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108 lines
3.9 KiB
108 lines
3.9 KiB
import os
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import json
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from common import calc_throughput, index_from_element
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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 10", "Batch 50", "Multi-Submit 10", "Multi-Submit 50", "Single-Submit"]
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title = \
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"""Throughput showing Optimal Submission Method and Size\n
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Copy Operation tested Intra-Node on DDR with 1 Engine per WQ"""
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description = \
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"""Throughput showing Optimal Submission Method and Size\n
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Batch uses a Batch Descriptor of given Size\n
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Multi-Submit fills the Work Queue with n Single Descriptors\n
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Single-Submit submits one Descriptor and immediately waits\n
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Copy Operation tested Intra-Node on DDR with 1 Engine per WQ"""
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index = [runid, x_label, var_label]
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data = []
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# loads the measurements from a given file and processes them
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# so that they are normalized, meaning that the timings returned
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# are nanoseconds per element transfered
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def load_time_mesurements(file_path,type_label):
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with open(file_path, 'r') as file:
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data = json.load(file)
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iterations = data["list"][0]["task"]["iterations"]
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divisor = 1
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# bs and ms types for submission process more than one
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# element per run and the results therefore must be
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# divided by this number
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if type_label in ["bs10", "ms10"]: divisor = 10
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elif type_label in ["ms50", "bs50"]: divisor = 50
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else: divisor = 1
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return {
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"total": data["list"][0]["report"]["time"]["total"] / (iterations * divisor),
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"combined": [ x / divisor for x in data["list"][0]["report"]["time"]["combined"]],
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"submission": [ x / divisor for x in data["list"][0]["report"]["time"]["submission"]],
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"completion": [ x / divisor for x in data["list"][0]["report"]["time"]["completion"]]
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}
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# procceses a single file and appends the desired timings
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# to the global data-array, handles multiple runs with a runid
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# and ignores if the given file is not found as some
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# configurations may not be benchmarked
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def process_file_to_dataset(file_path, type_label,size_label):
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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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size_index = index_from_element(size_label, sizes)
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size_nice = sizes_nice[size_index]
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data_size = 0
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if size_label == "1kib": data_size = 1024;
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elif size_label == "4kib": data_size = 4 * 1024;
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elif size_label == "1mib": data_size = 1024 * 1024;
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elif size_label == "32mib": data_size = 32 * 1024 * 1024;
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elif size_label == "1gib": data_size = 1024 * 1024 * 1024;
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else: data_size = 0
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try:
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time = load_time_mesurements(file_path,type_label)["combined"]
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run_idx = 0
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for t in time:
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data.append({ runid : run_idx, x_label: size_nice, var_label : type_nice, y_label : calc_throughput(data_size, t)})
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run_idx = run_idx + 1
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except FileNotFoundError:
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return
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# loops over all possible configuration combinations and calls
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# process_file_to_dataset for them in order to build a dataframe
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# which is then displayed and saved
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def main():
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folder_path = "benchmark-results/"
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for type_label in types:
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for size in sizes:
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file = os.path.join(folder_path, f"submit-{type_label}-{size}-1e.json")
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process_file_to_dataset(file, type_label, size)
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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-opt-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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