res_no_gpu = optimize.curve_fit(exclusive_hit_topology_nogpu_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
|
||||
#print("Exclusive hit topology (No GPU):")
|
||||
#print(res_no_gpu)
|
||||
|
||||
res_gpu = optimize.curve_fit(exclusive_hit_topology_gpu_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
|
||||
print("Exclusive hit topology (GPU):")
|
||||
print(res_gpu)
|
||||
|
||||
#res_gpu2 = optimize.curve_fit(exclusive_hit_topology_gpu2_df, stats[["main_core_fixed", "slice_group", "helper_core_fixed"]], stats["clflush_remote_hit"])
|
||||
#print("Exclusive hit topology (GPU2):")
|
||||
#print(res_gpu2)
|
||||
|
||||
def remote_hit_topology_1(x, C, h):
|
||||
main_core = x["main_core_fixed"]
|
||||
slice_group = x["slice_group"]
|
||||
helper_core = x["helper_core_fixed"]
|
||||
return C + h * abs(main_core - slice_group) + h * abs(slice_group - helper_core)
|
||||
|
||||
|
||||
def remote_hit_topology_2(x, C, h):
|
||||
@ -133,6 +219,33 @@ def shared_hit_topology_1(x, C, h):
|
||||
return C + h * abs(main_core - slice_group) + h * max(abs(slice_group - main_core), abs(slice_group - helper_core))
|
||||
|
||||
|
||||
def plot_func(function, *params):
|
||||
def plot_it(x, **kwargs):
|
||||
# plot_x = []
|
||||
# plot_y = []
|
||||
# for x in set(x):
|
||||
# plot_y.append(function(x, *params))
|
||||
# plot_x = x
|
||||
print(x)
|
||||
plot_y = function(x, *params)
|
||||
sns.lineplot(x, plot_y, **kwargs)
|
||||
return plot_it
|
||||
|
||||
|
||||
#stats["predicted_remote_hit_no_gpu"] = exclusive_hit_topology_nogpu_df(stats, *(res_no_gpu[0]))
|
||||
stats["predicted_remote_hit_gpu"] = exclusive_hit_topology_gpu_df(stats, *(res_gpu[0]))
|
||||
stats["predicted_remote_hit_gpu2"] = exclusive_hit_topology_gpu_df(stats, *(res_gpu2[0]))
|
||||
|
||||
g2 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g2.map(sns.scatterplot, 'helper_core_fixed', 'clflush_remote_hit', color="r")
|
||||
g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu', color="r")
|
||||
g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_gpu2', color="g")
|
||||
#g2.map(sns.lineplot, 'helper_core_fixed', 'predicted_remote_hit_no_gpu', color="g")
|
||||
#g2.map(plot_func(exclusive_hit_topology_nogpu_df, *(res_no_gpu[0])), 'helper_core_fixed', color="g")
|
||||
|
||||
g3 = sns.FacetGrid(stats, row="main_core_fixed", col="slice_group")
|
||||
g3.map(sns.scatterplot, 'helper_core_fixed', 'clflush_shared_hit', color="y")
|
||||
|
||||
# more ideas needed
|
||||
|
||||
plt.show()
|
||||
|
||||
15
cache_utils/2T/analyse.sh
Executable file
15
cache_utils/2T/analyse.sh
Executable file
@ -0,0 +1,15 @@
|
||||
#!/bin/sh
|
||||
|
||||
NAME=`basename "$1" .txt.bz2`
|
||||
echo $NAME
|
||||
|
||||
#bzcat $1 | awk '/^Iteration [:digit:]*[.]*/ ' > "${NAME}-iterations.txt"
|
||||
#rm "${NAME}-results.csv.bz2"
|
||||
#TODO forward NAME to awk script
|
||||
#awk -v logname="${NAME}" -f `dirname $0`/analyse_iterations.awk < "${NAME}-iterations.txt" | bzip2 -c > "${NAME}-results.csv.bz2" # This uses system to split off awk scripts doing the analysis
|
||||
|
||||
bzgrep "RESULT:" "$1" | cut -b 8- | bzip2 -c > "${NAME}-results.csv.bz2"
|
||||
|
||||
# remove line with no data points
|
||||
bzgrep -v -e "0,0,0,0,0,0,0,0,0,0$" "${NAME}-results.csv.bz2" | bzip2 -c > "${NAME}-results_lite.csv.bz2"
|
||||
#paste -d"," *.csv > combined.csv
|
||||
139
cache_utils/2T/analyse_csv.py
Normal file
139
cache_utils/2T/analyse_csv.py
Normal file
@ -0,0 +1,139 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sys import exit
|
||||
import wquantiles as wq
|
||||
import numpy as np
|
||||
|
||||
from functools import partial
|
||||
|
||||
import sys
|
||||
|
||||
def convert64(x):
|
||||
return np.int64(int(x, base=16))
|
||||
|
||||
def convert8(x):
|
||||
return np.int8(int(x, base=16))
|
||||
|
||||
df = pd.read_csv(sys.argv[1],
|
||||
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},
|
||||
)
|
||||
|
||||
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",
|
||||
]
|
||||
print(df.columns)
|
||||
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
|
||||
|
||||
print(df.head())
|
||||
|
||||
print(df["hash"].unique())
|
||||
|
||||
min_time = df["time"].min()
|
||||
max_time = df["time"].max()
|
||||
|
||||
q10s = [wq.quantile(df["time"], df[col], 0.05) for col in sample_flush_columns]
|
||||
q90s = [wq.quantile(df["time"], df[col], 0.95) for col in sample_flush_columns]
|
||||
|
||||
graph_upper = int(((max(q90s) + 29) // 10) * 10)
|
||||
graph_lower = int(((min(q10s) - 20) // 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]
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
# 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()
|
||||
|
||||
|
||||
exit(0)
|
||||
|
||||
def stat(x, key):
|
||||
return wq.median(x["Time"], x[key])
|
||||
|
||||
|
||||
miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
|
||||
stats = miss.reset_index()
|
||||
stats.columns = ["Core", "Hash", "Miss"]
|
||||
hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
|
||||
stats["Hit"] = hit.values
|
||||
|
||||
|
||||
print(stats.to_string())
|
||||
|
||||
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)
|
||||
|
||||
exit(0)
|
||||
11
cache_utils/g5k-2020-60-03-dfd59064fc/analyse.sh
Executable file
11
cache_utils/g5k-2020-60-03-dfd59064fc/analyse.sh
Executable file
@ -0,0 +1,11 @@
|
||||
#!/bin/sh
|
||||
|
||||
NAME=`basename "$1" .txt.bz2`
|
||||
echo $NAME
|
||||
|
||||
bzcat $1 | awk '/^Iteration [:digit:]*[.]*/ ' > "${NAME}-iterations.txt"
|
||||
rm "${NAME}-results.csv.bz2"
|
||||
#TODO forward NAME to awk script
|
||||
awk -v logname="${NAME}" -f `dirname $0`/analyse_iterations.awk < "${NAME}-iterations.txt" | bzip2 -c > "${NAME}-results.csv.bz2" # This uses system to split off awk scripts doing the analysis
|
||||
bzgrep -v -e "0,0$" "${NAME}-results.csv.bz2" | bzip2 -c > "${NAME}-results_lite.csv.bz2"
|
||||
#paste -d"," *.csv > combined.csv
|
||||
68
cache_utils/g5k-2020-60-03-dfd59064fc/analyse_csv.py
Normal file
68
cache_utils/g5k-2020-60-03-dfd59064fc/analyse_csv.py
Normal file
@ -0,0 +1,68 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sys import exit
|
||||
import wquantiles as wq
|
||||
import numpy as np
|
||||
|
||||
from functools import partial
|
||||
|
||||
import sys
|
||||
|
||||
df = pd.read_csv(sys.argv[1], header=1, names=["Core", "Addr", "Hash", "Time", "ClflushHit", "ClflushMiss"], dtype={"Core": int, "Time": int, "ClflushHit": int, "ClflushMiss": int},
|
||||
converters={'Addr': partial(int, base=16), 'Hash': partial(int, base=16)},
|
||||
usecols=["Core", "Addr", "Hash", "Time", "ClflushHit", "ClflushMiss"]
|
||||
)
|
||||
|
||||
print(df.columns)
|
||||
#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
|
||||
|
||||
print(df.head())
|
||||
|
||||
print(df["Hash"].unique())
|
||||
|
||||
g = sns.FacetGrid(df, col="Core", row="Hash", legend_out=True)
|
||||
|
||||
|
||||
def custom_hist(x, y1, y2, **kwargs):
|
||||
sns.distplot(x, range(100, 400), hist_kws={"weights": y1, "histtype":"step"}, kde=False, **kwargs)
|
||||
kwargs["color"] = "r"
|
||||
sns.distplot(x, range(100, 400), hist_kws={"weights": y2, "histtype":"step"}, kde=False, **kwargs)
|
||||
|
||||
g.map(custom_hist, "Time", "ClflushHit", "ClflushMiss")
|
||||
# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)
|
||||
|
||||
#plt.figure()
|
||||
|
||||
plt.show()
|
||||
exit(0)
|
||||
|
||||
def stat(x, key):
|
||||
return wq.median(x["Time"], x[key])
|
||||
|
||||
|
||||
miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
|
||||
stats = miss.reset_index()
|
||||
stats.columns = ["Core", "Hash", "Miss"]
|
||||
hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
|
||||
stats["Hit"] = hit.values
|
||||
|
||||
|
||||
print(stats.to_string())
|
||||
|
||||
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)
|
||||
|
||||
exit(0)
|
||||
17
cache_utils/g5k-2020-60-03-dfd59064fc/analyse_iterations.awk
Normal file
17
cache_utils/g5k-2020-60-03-dfd59064fc/analyse_iterations.awk
Normal file
@ -0,0 +1,17 @@
|
||||
BEGIN {
|
||||
i = 0
|
||||
print(logname)
|
||||
}
|
||||
{
|
||||
start = $0
|
||||
getline
|
||||
end = $0
|
||||
if (i == 0) {
|
||||
# generate header
|
||||
system("bzcat < "logname".txt.bz2 | awk '$0 == \""start"\",$0 == \""end"\"' | grep \"RESULT:\" | head -n 1 | cut -b 8- | awk '{print \"core,\" $0}'")
|
||||
}
|
||||
cut = "cut -b 8- | tail -n +2"
|
||||
|
||||
system("bzcat < "logname".txt.bz2 | awk '$0 == \""start"\",$0 == \""end"\"' | grep \"RESULT:\" | " cut " | awk '{print \""i",\" $0}'")
|
||||
i = i + 1
|
||||
}
|
||||
@ -176,8 +176,8 @@ fn main() {
|
||||
|
||||
let r = calibrate_fixed_freq_2_thread(
|
||||
pointer,
|
||||
64,
|
||||
array.len() as isize >> 3,
|
||||
64, // FIXME : MAGIC
|
||||
array.len() as isize >> 3, // MAGIC
|
||||
&mut core_pairs.into_iter(),
|
||||
&operations,
|
||||
CalibrationOptions {
|
||||
@ -227,8 +227,8 @@ fn main() {
|
||||
Ok(results) => {
|
||||
for r in results {
|
||||
let offset = r.offset;
|
||||
let miss_hist = r.histogram[miss_index].clone();
|
||||
let hit_hist = r.histogram[hit_index].clone();
|
||||
let miss_hist = &r.histogram[miss_index];
|
||||
let hit_hist = &r.histogram[hit_index];
|
||||
|
||||
if miss_hist.len() != hit_hist.len() {
|
||||
panic!("Maformed results");
|
||||
@ -272,10 +272,10 @@ fn main() {
|
||||
victim: result.helper_core as u8,
|
||||
},
|
||||
ResultAnalysis {
|
||||
miss: miss_hist,
|
||||
miss: miss_hist.clone(),
|
||||
miss_cum_sum,
|
||||
miss_total,
|
||||
hit: hit_hist,
|
||||
hit: hit_hist.clone(),
|
||||
hit_cum_sum,
|
||||
hit_total,
|
||||
error_miss_less_than_hit,
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
use crate::complex_addressing::{cache_slicing, CacheSlicing};
|
||||
use crate::{flush, maccess, rdtsc_fence};
|
||||
|
||||
use cpuid::{CPUVendor, MicroArchitecture};
|
||||
use cpuid::MicroArchitecture;
|
||||
|
||||
use core::arch::x86_64 as arch_x86;
|
||||
#[cfg(feature = "no_std")]
|
||||
|
||||
@ -5,8 +5,6 @@ use cpuid::{CPUVendor, MicroArchitecture};
|
||||
|
||||
extern crate alloc;
|
||||
|
||||
#[cfg(feature = "no_std")]
|
||||
use alloc::collections::VecDeque;
|
||||
#[cfg(feature = "no_std")]
|
||||
use alloc::vec::Vec;
|
||||
#[cfg(feature = "no_std")]
|
||||
@ -18,8 +16,6 @@ use hashbrown::HashSet;
|
||||
use std::collections::HashMap;
|
||||
#[cfg(feature = "use_std")]
|
||||
use std::collections::HashSet;
|
||||
#[cfg(feature = "use_std")]
|
||||
use std::collections::VecDeque;
|
||||
|
||||
#[derive(Debug, Copy, Clone)]
|
||||
pub struct SimpleAddressingParams {
|
||||
@ -72,20 +68,25 @@ pub fn cache_slicing(
|
||||
physical_cores: u8,
|
||||
vendor: CPUVendor,
|
||||
family_model_display: u32,
|
||||
stepping: u32,
|
||||
_stepping: u32,
|
||||
) -> CacheSlicing {
|
||||
let trailing_zeros = physical_cores.trailing_zeros();
|
||||
if physical_cores != (1 << trailing_zeros) {
|
||||
return Unsupported;
|
||||
}
|
||||
|
||||
match vendor {
|
||||
CPUVendor::Intel => {
|
||||
match uarch {
|
||||
MicroArchitecture::KabyLake | MicroArchitecture::Skylake => {
|
||||
ComplexAddressing(&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros + 1) as usize)])
|
||||
}
|
||||
MicroArchitecture::KabyLake | MicroArchitecture::Skylake => ComplexAddressing(
|
||||
&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros + 1) as usize)],
|
||||
),
|
||||
MicroArchitecture::CoffeeLake => {
|
||||
if family_model_display == 0x6_9E {
|
||||
ComplexAddressing(&COFFEELAKE_R_i9_FUNCTIONS[0..((trailing_zeros + 1) as usize)])
|
||||
// TODO stepping should probably be involved here
|
||||
ComplexAddressing(
|
||||
&COFFEELAKE_R_i9_FUNCTIONS[0..((trailing_zeros + 1) as usize)],
|
||||
)
|
||||
} else {
|
||||
ComplexAddressing(
|
||||
&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros + 1) as usize)],
|
||||
@ -96,14 +97,16 @@ pub fn cache_slicing(
|
||||
| MicroArchitecture::HaswellE
|
||||
| MicroArchitecture::Broadwell
|
||||
| MicroArchitecture::IvyBridge
|
||||
| MicroArchitecture::IvyBridgeE => {
|
||||
ComplexAddressing(&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros) as usize)])
|
||||
}
|
||||
| MicroArchitecture::IvyBridgeE => ComplexAddressing(
|
||||
&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros) as usize)],
|
||||
),
|
||||
MicroArchitecture::Haswell => {
|
||||
if family_model_display == 0x06_46 {
|
||||
ComplexAddressing(&CRYSTAL_WELL_FUNCTIONS[0..((trailing_zeros) as usize)])
|
||||
} else {
|
||||
ComplexAddressing(&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros) as usize)])
|
||||
ComplexAddressing(
|
||||
&SANDYBRIDGE_TO_SKYLAKE_FUNCTIONS[0..((trailing_zeros) as usize)],
|
||||
)
|
||||
}
|
||||
}
|
||||
MicroArchitecture::Nehalem | MicroArchitecture::Westmere => {
|
||||
@ -112,6 +115,10 @@ pub fn cache_slicing(
|
||||
_ => Unsupported,
|
||||
}
|
||||
}
|
||||
CPUVendor::AMD => Unsupported,
|
||||
_ => Unsupported,
|
||||
}
|
||||
}
|
||||
|
||||
fn hash(addr: usize, mask: usize) -> u8 {
|
||||
((addr & mask).count_ones() & 1) as u8
|
||||
@ -126,7 +133,7 @@ impl CacheSlicing {
|
||||
}
|
||||
pub fn hash(&self, addr: usize) -> Option<u8> {
|
||||
match self {
|
||||
SimpleAddressing(mask) => None, //Some(addr & *mask),
|
||||
SimpleAddressing(mask) => Some(((addr >> mask.shift) & ((1 << mask.bits) - 1)) as u8),
|
||||
ComplexAddressing(masks) => {
|
||||
let mut res = 0;
|
||||
for mask in *masks {
|
||||
@ -143,7 +150,7 @@ impl CacheSlicing {
|
||||
// May work in the future for simple.
|
||||
fn pivot(&self, mask: isize) -> Vec<(u8, isize)> {
|
||||
match self {
|
||||
ComplexAddressing(functions) => {
|
||||
ComplexAddressing(_functions) => {
|
||||
let mut matrix = Vec::new();
|
||||
|
||||
let mut i = 1;
|
||||
@ -248,7 +255,7 @@ impl CacheSlicing {
|
||||
result.insert(0, 0);
|
||||
|
||||
for (slice_u, addr_u) in matrix {
|
||||
if (slice_u != 0) {
|
||||
if slice_u != 0 {
|
||||
let mut tmp = HashMap::new();
|
||||
for (slice_v, addr_v) in &result {
|
||||
tmp.insert(slice_v ^ slice_u, addr_v ^ addr_u);
|
||||
|
||||
@ -47,6 +47,28 @@ pub unsafe fn flush(p: *const u8) {
|
||||
|
||||
pub fn noop<T>(_: *const T) {}
|
||||
|
||||
#[cfg(feature = "use_std")]
|
||||
pub fn find_core_per_socket() -> u8 {
|
||||
use std::process::Command;
|
||||
use std::str::from_utf8;
|
||||
|
||||
let core_per_socket_out = Command::new("sh")
|
||||
.arg("-c")
|
||||
.arg("lscpu | grep socket | cut -b 22-")
|
||||
.output()
|
||||
.expect("Failed to detect cpu count");
|
||||
//println!("{:#?}", core_per_socket_str);
|
||||
|
||||
let core_per_socket_str = from_utf8(&core_per_socket_out.stdout).unwrap();
|
||||
|
||||
//println!("Number of cores per socket: {}", cps_str);
|
||||
|
||||
let core_per_socket: u8 = core_per_socket_str[0..(core_per_socket_str.len() - 1)]
|
||||
.parse()
|
||||
.unwrap_or(0);
|
||||
core_per_socket
|
||||
}
|
||||
|
||||
// future enhancements
|
||||
// prefetch
|
||||
// long nop (64 nops)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user