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158 lines
5.2 KiB
158 lines
5.2 KiB
import os
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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, load_time_mesurements, get_task_count
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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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data_avg = {}
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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 get_timing(file_path):
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divisor = get_task_count(file_path)
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return [ x / divisor for x in load_time_mesurements(file_path)]
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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, config, dst_node):
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size = 1024*1024*1024
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if config not in data_avg:
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data_avg[config] = 0
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timing = get_timing(file_path)
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run_idx = 0
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for t in timing:
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tp = calc_throughput(size, t)
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data_avg[config] += tp / len(timing)
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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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def plot_bar(table,node_config,display_x,display_y):
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plt.figure(figsize=(2, 3))
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sns.barplot(x=x_label, y=y_label, data=table, palette="mako", errorbar="sd")
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plt.ylim(0, 70)
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plt.yticks([15,30,45,60,65])
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plt.xlabel(display_x)
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plt.ylabel(display_y)
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plt.savefig(os.path.join(output_path, f"plot-{node_config}-throughput.pdf"), bbox_inches='tight')
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def PlotAndrePeakResults():
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data_peakbench_andre = [
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{ runid : 0, x_label : 8, y_label : 64 },
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{ runid : 0, x_label : 11, y_label : 63 },
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{ runid : 0, x_label : 12, y_label : 40 },
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{ runid : 0, x_label : 15, y_label : 54 }
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]
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df = pd.DataFrame(data_peakbench_andre)
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df.set_index(index, inplace=True)
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plot_bar(df, "andrepeak", x_label, y_label)
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return df
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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):
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dst_nodes = {8,11,12,15}
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for dst_node in dst_nodes:
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file = os.path.join(result_path, f"copy-n0ton{dst_node}-1gib-{node_config}.json")
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process_file_to_dataset(file, node_config, dst_node)
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data_avg[node_config] = data_avg[node_config] / len(dst_nodes)
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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, node_config, x_label, y_label)
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return df
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def get_scaling_factor(baseline,topline,utilfactor):
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return (topline / baseline) * (1 / utilfactor)
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if __name__ == "__main__":
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dsa_df1 = main("1dsa")
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dsa_df2 = main("2dsa")
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dsa_df4 = main("4dsa")
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dsa_df8 = main("8dsa")
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cpu_df8 = main("8cpu")
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cpu_dfandre = PlotAndrePeakResults()
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x_dsacount = "Count of DSAs"
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y_avgtp = "Average Throughput in GiB/s"
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y_scaling = "Scaling Factor"
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data_average = [
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{ runid : 0, x_label : 1, y_label : data_avg["1dsa"] },
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{ runid : 0, x_label : 2, y_label : data_avg["2dsa"] },
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{ runid : 0, x_label : 4, y_label : data_avg["4dsa"] },
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{ runid : 0, x_label : 8, y_label : data_avg["8dsa"] }
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]
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average_df = pd.DataFrame(data_average)
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average_df.set_index(index, inplace=True)
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plot_bar(average_df, "average", x_dsacount, y_avgtp)
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data_scaling = [
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{ x_dsacount : 1, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["1dsa"], 1) },
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{ x_dsacount : 2, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["2dsa"], 2) },
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{ x_dsacount : 4, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["4dsa"], 4) },
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{ x_dsacount : 8, y_scaling : get_scaling_factor(data_avg["1dsa"], data_avg["8dsa"], 8) }
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]
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scaling_df = pd.DataFrame(data_scaling)
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plt.figure(figsize=(2, 3))
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fig = sns.lineplot(x=x_dsacount, y=y_scaling, data=scaling_df, marker='o', linestyle='-', color='b', markersize=8)
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plt.xticks([1,2,4,8])
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plt.yticks([0.25,0.5,0.75,1.0])
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plt.xlim(0,9)
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plt.ylim(0.2,1.05)
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plt.savefig(os.path.join(output_path, f"plot-dsa-throughput-scaling.pdf"), bbox_inches='tight')
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