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
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import os
import json
from numpy import float64, int64
from typing import List
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 = "Size of Submitted Task"
y_label = "Throughput in GiB/s"
var_label = "Submission Type"
sizes = ["1kib", "4kib", "1mib", "128mib"]
sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "128 MiB"]
types = ["bs10", "bs50", "ssaw"]
types_nice = ["Batch, Size 10", "Batch, Size 50", "Single Submit"]
title = \
"""Throughput showing Optimal Submission Method and Size\n
Copy Operation tested Intra-Node on DDR with 1 Engine per WQ"""
description = \
"""Throughput showing Optimal Submission Method and Size\n
Batch uses a Batch Descriptor of given Size\n
Multi-Submit fills the Work Queue with n Single Descriptors\n
Single-Submit submits one Descriptor and immediately waits\n
Copy Operation tested Intra-Node on DDR with 1 Engine per WQ"""
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,type_label) -> List[float64]:
divisor = 0
if type_label == "bs10": divisor = 10
elif type_label == "bs50" : divisor = 50
else: divisor = 1
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, type_label,size_label):
type_index = index_from_element(type_label,types)
type_nice = types_nice[type_index]
size_index = index_from_element(size_label, sizes)
size_nice = sizes_nice[size_index]
data_size = 0
if size_label == "1kib": data_size = 1024;
elif size_label == "4kib": data_size = 4 * 1024;
elif size_label == "1mib": data_size = 1024 * 1024;
elif size_label == "32mib": data_size = 32 * 1024 * 1024;
elif size_label == "1gib": data_size = 1024 * 1024 * 1024;
else: data_size = 0
try:
time = get_timing(file_path,type_label)
run_idx = 0
for t in time:
data.append({ runid : run_idx, x_label: size_nice, var_label : type_nice, y_label : calc_throughput(data_size, t)})
run_idx = run_idx + 1
except FileNotFoundError:
return
# 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 type_label in types:
for size in sizes:
file = os.path.join(result_path, f"submit-{type_label}-{size}-1e.json")
process_file_to_dataset(file, type_label, size)
df = pd.DataFrame(data)
df.set_index(index, inplace=True)
df = df.sort_values(y_label)
sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd")
plt.savefig(os.path.join(output_path, "plot-opt-submitmethod.pdf"), bbox_inches='tight')
plt.show()
if __name__ == "__main__":
main()