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.
94 lines
3.0 KiB
94 lines
3.0 KiB
import os
|
|
import pandas as pd
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
from common import calc_throughput, load_time_mesurements, get_task_count
|
|
|
|
result_path = "benchmark-results/"
|
|
output_path = "benchmark-plots/"
|
|
|
|
runid = "Run ID"
|
|
x_label = "Destination Node"
|
|
y_label = "Throughput in GiB/s"
|
|
|
|
title_allnodes = \
|
|
"""Copy Throughput in GiB/s tested for 1GiB Elements\n
|
|
Using all 8 DSA Chiplets available on the System"""
|
|
title_smartnodes = \
|
|
"""Copy Throughput in GiB/s tested for 1GiB Elements\n
|
|
Using Cross-Copy for Intersocket and all 4 Chiplets of Socket for Intrasocket"""
|
|
title_difference = \
|
|
"""Gain in Copy Throughput in GiB/s of All-DSA vs. Smart Assignment"""
|
|
|
|
description_smartnodes = \
|
|
"""Copy Throughput in GiB/s tested for 1GiB Elements\n
|
|
Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
|
|
Using all 4 DSA Chiplets of a Socket for Intra-Socket Operation\n
|
|
And using only the Source and Destination Nodes DSA for Inter-Socket"""
|
|
description_allnodes = \
|
|
"""Copy Throughput in GiB/s tested for 1GiB Elements\n
|
|
Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
|
|
Using all 8 DSA Chiplets available on the System"""
|
|
|
|
index = [ runid, x_label, y_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):
|
|
divisor = get_task_count(file_path)
|
|
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, src_node, dst_node):
|
|
size = 1024*1024*1024
|
|
|
|
timing = get_timing(file_path)
|
|
run_idx = 0
|
|
for t in timing:
|
|
tp = calc_throughput(size, t)
|
|
data.append({ runid : run_idx, x_label : dst_node, y_label : tp})
|
|
run_idx = run_idx + 1
|
|
|
|
|
|
def plot_bar(table,title,node_config):
|
|
plt.figure(figsize=(2, 3))
|
|
|
|
sns.barplot(x=x_label, y=y_label, data=table, palette="mako", errorbar="sd")
|
|
|
|
plt.ylim(0, 75)
|
|
|
|
plt.savefig(os.path.join(output_path, f"plot-{node_config}-cpu-throughput.pdf"), bbox_inches='tight')
|
|
plt.show()
|
|
|
|
|
|
# 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(node_config,title,ext):
|
|
src_node = 0
|
|
for dst_node in {8,11,12,15}:
|
|
file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}{ext}.json")
|
|
process_file_to_dataset(file, src_node, dst_node)
|
|
|
|
df = pd.DataFrame(data)
|
|
|
|
data.clear()
|
|
df.set_index(index, inplace=True)
|
|
|
|
if ext == "brute": node_config = ext
|
|
plot_bar(df, title, node_config)
|
|
|
|
return df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
dall = main("allnodes", title_allnodes, "-cpu")
|
|
dbrt = main("brute", title_allnodes, "-cpu")
|