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Update result analysis scripts and code to the version for results-2020-04-20

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This commit is contained in:
GuillaumeDIDIER 2020-05-01 10:24:15 +02:00
parent bb8996efd0
commit 26b2d22942
12 changed files with 279 additions and 26 deletions
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5
.idea/DendrobatesTinctoriusAzureus.iml generated
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@ -2,7 +2,7 @@
<module type="CPP_MODULE" version="4">
<component name="FacetManager">
<facet type="Python" name="Python facet">
<configuration sdkName="Python 3.7" />
<configuration sdkName="Python 3.7 (dendrobates-t-azureus)" />
</facet>
</component>
<component name="NewModuleRootManager">
@ -35,10 +35,11 @@
<excludeFolder url="file://$MODULE_DIR$/kernel/target" />
<excludeFolder url="file://$MODULE_DIR$/polling_serial/target" />
<excludeFolder url="file://$MODULE_DIR$/target" />
<excludeFolder url="file://$MODULE_DIR$/venv" />
<excludeFolder url="file://$MODULE_DIR$/vga_buffer/target" />
</content>
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
<orderEntry type="library" name="Python 3.7 interpreter library" level="application" />
<orderEntry type="library" name="Python 3.7 (dendrobates-t-azureus) interpreter library" level="application" />
</component>
</module>

35
cache_utils/results-2020-04-07-5c025fb/analyse_csv.py
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@ -1,4 +1,6 @@
import pandas
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
columns = ["Addr", "Hash"]
core_number = 8 # FIXME
@ -7,8 +9,37 @@ for i in range(0, core_number):
for op in ["Hit", "Miss"]:
columns.append(op + str(i) + stat)
columns.append("Hmm")
df = pandas.read_csv("citron-vert/combined.csv", header=0, names=columns)
df = pd.read_csv("citron-vert/combined.csv", header=0, names=columns)
selected_columns = columns[:-1]
df = df[selected_columns]
print(df.head())
median_columns = list(filter(lambda s: s.endswith("Med"), columns))
median_hits_col = list(filter(lambda s: s.startswith("Hit"), median_columns))
median_miss_col = list(filter(lambda s: s.startswith("Miss"), median_columns))
print(list(median_columns))
print(list(median_hits_col), list(median_miss_col))
hashes = df["Hash"].drop_duplicates()
print(hashes)
#def distrib(x, y, **kwargs):
# sns.distplot()
separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_hits_col)
g = sns.FacetGrid(separate_core_df, row="variable")
g.map(sns.distplot, "value")
plt.figure()
separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_miss_col)
g = sns.FacetGrid(separate_core_df, row="variable")
g.map(sns.distplot, "value", hist_kws={"range":(75,115)})
plt.show()
#sns.distplot(df["values"], hist_kws={"weights": df["count"]})

6
cache_utils/results-2020-04-17/analyse.sh Executable file
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@ -0,0 +1,6 @@
#!/bin/sh
awk '/^Iteration [:digit:]*[.]*/ ' < log.txt > iterations.txt
rm results.csv
awk -f `dirname $0`/analyse_iterations.awk < iterations.txt # This uses system to split off awk scripts doing the analysis
grep -v -e "0,0$" results.csv > results_lite.csv
#paste -d"," *.csv > combined.csv

63
cache_utils/results-2020-04-17/analyse_csv.py Normal file
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@ -0,0 +1,63 @@
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sys import exit
columns = ["Addr", "Hash"]
core_number = 8 # FIXME
for i in range(0, core_number):
for stat in ["Min", "Med", "Max"]:
for op in ["Hit", "Miss"]:
columns.append(op + str(i) + stat)
columns.append("Hmm")
df = pd.read_csv("./results_lite.csv")
print(df.head())
g = sns.FacetGrid(df, col="core", row="hash", legend_out=True)
def custom_hist(x,y, **kwargs):
sns.distplot(x, range(100,150), hist_kws={"weights": y, "histtype":"step"}, kde=False, **kwargs)
g.map(custom_hist, "time", "clflush_hit")
# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)
plt.show()
# test = pd.DataFrame({"value" : [0, 5], "weight": [5, 1]})
# plt.figure()
# sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)
exit(0)
selected_columns = columns[:-1]
df = df[selected_columns]
print(df.head())
median_columns = list(filter(lambda s: s.endswith("Med"), columns))
median_hits_col = list(filter(lambda s: s.startswith("Hit"), median_columns))
median_miss_col = list(filter(lambda s: s.startswith("Miss"), median_columns))
print(list(median_columns))
print(list(median_hits_col), list(median_miss_col))
hashes = df["Hash"].drop_duplicates()
print(hashes)
# def distrib(x, y, **kwargs):
# sns.distplot()
separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_hits_col)
g = sns.FacetGrid(separate_core_df, row="variable")
g.map(sns.distplot, "value")
plt.figure()
separate_core_df = df.melt(id_vars=["Addr", "Hash"], value_vars=median_miss_col)
g = sns.FacetGrid(separate_core_df, row="variable")
g.map(sns.distplot, "value", hist_kws={"range": (75, 115)})
plt.show()
# sns.distplot(df["values"], hist_kws={"weights": df["count"]})

16
cache_utils/results-2020-04-17/analyse_iterations.awk Normal file
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@ -0,0 +1,16 @@
BEGIN {
i = 0
}
{
start = $0
getline
end = $0
if (i == 0) {
# generate header
system("awk '$0 == \""start"\",$0 == \""end"\"' < log.txt | grep \"RESULT:\" | head -n 1 | cut -b 8- | awk '{print \"core,\" $0}'> results.csv")
}
cut = "cut -b 8- | tail -n +2"
system("awk '$0 == \""start"\",$0 == \""end"\"' < log.txt | grep \"RESULT:\" | " cut " | awk '{print \""i",\" $0}'>> results.csv")
i = i + 1
}

6
cache_utils/results-2020-04-20/analyse.sh Executable file
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@ -0,0 +1,6 @@
#!/bin/sh
awk '/^Iteration [:digit:]*[.]*/ ' < log.txt > iterations.txt
rm results.csv
awk -f `dirname $0`/analyse_iterations.awk < iterations.txt # This uses system to split off awk scripts doing the analysis
grep -v -e "0,0$" results.csv > results_lite.csv
#paste -d"," *.csv > combined.csv

52
cache_utils/results-2020-04-20/analyse_csv.py Normal file
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@ -0,0 +1,52 @@
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sys import exit
import wquantiles as wq
df = pd.read_csv("./results_lite.csv")
print(df.head())
g = sns.FacetGrid(df, col="core", row="hash", legend_out=True)
def custom_hist(x, y1, y2, **kwargs):
sns.distplot(x,range(200, 280), hist_kws={"weights": y1, "histtype":"step"}, kde=False, **kwargs)
kwargs["color"] = "r"
sns.distplot(x, range(200, 280), hist_kws={"weights": y2, "histtype":"step"}, kde=False, **kwargs)
g.map(custom_hist, "time", "clflush_hit", "clflush_miss")
# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)
plt.figure()
def stat(x, key):
return wq.median(x["time"], x[key])
miss = df.groupby(["core", "hash"]).apply(stat, "clflush_miss")
stats = miss.reset_index()
stats.columns = ["Core", "Hash", "Miss"]
hit = df.groupby(["core", "hash"]).apply(stat, "clflush_hit")
stats["Hit"] = hit.values
print(stats.to_string())
g = sns.FacetGrid(stats, row="Core")
g.map(sns.distplot, 'Miss', bins=range(200, 280), color="r")
g.map(sns.distplot, 'Hit', bins=range(200, 280))
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)

16
cache_utils/results-2020-04-20/analyse_iterations.awk Normal file
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@ -0,0 +1,16 @@
BEGIN {
i = 0
}
{
start = $0
getline
end = $0
if (i == 0) {
# generate header
system("awk '$0 == \""start"\",$0 == \""end"\"' < log.txt | grep \"RESULT:\" | head -n 1 | cut -b 8- | awk '{print \"core,\" $0}'> results.csv")
}
cut = "cut -b 8- | tail -n +2"
system("awk '$0 == \""start"\",$0 == \""end"\"' < log.txt | grep \"RESULT:\" | " cut " | awk '{print \""i",\" $0}'>> results.csv")
i = i + 1
}

75
cache_utils/src/calibration.rs
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@ -170,8 +170,16 @@ pub fn calibrate_flush(
cache_line_size,
array.len() as isize,
&[
(load_and_flush, "clflush hit"),
(flush_and_flush, "clflush miss"),
CalibrateOperation {
op: load_and_flush,
name: "clflush_hit",
display_name: "clflush hit",
},
CalibrateOperation {
op: flush_and_flush,
name: "clflush_miss",
display_name: "clflush miss",
},
],
CFLUSH_BUCKET_NUMBER,
CFLUSH_BUCKET_SIZE,
@ -189,11 +197,17 @@ pub struct CalibrateResult {
max: Vec<u64>,
}
pub struct CalibrateOperation<'a> {
pub op: unsafe fn(*const u8) -> u64,
pub name: &'a str,
pub display_name: &'a str,
}
pub unsafe fn calibrate(
p: *const u8,
increment: usize,
len: isize,
operations: &[(unsafe fn(*const u8) -> u64, &str)],
operations: &[CalibrateOperation],
buckets_num: usize,
bucket_size: usize,
num_iterations: u32,
@ -216,7 +230,7 @@ fn calibrate_impl(
p: *const u8,
increment: usize,
len: isize,
operations: &[(unsafe fn(*const u8) -> u64, &str)],
operations: &[CalibrateOperation],
buckets_num: usize,
bucket_size: usize,
num_iterations: u32,
@ -235,7 +249,10 @@ fn calibrate_impl(
if verbosity_level >= Thresholds {
println!(
"Calibrating {}...",
operations.iter().map(|(_, name)| { name }).format(", ")
operations
.iter()
.map(|operation| { operation.display_name })
.format(", ")
);
}
@ -245,11 +262,30 @@ fn calibrate_impl(
if verbosity_level >= Thresholds {
println!(
"CSV: address, hash, {} min, {} median, {} max",
operations.iter().map(|(_, name)| name).format(" min, "),
operations.iter().map(|(_, name)| name).format(" median, "),
operations.iter().map(|(_, name)| name).format(" max, ")
operations
.iter()
.map(|operation| operation.name)
.format(" min, "),
operations
.iter()
.map(|operation| operation.name)
.format(" median, "),
operations
.iter()
.map(|operation| operation.name)
.format(" max, ")
);
}
if verbosity_level >= RawResult {
println!(
"RESULT:address,hash,time,{}",
operations
.iter()
.map(|operation| operation.name)
.format(",")
);
}
for i in (0..len).step_by(increment) {
let pointer = unsafe { p.offset(i) };
let hash = hasher.hash(pointer as usize);
@ -271,7 +307,7 @@ fn calibrate_impl(
for op in operations {
let mut hist = vec![0; buckets_num];
for _ in 0..num_iterations {
let time = unsafe { op.0(pointer) };
let time = unsafe { (op.op)(pointer) };
let bucket = min(buckets_num - 1, to_bucket(time));
hist[bucket] += 1;
}
@ -286,16 +322,9 @@ fn calibrate_impl(
.map(|h| (num_iterations - h[buckets_num - 1]) / 2)
.collect();
if verbosity_level >= RawResult {
println!(
"time {}",
operations.iter().map(|(_, name)| name).format(" ")
);
}
for j in 0..buckets_num - 1 {
if verbosity_level >= RawResult {
print!("{:3}:", from_bucket(j));
print!("RESULT:{:p},{:x},{}", pointer, hash, from_bucket(j));
}
// ignore the last bucket : spurious context switches etc.
for op in 0..operations.len() {
@ -305,7 +334,7 @@ fn calibrate_impl(
let med = &mut calibrate_result.median[op];
let sum = &mut sums[op];
if verbosity_level >= RawResult {
print!("{:10}", hist);
print!(",{}", hist);
}
if *min == 0 {
@ -329,10 +358,10 @@ fn calibrate_impl(
}
}
if verbosity_level >= Thresholds {
for (j, (_, op)) in operations.iter().enumerate() {
for (j, op) in operations.iter().enumerate() {
println!(
"{}: min {}, median {}, max {}",
op,
op.display_name,
calibrate_result.min[j],
calibrate_result.median[j],
calibrate_result.max[j]
@ -367,7 +396,11 @@ pub fn calibrate_L3_miss_hit(
pointer,
cache_line_size,
array.len() as isize,
&[(l3_and_reload, "L3 hit")],
&[CalibrateOperation {
op: l3_and_reload,
name: "l3_hit",
display_name: "L3 hit",
}],
512,
2,
1 << 11,

1
cache_utils/src/lib.rs
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@ -1,5 +1,6 @@
#![cfg_attr(feature = "no_std", no_std)]
#![feature(ptr_internals)]
#![allow(clippy::missing_safety_doc)]
use static_assertions::assert_cfg;

27
cache_utils/src/main.rs
View File

@ -41,6 +41,31 @@ pub fn main() {
// Let's grab all the list of CPUS
// Then iterate the calibration on each CPU core.
eprint!("Warming up...");
for i in 0..(CpuSet::count() - 1) {
if old.is_set(i).unwrap() {
//println!("Iteration {}...", i);
let mut core = CpuSet::new();
core.set(i).unwrap();
match sched_setaffinity(Pid::from_raw(0), &core) {
Ok(()) => {
calibrate_flush(array, 64, Verbosity::NoOutput);
sched_setaffinity(Pid::from_raw(0), &old).unwrap();
//println!("Iteration {}...ok ", i);
eprint!(" {}", i);
}
Err(Sys(Errno::EINVAL)) => {
//println!("skipping");
continue;
}
Err(e) => {
panic!("Unexpected error while setting affinity: {}", e);
}
}
}
}
eprintln!();
for i in 0..(CpuSet::count() - 1) {
if old.is_set(i).unwrap() {
println!("Iteration {}...", i);
@ -50,7 +75,7 @@ pub fn main() {
match sched_setaffinity(Pid::from_raw(0), &core) {
Ok(()) => {
calibrate_flush(array, 64, Verbosity::NoOutput);
calibrate_flush(array, 64, Verbosity::Thresholds);
calibrate_flush(array, 64, Verbosity::RawResult);
sched_setaffinity(Pid::from_raw(0), &old).unwrap();
println!("Iteration {}...ok ", i);
eprintln!("Iteration {}...ok ", i);

3
requirements.txt Normal file
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@ -0,0 +1,3 @@
pandas==1.0.3
seaborn==0.10.0
wquantiles==0.5