4 changed files with 113 additions and 3243 deletions
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722benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton11-1gib-allnodes-cpu.json
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1262benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton12-1gib-allnodes-cpu.json
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1262benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton15-1gib-allnodes-cpu.json
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110benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py
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benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton12-1gib-allnodes-cpu.json
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benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton15-1gib-allnodes-cpu.json
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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): |
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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-1e.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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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) |
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dsmart = main("smart", title_smartnodes) |
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