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.
89 lines
3.0 KiB
89 lines
3.0 KiB
import os
|
|
import json
|
|
import pandas as pd
|
|
from itertools import chain
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
runid = "Run ID"
|
|
x_label = "Thread Count"
|
|
y_label = "Throughput in GiB/s"
|
|
var_label = "Thread Counts"
|
|
thread_counts = ["1t", "2t", "4t", "8t", "12t"]
|
|
thread_counts_nice = ["1 Thread", "2 Threads", "4 Threads", "8 Threads", "12 Threads"]
|
|
engine_counts = ["ms50-1e", "ms50-4e", "ssaw-1e", "ssaw-4e"]
|
|
engine_counts_nice = ["1 E/WQ Multisubmit 50", "4 E/WQ Multisubmit 50", "1 E/WQ Single Submit", "4 E/WQ Single Submit"]
|
|
title = "Per-Thread Throughput - Copy Operation Intra-Node on DDR with Size 1 MiB"
|
|
|
|
index = [runid, x_label, var_label]
|
|
data = []
|
|
|
|
def calc_throughput(size_bytes,time_microseconds):
|
|
time_seconds = time_microseconds * 1e-9
|
|
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_copy_json(file_path):
|
|
with open(file_path, 'r') as file:
|
|
data = json.load(file)
|
|
|
|
count = data["count"]
|
|
|
|
return {
|
|
"combined" : list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)])),
|
|
"submission" : list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)])),
|
|
"completion" : list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))
|
|
}
|
|
|
|
# Function to plot the graph for the new benchmark
|
|
def create_mtsubmit_dataset(file_paths, engine_label):
|
|
times = []
|
|
|
|
engine_index = index_from_element(engine_label,engine_counts)
|
|
engine_nice = engine_counts_nice[engine_index]
|
|
|
|
idx = 0
|
|
for file_path in file_paths:
|
|
time = load_and_process_copy_json(file_path)
|
|
times.append(time["combined"])
|
|
idx = idx + 1
|
|
|
|
throughput = [[calc_throughput(10*1024*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: thread_counts_nice[idx], var_label : engine_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/" # Replace with the actual path to your folder
|
|
|
|
for engine_label in engine_counts:
|
|
mt_file_paths = [os.path.join(folder_path, f"mtsubmit-{thread_count}-{engine_label}.json") for thread_count in thread_counts]
|
|
create_mtsubmit_dataset(mt_file_paths, engine_label)
|
|
|
|
df = pd.DataFrame(data)
|
|
df.set_index(index, inplace=True)
|
|
|
|
sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd")
|
|
|
|
plt.title(title)
|
|
plt.savefig(os.path.join(folder_path, "plot-perf-mtsubmit.png"), bbox_inches='tight')
|
|
plt.show()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|