128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
from matplotlib.ticker import FuncFormatter
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.lines import Line2D
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from enum import Enum
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class columns ():
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benchmark_type = 'benchmark_type'
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proc_num = 'proc_num'
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msg_size_bytes = 'msg_size_bytes'
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repetitions = 'repetitions'
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t_min_usec = 't_min_usec'
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t_max_usec = 't_max_usec'
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t_avg_usec = 't_avg_usec'
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mpi_datatype = 'mpi_datatype'
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mpi_red_datatype = 'mpi_red_datatype'
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mpi_red_op = 'mpi_red_op'
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creation_time = 'creation_time'
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n_nodes = 'n_nodes'
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off_cache_flag = 'off_cache_flag'
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algorithm = 'algorithm'
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class collectives(Enum):
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Bcast = 'Bcast'
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Reduce = 'Reduce'
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Allreduce = 'Allreduce'
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Alltoall = 'Alltoall'
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Scatter = 'Scatter'
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Reduce_scatter = 'Reduce_scatter'
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Allgather = 'Allgather'
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Gather = 'Gather'
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def log_notation(val, pos):
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return f" $1e{int(val)}$" if val != 0 else "1"
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def log2_notation(val, pos):
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return "$2^{"+str(int(val))+"}$" if val != 0 else "1"
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data_file = "./data/data_04_11_25_algs.csv"
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df_multinode = pd.read_csv(data_file, delimiter=',')
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df_multinode.fillna(0, inplace=True)
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df_multinode = df_multinode[df_multinode[columns.off_cache_flag] == 50]
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for c in collectives:
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df_single = df_multinode[df_multinode[columns.benchmark_type]
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== c.value]
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df_single = df_single[df_single[columns.msg_size_bytes] > 1000]
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df_single = df_single[[columns.proc_num, columns.msg_size_bytes,
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columns.t_avg_usec, columns.algorithm]]
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df_gather_best = df_single.loc[ # pyright: ignore
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df_single.groupby([columns.msg_size_bytes, columns.proc_num])[ # pyright: ignore
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columns.t_avg_usec].idxmin()
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].reset_index(drop=True)
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df_gather_worst = df_single.loc[ # pyright: ignore
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df_single.groupby([columns.msg_size_bytes, columns.proc_num])[ # pyright: ignore
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columns.t_avg_usec].idxmax()
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].reset_index(drop=True)
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# df_gather_select = df_gather_select[df_gather_select[columns.msg_size_bytes] > 2**17]
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pivot_best = df_gather_best.pivot(index=columns.msg_size_bytes, # pyright: ignore
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columns=columns.proc_num, values=columns.t_avg_usec)
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pivot_worst = df_gather_worst.pivot(index=columns.msg_size_bytes, # pyright: ignore
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columns=columns.proc_num, values=columns.t_avg_usec)
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X = pivot_best.columns.values # proc_num
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Y = pivot_best.index.values # msg_size_bytes
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X, Y = np.meshgrid(X, Y) # pyright: ignore
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Z = pivot_best.values
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X_w = pivot_worst.columns.values # proc_num
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Y_w = pivot_worst.index.values # msg_size_bytes
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X_w, Y_w = np.meshgrid(X_w, Y_w) # pyright: ignore
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Z_w = pivot_worst.values
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alg_pivot = df_gather_best.pivot(
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index=columns.msg_size_bytes,
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columns=columns.proc_num,
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values=columns.algorithm
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)
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algorithms = alg_pivot.values.flatten()
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unique_algs = sorted(pd.unique(algorithms[~pd.isna(algorithms)]))
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color_map = {alg: i for i, alg in enumerate(unique_algs)}
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color_values = np.array([color_map.get(a, np.nan) for a in algorithms])
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fig = plt.figure(figsize=(16, 9))
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ax = fig.add_subplot(111, projection='3d')
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surf = ax.plot_wireframe(X, np.log2(Y), np.log(Z),
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color='black', linewidths=1)
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surf = ax.plot_wireframe(X_w, np.log2(
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Y_w), np.log(Z_w), color='gray', linewidths=0.3)
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surf_points = ax.scatter(X, np.log2(
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Y), np.log(Z), c=color_values, cmap='viridis', s=20, depthshade=False) # pyright: ignore
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surf_points = ax.scatter(X_w, np.log2(
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Y_w), np.log(Z_w), c='gray', alpha=0.2, s=20, depthshade=False) # pyright: ignore
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handles = [
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Line2D([0], [0],
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marker='o', color='w',
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label=alg,
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markerfacecolor=plt.cm.viridis(
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color_map[alg] / max(len(unique_algs)-1, 1)),
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markersize=8)
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for alg in unique_algs
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]
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ax.legend(handles=handles, title="Algorithm", loc='upper right')
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ax.set_xlabel("Process Count")
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ax.set_ylabel("Message Size [B] (log2)")
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ax.set_zlabel("Average Time [μs] (log10)")
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ax.set_title(f"{c.value}")
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ax.set_xticks(pivot_best.columns.values) # pyright: ignore
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ax.set_xticklabels(pivot_best.columns.values)
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ax.yaxis.set_major_formatter(FuncFormatter(log2_notation))
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ax.zaxis.set_major_formatter(FuncFormatter(log_notation))
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plt.savefig(f"./plots/{c.value.lower()}_algcomp_log.png")
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