bachelor-thesis/benchmarks/benchmark-plotters/plot-perf-enginelocation.py

import os
import json
import pandas as pd
from pandas.core.ops import methods
import seaborn as sns
import matplotlib.pyplot as plt

x_label = "Copy Type"
y_label = "Throughput in GiB/s"
var_label = "Configuration"
types = ["intersock-n0ton4", "internode-n0ton1"]
types_nice = ["Inter-Socket Copy", "Inter-Node Copy"]
copy_methods = ["dstcopy", "srccopy", "xcopy"]
copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines" ]
title = "Performance of Engine Location - Copy Operation on DDR with Size 1 MiB"

data = {
    x_label : types_nice,
    copy_methods_nice[0] : [],
    copy_methods_nice[1] : [],
    copy_methods_nice[2] : []
}


def calc_throughput(size_bytes,time_microseconds):
    time_seconds = time_microseconds * 1e-6
    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


# Function to load and process the JSON file for the new benchmark
def load_and_process_copy_json(file_path, method_label):
    with open(file_path, 'r') as file:
        data = json.load(file)

        # Extracting time from JSON structure
        if method_label == "xcopy":
            # For xcopy method, add times from two entries and divide by 4
            time_entry1 = data["list"][0]["report"]["time"]["combined_avg"]
            time_entry2 = data["list"][1]["report"]["time"]["combined_avg"]
            time_microseconds = (time_entry1 + time_entry2) / 4
        else:
            # For other methods, use the time from the single entry
            time_microseconds = data["list"][0]["report"]["time"]["combined_avg"]

        return time_microseconds


# Function to plot the graph for the new benchmark
def plot_copy_graph(file_paths, method_label):
    times = []

    for file_path in file_paths:
        # Load and process JSON file for the new benchmark
        time_microseconds = load_and_process_copy_json(file_path, method_label)
        times.append(time_microseconds)

    method_index = index_from_element(method_label,copy_methods)
    method_nice = copy_methods_nice[method_index]

    throughput = [calc_throughput(1024*1024, t) for t in times]

    data[method_nice] = throughput


# Main function to iterate over files and create plots for the new benchmark
def main():
    folder_path = "benchmark-results/cross-copy-bench/"  # Replace with the actual path to your folder


    for method_label in copy_methods:
        copy_file_paths = [os.path.join(folder_path, f"{method_label}-{type_label}-1mib-4e.json") for type_label in types]
        plot_copy_graph(copy_file_paths, method_label)

    df = pd.DataFrame(data)
    dfm = pd.melt(df, id_vars=x_label, var_name=var_label, value_name=y_label)

    sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis")
    plt.title(title)
    plt.savefig(os.path.join(folder_path, "plot-perf-enginelocation.png"), bbox_inches='tight')
    plt.show()

if __name__ == "__main__":
    main()