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114 lines
3.4 KiB
114 lines
3.4 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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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", "1gib"]
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sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "1 GiB"]
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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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data = {
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x_label : sizes_nice,
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types_nice[0] : [],
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types_nice[1] : [],
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types_nice[2] : [],
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types_nice[3] : [],
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types_nice[4] : []
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}
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stdev = {}
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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,s,t):
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with open(file_path, 'r') as file:
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data = json.load(file)
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time_microseconds = data["list"][0]["report"]["time"]["combined_avg"]
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if t not in stdev: stdev[t] = dict()
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stdev[t][s] = data["list"][0]["report"]["time"]["combined_stdev"]
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return time_microseconds
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def stdev_functor(values):
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v = values[0]
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sd = stdev[v]
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return (v - sd, v + sd)
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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_microseconds = load_and_process_submit_json(file_path,sizes_nice[idx],type_nice)
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times.append(time_microseconds)
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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 = [time / 10 for time in times]
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elif type_label in {"ms50", "bs50"}:
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times = [time / 50 for time in times]
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times[0] = times[0] / 1
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times[1] = times[1] / 4
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times[2] = times[2] / 1024
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times[3] = times[3] / (1024 * 1024)
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throughput = [calc_throughput(1024,t) for t in times]
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data[type_nice] = throughput
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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/submit-bench/" # 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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dfm = pd.melt(df, id_vars=x_label, var_name=var_label, value_name=y_label)
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error_values: List[float] = []
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for index,row in dfm.iterrows():
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s = dfm[x_label][index]
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t = dfm[var_label][index]
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error_values.append(stdev[t][s])
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dfm["Stdev"] = error_values
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print(dfm)
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sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis", errorbar=("ci", 100))
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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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