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.
91 lines
2.9 KiB
91 lines
2.9 KiB
import os
|
|
import pandas as pd
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
from common import calc_throughput, index_from_element, load_time_mesurements
|
|
|
|
runid = "Run ID"
|
|
x_label = "Thread Count"
|
|
y_label = "Throughput in GiB/s"
|
|
var_label = "Transfer Size"
|
|
thread_counts = ["1t", "2t", "12t"]
|
|
thread_counts_nice = ["1 Thread", "2 Threads", "12 Threads"]
|
|
size_labels = ["1mib", "1gib"]
|
|
size_labels_nice = ["1 MiB", "1 GiB"]
|
|
|
|
title = \
|
|
"""Total Throughput showing cost of MT Submit\n
|
|
Copying 120x split on n Threads Intra-Node on DDR\n
|
|
"""
|
|
|
|
description = \
|
|
"""Total Throughput showing cost of MT Submit\n
|
|
Running 120 Copy Operations split on n Threads\n
|
|
Copying Intra-Node on DDR performed for multiple Configurations\n
|
|
"""
|
|
|
|
index = [runid, x_label, var_label]
|
|
data = []
|
|
|
|
|
|
# loads the measurements from a given file and processes them
|
|
# so that they are normalized, meaning that the timings returned
|
|
# are nanoseconds per element transfered
|
|
def get_timing(file_path,thread_count):
|
|
divisor = 0
|
|
|
|
if thread_count == "1t": divisor = 1
|
|
elif thread_count == "2t" : divisor = 2
|
|
elif thread_count == "12t" : divisor = 12
|
|
|
|
return [ x / divisor for x in load_time_mesurements(file_path)]
|
|
|
|
|
|
# procceses a single file and appends the desired timings
|
|
# to the global data-array, handles multiple runs with a runid
|
|
# and ignores if the given file is not found as some
|
|
# configurations may not be benchmarked
|
|
def process_file_to_dataset(file_path, size_label, thread_count):
|
|
size_index = index_from_element(size_label,size_labels)
|
|
size_nice = size_labels_nice[size_index]
|
|
threadc_index = index_from_element(thread_count, thread_counts)
|
|
thread_count_nice = thread_counts_nice[threadc_index]
|
|
data_size = 0
|
|
|
|
if size_label == "1gib" : data_size = 1024*1024*1024
|
|
elif size_label == "1mib" : data_size = 1024*1024
|
|
|
|
timing = get_timing(file_path, thread_count)
|
|
run_idx = 0
|
|
for t in timing:
|
|
data.append({ runid : run_idx, x_label: thread_count_nice, var_label : size_nice, y_label : calc_throughput(data_size, t)})
|
|
run_idx = run_idx + 1
|
|
|
|
|
|
|
|
# loops over all possible configuration combinations and calls
|
|
# process_file_to_dataset for them in order to build a dataframe
|
|
# which is then displayed and saved
|
|
def main():
|
|
result_path = "benchmark-results/"
|
|
output_path = "benchmark-plots/"
|
|
|
|
for size in size_labels:
|
|
for thread_count in thread_counts:
|
|
file = os.path.join(result_path, f"mtsubmit-{thread_count}-{size}.json")
|
|
process_file_to_dataset(file, size, thread_count)
|
|
|
|
df = pd.DataFrame(data)
|
|
df.set_index(index, inplace=True)
|
|
|
|
plt.figure(figsize=(4, 4))
|
|
plt.ylim(0, 30)
|
|
sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="mako", errorbar="sd")
|
|
|
|
plt.savefig(os.path.join(output_path, "plot-mtsubmit.pdf"), bbox_inches='tight')
|
|
plt.show()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|