70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from scipy.optimize import curve_fit
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import matplotlib.cm as cm
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def max_transfer_size(msg_size, np_procs, benchmark_type):
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if benchmark_type == 'Allgather':
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return (np_procs-72)*msg_size
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elif benchmark_type == 'Scatter':
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return (np_procs-72)*msg_size # ?
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elif benchmark_type == 'Alltoall':
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return 72*(np_procs-72)*msg_size
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elif benchmark_type == 'Bcast':
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return msg_size
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elif benchmark_type == 'Gather':
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return (np_procs)*msg_size # ?
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elif benchmark_type == 'Reduce_scatter':
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return 0.25*(np_procs-72)*(1/72)*msg_size # ?
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elif benchmark_type == 'Allreduce':
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return 0.25*(np_procs-72)*(1/72)*msg_size
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elif benchmark_type == 'Reduce':
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return 0.25*(np_procs-72)*(1/72)*msg_size
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data_file = "data/data-multi-defand100cflag.csv"
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df_multinode = pd.read_csv(data_file, delimiter=',')
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df_multinode_offdef = df_multinode[df_multinode['off_cache_flag'] == 100]
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benchmarks = df_multinode_offdef['benchmark_type'].unique().tolist()
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benchmarks = [x for x in benchmarks if x[-1] != 'v']
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print(benchmarks)
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df_multinode_offdef = df_multinode_offdef[df_multinode_offdef['benchmark_type'].isin(
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benchmarks)][df_multinode_offdef['msg_size_bytes'] > 1000]
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df_multinode_offdef["max_transfer"] = df_multinode_offdef.apply(
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lambda row: max_transfer_size(
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msg_size=row["msg_size_bytes"],
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np_procs=row["proc_num"],
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benchmark_type=row["benchmark_type"]
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),
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axis=1
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)
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df_multinode_offdef["bytes/usec"] = df_multinode_offdef["max_transfer"] / \
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df_multinode_offdef["t_avg_usec"]
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df_multinode_offdef = df_multinode_offdef[df_multinode_offdef['benchmark_type']!='Allgather'][df_multinode_offdef['benchmark_type']!='Alltoall']
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df_multinode_offdef = df_multinode_offdef[['benchmark_type','msg_size_bytes','t_avg_usec','proc_num']]
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plt.figure(figsize=(16, 9))
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sns.barplot(
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data=df_multinode_offdef,
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x="benchmark_type",
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y="t_avg_usec",
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dodge=True,
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hue=df_multinode_offdef["msg_size_bytes"].astype(str),
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)
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# plt.yscale("log")
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plt.title("Average Time (usec) per Benchmark Type and Message Size")
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plt.ylabel("Average Time (usec)")
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plt.xlabel("Benchmark Type")
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plt.xticks(rotation=45)
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plt.legend(title="Message Size (bytes)")
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plt.tight_layout()
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# plt.show()
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plt.savefig("./plots/benchmark_avg_time_barplot.png", dpi=300)
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plt.close()
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