This contains my bachelors thesis and associated tex files, code snippets and maybe more. Topic: Data Movement in Heterogeneous Memories with Intel Data Streaming Accelerator
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.
 
 
 
 
 
 

158 lines
5.2 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 = []
data_avg = {}
# 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, config, dst_node):
size = 1024*1024*1024
if config not in data_avg:
data_avg[config] = 0
timing = get_timing(file_path)
run_idx = 0
for t in timing:
tp = calc_throughput(size, t)
data_avg[config] += tp / len(timing)
data.append({ runid : run_idx, x_label : dst_node, y_label : tp})
run_idx = run_idx + 1
def plot_bar(table,node_config,display_x,display_y):
plt.figure(figsize=(2, 3))
sns.barplot(x=x_label, y=y_label, data=table, palette="mako", errorbar="sd")
plt.ylim(0, 70)
plt.yticks([15,30,45,60,65])
plt.xlabel(display_x)
plt.ylabel(display_y)
plt.savefig(os.path.join(output_path, f"plot-{node_config}-throughput.pdf"), bbox_inches='tight')
def PlotAndrePeakResults():
data_peakbench_andre = [
{ runid : 0, x_label : 8, y_label : 64 },
{ runid : 0, x_label : 11, y_label : 63 },
{ runid : 0, x_label : 12, y_label : 40 },
{ runid : 0, x_label : 15, y_label : 54 }
]
df = pd.DataFrame(data_peakbench_andre)
df.set_index(index, inplace=True)
plot_bar(df, "andrepeak", x_label, y_label)
return df
# 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):
dst_nodes = {8,11,12,15}
for dst_node in dst_nodes:
file = os.path.join(result_path, f"copy-n0ton{dst_node}-1gib-{node_config}.json")
process_file_to_dataset(file, node_config, dst_node)
data_avg[node_config] = data_avg[node_config] / len(dst_nodes)
df = pd.DataFrame(data)
data.clear()
df.set_index(index, inplace=True)
plot_bar(df, node_config, x_label, y_label)
return df
def get_scaling_factor(baseline,topline,utilfactor):
return (topline / baseline) * (1 / utilfactor)
if __name__ == "__main__":
dsa_df1 = main("1dsa")
dsa_df2 = main("2dsa")
dsa_df4 = main("4dsa")
dsa_df8 = main("8dsa")
cpu_df8 = main("8cpu")
cpu_dfandre = PlotAndrePeakResults()
x_dsacount = "Count of DSAs"
y_avgtp = "Average Throughput in GiB/s"
y_scaling = "Scaling Factor"
data_average = [
{ runid : 0, x_label : 1, y_label : data_avg["1dsa"] },
{ runid : 0, x_label : 2, y_label : data_avg["2dsa"] },
{ runid : 0, x_label : 4, y_label : data_avg["4dsa"] },
{ runid : 0, x_label : 8, y_label : data_avg["8dsa"] }
]
average_df = pd.DataFrame(data_average)
average_df.set_index(index, inplace=True)
plot_bar(average_df, "average", x_dsacount, y_avgtp)
data_scaling = [
{ x_dsacount : 1, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["1dsa"], 1) },
{ x_dsacount : 2, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["2dsa"], 2) },
{ x_dsacount : 4, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["4dsa"], 4) },
{ x_dsacount : 8, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["8dsa"], 8) }
]
scaling_df = pd.DataFrame(data_scaling)
plt.figure(figsize=(2, 3))
fig = sns.lineplot(x=x_dsacount, y=y_scaling, data=scaling_df, marker='o', linestyle='-', color='b', markersize=8)
plt.xticks([1,2,4,8])
plt.yticks([0.25,0.5,0.75,1.0])
plt.xlim(0,10)
plt.ylim(0.2,1.05)
plt.savefig(os.path.join(output_path, f"plot-dsa-throughput-scaling.pdf"), bbox_inches='tight')