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25077667/banchmark.py

Created February 4, 2024 13:00
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perfect-forwarding-is-faster-than-c-pointer
Raw
banchmark.py
import subprocess
import re
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
import json
import datetime
import os
def execute_command(command, regex, iterations):
c_style_times = []
perfect_forward_times = []
for _ in range(iterations):
print(f"\rProgress: {(_+1)/iterations*100:.2f}%", end="")
try:
output = subprocess.check_output(command, shell=True, text=True)
times = regex.findall(output)
for method, time in times:
if method == "C-style pointer":
c_style_times.append(float(time))
elif method == "perfect forwarding":
perfect_forward_times.append(float(time))
except subprocess.CalledProcessError as e:
print(f"\nAn error occurred: {e}")
return c_style_times, perfect_forward_times
def remove_outliers(data):
# it might be divided by zero
try:
q1, q3 = np.percentile(data, [25, 75])
except:
return data
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
return [x for x in data if x >= lower_bound and x <= upper_bound]
def plot_data(
c_style_times, perfect_forward_times, other_info, title_suffix="", filtered=False
):
if filtered:
c_style_times = remove_outliers(c_style_times)
perfect_forward_times = remove_outliers(perfect_forward_times)
# Ensure there's enough data to calculate statistics
if len(c_style_times) > 1 and len(perfect_forward_times) > 1:
c_style_std = np.std(c_style_times)
perfect_forward_std = np.std(perfect_forward_times)
_, p_value = stats.ttest_ind(c_style_times, perfect_forward_times)
c_style_mean = np.mean(c_style_times)
perfect_forward_mean = np.mean(perfect_forward_times)
else:
c_style_std = perfect_forward_std = c_style_mean = perfect_forward_mean = (
p_value
) = np.nan
plt.figure(figsize=(10, 6))
plt.scatter(
range(len(c_style_times)),
c_style_times,
label=f"C-style Pointer {title_suffix} (std: {c_style_std:.2f} mean: {c_style_mean:.2f})",
color="blue",
s=10,
)
plt.scatter(
range(len(perfect_forward_times)),
perfect_forward_times,
label=f"Perfect Forwarding {title_suffix} (std: {perfect_forward_std:.2f} mean: {perfect_forward_mean:.2f}",
color="red",
s=10,
)
plt.title(f"Performance Comparison {title_suffix} (p-value: {p_value:.2e})")
plt.xlabel("Iteration")
plt.ylabel("Time Taken (ms)")
plt.text(
0.01,
0.01,
"\n".join(f"{k}: {v}" for k, v in other_info.items()),
horizontalalignment="left",
verticalalignment="bottom",
transform=plt.gca().transAxes,
)
plt.legend()
plt.show()
def save_data(c_style_times, perfect_forward_times, other_info):
data = {
"c_style_times": c_style_times,
"perfect_forward_times": perfect_forward_times,
"other_info": other_info,
}
file_name = (
f"benchmark_data_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.json"
)
with open(file_name, "w") as f:
json.dump(data, f)
print(f"Data saved to {file_name}")
OTHER_INFO = {
"gcc": "13.2.1",
"linux": "6.7.3-arch1-1",
"CPU": "11th Gen Intel i5-1135G7 (8) @ 4.200GHz",
"Memory": "39963MiB DDR4 @ 3200MHz",
}
def main():
iterations = 1068
command = "./out"
time_regex = re.compile(
r"Time taken by function with (C-style pointer|perfect forwarding): (\d+) ns"
)
c_style_times, perfect_forward_times = execute_command(
command, time_regex, iterations
)
save_data(c_style_times, perfect_forward_times, OTHER_INFO)
# Process and plot data
plot_data(
c_style_times,
perfect_forward_times,
OTHER_INFO,
title_suffix="(Raw Data)",
filtered=False,
)
plot_data(
c_style_times,
perfect_forward_times,
{
"gcc": "13.2.1",
"linux": "6.7.3-arch1-1",
"CPU": "11th Gen Intel i5-1135G7 (8) @ 4.200GHz",
"Memory": "39963MiB DDR4 @ 3200MHz",
},
title_suffix="(Filtered Data)",
filtered=True,
)
if __name__ == "__main__":
main()
Raw
benchmark_data_2024-02-04_17-26-13(largeObject).json
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Raw
benchmark_data_2024-02-04_17-41-14(emptyObject).json
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"other_info": {"gcc": "13.2.1", "linux": "6.7.3-arch1-1", "CPU": "11th Gen Intel i5-1135G7 (8) @ 4.200GHz", "Memory": "39963MiB DDR4 @ 3200MHz"}}
Raw
main.cpp
#include <iostream>
#include <utility>
#include <vector>
#include <chrono>
#include <limits>
class HeavyObject
{
public:
HeavyObject()
{
// 4 pages of data
// data.resize(4 * 4096);
}
HeavyObject(const HeavyObject &)
{
}
HeavyObject(HeavyObject &&) noexcept
{
}
~HeavyObject()
{
}
// Some meaningful methods here
void processData()
{
// Process data...
}
private:
std::vector<int> data;
};
void processWithPointer(HeavyObject *obj)
{
obj->processData();
// More operations...
}
template <typename T>
void processWithPerfectForwarding(T &&obj)
{
std::forward<T>(obj).processData();
// More operations...
}
int main()
{
constexpr auto iterations = std::numeric_limits<int>::max(); // Number of iterations for the measurement
// Measure time for C-style pointer
auto start1 = std::chrono::high_resolution_clock::now();
for (int i = 0; i < iterations; ++i)
{
HeavyObject obj;
processWithPointer(&obj);
}
auto finish1 = std::chrono::high_resolution_clock::now();
// Measure time for perfect forwarding
auto start2 = std::chrono::high_resolution_clock::now();
for (int i = 0; i < iterations; ++i)
{
HeavyObject obj;
processWithPerfectForwarding(std::move(obj));
}
auto finish2 = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::nano> elapsed1 = finish1 - start1;
std::chrono::duration<double, std::nano> elapsed2 = finish2 - start2;
std::cout << "Time taken by function with C-style pointer: " << elapsed1.count() << " ns\n";
std::cout << "Time taken by function with perfect forwarding: " << elapsed2.count() << " ns\n";
return 0;
}
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