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111 lines
4.2 KiB
111 lines
4.2 KiB
import os
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import json
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import pandas as pd
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from itertools import chain
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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
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result_path = "benchmark-results/"
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output_path = "benchmark-plots/"
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runid = "Run ID"
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x_label = "Destination Node"
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y_label = "Throughput in GiB/s"
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title_allnodes = \
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"""Copy Throughput in GiB/s tested for 1GiB Elements\n
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Using all 8 DSA Chiplets available on the System"""
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title_smartnodes = \
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"""Copy Throughput in GiB/s tested for 1GiB Elements\n
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Using Cross-Copy for Intersocket and all 4 Chiplets of Socket for Intrasocket"""
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title_difference = \
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"""Gain in Copy Throughput in GiB/s of All-DSA vs. Smart Assignment"""
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description_smartnodes = \
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"""Copy Throughput in GiB/s tested for 1GiB Elements\n
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Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
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Using all 4 DSA Chiplets of a Socket for Intra-Socket Operation\n
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And using only the Source and Destination Nodes DSA for Inter-Socket"""
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description_allnodes = \
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"""Copy Throughput in GiB/s tested for 1GiB Elements\n
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Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
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Using all 8 DSA Chiplets available on the System"""
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index = [ runid, x_label, y_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):
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with open(file_path, 'r') as file:
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data = json.load(file)
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count = data["count"]
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batch_size = data["list"][0]["task"]["batching"]["batch_size"] if data["list"][0]["task"]["batching"]["batch_size"] > 0 else 1
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iterations = data["list"][0]["task"]["iterations"]
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return {
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"size": data["list"][0]["task"]["size"],
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"total": sum([x / (iterations * batch_size * count * count) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
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"combined": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
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"submission": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
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"completion": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
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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, src_node, dst_node):
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try:
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file_data = load_time_mesurements(file_path)
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time = file_data["combined"]
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run_idx = 0
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for t in time:
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size = file_data["size"]
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tp = calc_throughput(size, t)
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data.append({ runid : run_idx, x_label : dst_node, y_label : tp})
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run_idx = run_idx + 1
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except FileNotFoundError:
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return
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def plot_bar(table,title,node_config):
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plt.figure(figsize=(8, 6))
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sns.barplot(x=x_label, y=y_label, data=table, palette="rocket", errorbar=None)
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plt.ylim(0, 100)
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plt.savefig(os.path.join(output_path, f"plot-perf-{node_config}-cpu-throughput-selectbarplot.pdf"), bbox_inches='tight')
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plt.show()
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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(node_config,title,ext):
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src_node = 0
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for dst_node in {8,11,12,15}:
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size = "512mib" if node_config == "allnodes" and src_node == dst_node and src_node >= 8 else "1gib"
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file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-{size}-{node_config}-cpu-{ext}.json")
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process_file_to_dataset(file, src_node, dst_node)
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df = pd.DataFrame(data)
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data.clear()
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df.set_index(index, inplace=True)
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if ext == "brute": node_config = ext
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plot_bar(df, title, node_config)
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return df
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if __name__ == "__main__":
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dall = main("allnodes", title_allnodes, "1e")
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dbrt = main("allnodes", title_allnodes, "brute")
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