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80 lines
2.7 KiB
80 lines
2.7 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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import seaborn as sns
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import matplotlib.pyplot as plt
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x_label = "Copy Type"
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y_label = "Time in Microseconds"
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var_label = "Configuration"
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types = ["intersock-n0ton4", "internode-n0ton1"]
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types_nice = ["Inter-Socket Copy", "Inter-Node Copy"]
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copy_methods = ["dstcopy", "srccopy", "xcopy"]
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copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines" ]
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data = {
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x_label : types_nice,
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copy_methods_nice[0] : [],
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copy_methods_nice[1] : [],
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copy_methods_nice[2] : []
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}
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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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# Function to load and process the JSON file for the new benchmark
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def load_and_process_copy_json(file_path, method_label):
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with open(file_path, 'r') as file:
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data = json.load(file)
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# Extracting time from JSON structure
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if method_label == "xcopy":
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# For xcopy method, add times from two entries and divide by 4
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time_entry1 = data["list"][0]["report"]["time"]["combined_avg"]
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time_entry2 = data["list"][1]["report"]["time"]["combined_avg"]
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time_microseconds = (time_entry1 + time_entry2) / 4
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else:
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# For other methods, use the time from the single entry
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time_microseconds = data["list"][0]["report"]["time"]["combined_avg"]
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return time_microseconds
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# Function to plot the graph for the new benchmark
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def plot_copy_graph(file_paths, method_label):
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times = []
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for file_path in file_paths:
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# Load and process JSON file for the new benchmark
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time_microseconds = load_and_process_copy_json(file_path, method_label)
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times.append(time_microseconds)
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method_index = index_from_element(method_label,copy_methods)
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method_nice = copy_methods_nice[method_index]
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data[method_nice] = times
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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/cross-copy-bench/" # Replace with the actual path to your folder
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for method_label in copy_methods:
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copy_file_paths = [os.path.join(folder_path, f"{method_label}-{type_label}-1mib-4e.json") for type_label in types]
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plot_copy_graph(copy_file_paths, method_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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sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis")
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plt.savefig(os.path.join(folder_path, "plot-perf-enginelocation.png"))
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plt.show()
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if __name__ == "__main__":
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main()
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