You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
96 lines
3.2 KiB
96 lines
3.2 KiB
import os
|
|
import json
|
|
import pandas as pd
|
|
from pandas.core.ops import methods
|
|
from typing import List
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
runid = "Run ID"
|
|
x_label = "Size of Submitted Task"
|
|
y_label = "Throughput in GiB/s"
|
|
var_label = "Submission Type"
|
|
sizes = ["1kib", "4kib", "1mib", "1gib"]
|
|
sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "1 GiB"]
|
|
types = ["bs10", "bs50", "ms10", "ms50", "ssaw"]
|
|
types_nice = ["Batch, Size 10", "Batch, Size 50", "Multi-Submit, Count 10", "Multi-Submit, Count 50", "Single Submit"]
|
|
title = "Performance of Submission Methods - Copy Operation tested Intra-Node on DDR"
|
|
|
|
index = [runid, x_label, var_label]
|
|
data = []
|
|
|
|
def calc_throughput(size_bytes,time_microseconds):
|
|
time_seconds = time_microseconds * 1e-6
|
|
size_gib = size_bytes / (1024 ** 3)
|
|
throughput_gibs = size_gib / time_seconds
|
|
return throughput_gibs
|
|
|
|
|
|
def index_from_element(value,array):
|
|
for (idx,val) in enumerate(array):
|
|
if val == value: return idx
|
|
return 0
|
|
|
|
|
|
def load_and_process_submit_json(file_path):
|
|
with open(file_path, 'r') as file:
|
|
data = json.load(file)
|
|
time = { "combined" : data["list"][0]["report"]["time"]["combined"], "submit" : data["list"][0]["report"]["time"]["submit"], "complete" : data["list"][0]["report"]["time"]["complete"]}
|
|
return time
|
|
|
|
# Function to plot the graph for the new benchmark
|
|
def plot_submit_graph(file_paths, type_label):
|
|
times = []
|
|
|
|
type_index = index_from_element(type_label,types)
|
|
type_nice = types_nice[type_index]
|
|
|
|
idx = 0
|
|
for file_path in file_paths:
|
|
time = load_and_process_submit_json(file_path)
|
|
times.append(time["combined"])
|
|
idx = idx + 1
|
|
|
|
# Adjust time measurements based on type
|
|
# which can contain multiple submissions
|
|
if type_label in {"bs10", "ms10"}:
|
|
times = [[t / 10 for t in time] for time in times]
|
|
elif type_label in {"ms50", "bs50"}:
|
|
times = [[t / 50 for t in time] for time in times]
|
|
|
|
times[0] = [t / 1 for t in times[0]]
|
|
times[1] = [t / 4 for t in times[1]]
|
|
times[2] = [t / (1024) for t in times[2]]
|
|
times[3] = [t / (1024*1024) for t in times[3]]
|
|
|
|
throughput = [[calc_throughput(1024,time) for time in t] for t in times]
|
|
|
|
idx = 0
|
|
for run_set in throughput:
|
|
run_idx = 0
|
|
for run in run_set:
|
|
data.append({ runid : run_idx, x_label: sizes_nice[idx], var_label : type_nice, y_label : throughput[idx][run_idx]})
|
|
run_idx = run_idx + 1
|
|
idx = idx + 1
|
|
|
|
|
|
# Main function to iterate over files and create plots for the new benchmark
|
|
def main():
|
|
folder_path = "benchmark-results/submit-bench/" # Replace with the actual path to your folder
|
|
|
|
|
|
for type_label in types:
|
|
file_paths = [os.path.join(folder_path, f"submit-{type_label}-{size}-1e.json") for size in sizes]
|
|
plot_submit_graph(file_paths, type_label)
|
|
|
|
df = pd.DataFrame(data)
|
|
df.set_index(index, inplace=True)
|
|
print(df)
|
|
|
|
sns.catplot(x=x_label, y=y_label, hue=var_label, data=df, kind='bar', height=5, aspect=1, palette="viridis", errorbar="sd")
|
|
plt.title(title)
|
|
plt.savefig(os.path.join(folder_path, "plot-perf-submitmethod.png"), bbox_inches='tight')
|
|
plt.show()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|