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95 lines
3.2 KiB
95 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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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 = "Copy Type"
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y_label = "Throughput in GiB/s"
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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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title = "Performance of Engine Location - Copy Operation on DDR with Size 1 MiB and 1 Engine per WQ"
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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-9
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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_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 3
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time0 = data["list"][0]["report"]["time"]
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time1 = data["list"][1]["report"]["time"]
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return {
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"combined" : [sum(x) / 4 for x in zip(time0["combined"], time1["combined"])],
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"submission" : [sum(x) / 4 for x in zip(time0["completion"], time1["completion"])],
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"completion" : [sum(x) / 4 for x in zip(time0["submission"], time1["submission"])]
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}
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else:
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return data["list"][0]["report"]["time"]
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# Function to plot the graph for the new benchmark
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def create_copy_dataset(file_paths, method_label):
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times = []
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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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idx = 0
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for file_path in file_paths:
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time = load_and_process_copy_json(file_path,method_label)
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times.append(time["combined"])
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idx = idx + 1
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throughput = [[calc_throughput(1024*1024,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: types_nice[idx], var_label : method_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/"
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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-1e.json") for type_label in types]
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create_copy_dataset(copy_file_paths, method_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-enginelocation.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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