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112 lines
4.4 KiB
112 lines
4.4 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 = "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-n0ton4-1mib", "internode-n0ton1-1mib", "intersock-n0ton4-1gib", "internode-n0ton1-1gib"]
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types_nice = ["Inter-Socket 1MiB", "Inter-Node 1MiB", "Inter-Socket 1GiB", "Inter-Node 1GiB"]
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copy_methods = ["dstcopy", "srccopy", "xcopy", "srcoutsidercopy", "dstoutsidercopy", "sockoutsidercopy", "nodeoutsidercopy"]
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copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines", "Engine on SRC-Socket, not SRC-Node", "Engine on DST-Socket, not DST-Node", "Engine on different Socket", "Engine on same Socket"]
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title = \
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"""Throughput showing impact of Engine Location\n
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Copy Operation on DDR with 1 Engine per WQ"""
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description = \
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"""Throughput showing impact of Engine Location\n
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Some Configurations missing as they are not feesible\n
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Copy Operation 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,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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iterations = data["list"][0]["task"]["iterations"]
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if method_label == "xcopy":
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# xcopy runs on two engines that both copy 1/2 of the entire
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# specified size of 1gib, therefore the maximum time between
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# these two is going to be the total time for copy
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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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"total": max(time0["total"],time1["total"]) / iterations,
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"combined" : [max(x,y) for x,y in zip(time0["combined"], time1["combined"])],
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"submission" : [max(x,y) for x,y in zip(time0["completion"], time1["completion"])],
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"submission" : [max(x,y) for x,y in zip(time0["completion"], time1["completion"])],
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}
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else:
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return {
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"total": data["list"][0]["report"]["time"]["total"] / iterations,
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"combined": data["list"][0]["report"]["time"]["combined"],
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"submission": data["list"][0]["report"]["time"]["submission"],
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"completion": 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 create_copy_dataset(file_path, method_label, type_label):
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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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type_index = index_from_element(type_label, types)
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type_nice = types_nice[type_index]
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data_size = 0
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if type_label in ["internode-n0ton1-1gib", "intersock-n0ton4-1gib"]: data_size = 1024*1024*1024
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elif type_label in ["internode-n0ton1-1mib", "intersock-n0ton4-1mib"]: data_size = 1024 * 1024
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else: data_size = 0
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try:
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run_idx = 0
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time = [load_time_mesurements(file_path,method_label)["total"]]
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for t in time:
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data.append({ runid : run_idx, x_label: type_nice, var_label : method_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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result_path = "benchmark-results/"
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output_path = "benchmark-plots/"
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for method_label in copy_methods:
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for type_label in types:
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file = os.path.join(result_path, f"{method_label}-{type_label}-1e.json")
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create_copy_dataset(file, method_label, 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(output_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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