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91 lines
3.4 KiB
91 lines
3.4 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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runid = "Run ID"
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x_label = "Thread Count"
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y_label = "Throughput in GiB/s"
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var_label = "Thread Counts"
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thread_counts = ["1t", "2t", "4t", "8t", "12t"]
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thread_counts_nice = ["1 Thread", "2 Threads", "4 Threads", "8 Threads", "12 Threads"]
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engine_counts = ["1mib-1e_PREVENT_FROM_DISPLAYING", "1mib-4e_PREVENT_FROM_DISPLAYING", "1gib-1e", "1gib-4e"]
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engine_counts_nice = ["1 E/WQ and Tasksize 1 MiB", "4 E/WQ and Tasksize 1 MiB", "1 E/WQ and Tasksize 1 GiB", "4 E/WQ and Tasksize 1 GiB"]
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title = "Total Throughput - 120 Copy Operations split on Threads Intra-Node on DDR"
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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_ns):
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time_seconds = time_ns * 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_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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iterations = data["list"][0]["task"]["iterations"]
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# work queue size is 120 which is split over all available threads
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# therefore we divide the result by 120/n_threads to get the per-element speed
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return {
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"total" : sum([x / (iterations * 120) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
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"combined" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
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"submission" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
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"completion" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
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}
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def process_file_to_dataset(file_path, engine_label, thread_count):
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engine_index = index_from_element(engine_label,engine_counts)
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engine_nice = engine_counts_nice[engine_index]
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threadc_index = index_from_element(thread_count, thread_counts)
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thread_count_nice = thread_counts_nice[threadc_index]
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data_size = 0
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if engine_label in ["1gib-1e", "1gib-4e"]: data_size = 1024*1024*1024
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elif engine_label in ["1mib-1e", "1mib-4e"]: data_size = 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)["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: thread_count_nice, var_label : engine_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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def main():
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folder_path = "benchmark-results/"
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for engine_label in engine_counts:
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for thread_count in thread_counts:
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file = os.path.join(folder_path, f"mtsubmit-{thread_count}-{engine_label}.json")
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process_file_to_dataset(file, engine_label, thread_count)
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df = pd.DataFrame(data)
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df.set_index(index, inplace=True)
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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-mtsubmit.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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