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101 lines
3.2 KiB
101 lines
3.2 KiB
import os
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import json
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import pandas as pd
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from pandas.core.ops import methods
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from typing import List
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import seaborn as sns
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import matplotlib.pyplot as plt
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runid = "Run ID"
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x_label = "Size of Submitted Task"
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y_label = "Throughput in GiB/s"
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var_label = "Submission Type"
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sizes = ["1kib", "4kib", "1mib", "32mib"]
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sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "32 MiB"]
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types = ["bs10", "bs50", "ms10", "ms50", "ssaw"]
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types_nice = ["Batch, Size 10", "Batch, Size 50", "Multi-Submit, Count 10", "Multi-Submit, Count 50", "Single Submit"]
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title = "Performance of Submission Methods - Copy Operation tested Intra-Node on DDR"
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index = [runid, x_label, var_label]
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data = []
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def calc_throughput(size_bytes,time_microseconds):
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time_seconds = time_microseconds * 1e-6
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size_gib = size_bytes / (1024 ** 3)
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throughput_gibs = size_gib / time_seconds
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return throughput_gibs
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def index_from_element(value,array):
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for (idx,val) in enumerate(array):
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if val == value: return idx
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return 0
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def load_and_process_submit_json(file_path):
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with open(file_path, 'r') as file:
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data = json.load(file)
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time = {
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"combined" : data["list"][0]["report"]["time"]["combined"],
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"submit" : data["list"][0]["report"]["time"]["submission"],
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"complete" : data["list"][0]["report"]["time"]["completion"]
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}
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return time
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# Function to plot the graph for the new benchmark
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def plot_submit_graph(file_paths, type_label):
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times = []
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type_index = index_from_element(type_label,types)
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type_nice = types_nice[type_index]
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idx = 0
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for file_path in file_paths:
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time = load_and_process_submit_json(file_path)
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times.append(time["combined"])
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idx = idx + 1
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# Adjust time measurements based on type
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# which can contain multiple submissions
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if type_label in {"bs10", "ms10"}:
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times = [[t / 10 for t in time] for time in times]
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elif type_label in {"ms50", "bs50"}:
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times = [[t / 50 for t in time] for time in times]
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times[0] = [t / 1 for t in times[0]]
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times[1] = [t / 4 for t in times[1]]
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times[2] = [t / (1024) for t in times[2]]
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times[3] = [t / (32*1024) for t in times[3]]
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throughput = [[calc_throughput(1000*1000,time) for time in t] for t in times]
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idx = 0
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for run_set in throughput:
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run_idx = 0
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for run in run_set:
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data.append({ runid : run_idx, x_label: sizes_nice[idx], var_label : type_nice, y_label : throughput[idx][run_idx]})
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run_idx = run_idx + 1
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idx = idx + 1
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# Main function to iterate over files and create plots for the new benchmark
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def main():
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folder_path = "benchmark-results/" # Replace with the actual path to your folder
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for type_label in types:
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file_paths = [os.path.join(folder_path, f"submit-{type_label}-{size}-1e.json") for size in sizes]
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plot_submit_graph(file_paths, type_label)
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df = pd.DataFrame(data)
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df.set_index(index, inplace=True)
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df = df.sort_values(y_label)
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sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd")
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plt.title(title)
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plt.savefig(os.path.join(folder_path, "plot-perf-submitmethod.png"), bbox_inches='tight')
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plt.show()
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if __name__ == "__main__":
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main()
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