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100 lines
4.1 KiB
Python
100 lines
4.1 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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def p(data, width=14):
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print(list(data))
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with pd.option_context('display.max_colwidth', width, 'expand_frame_repr', False):
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mw = pd.get_option('display.max_colwidth')
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print(data.rename(columns=lambda x: x[:mw-3] + '...' if len(x) > mw else x))
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filename = 'unitcube128.output'
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filename = 'unitcube128.output.NEW'
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f = pd.read_csv(filename, sep=';')
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swap_di = {8:'isend/irecv', 16:'persistent', 32:'alltoallv', 64:'ialltoallv'}
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f['swap_mode'].replace(swap_di, inplace=True)
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q = f[['num_iterations','swap_mode','ave_halo_t_pi']]
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ni = 100
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q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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q_persistent = q[(q.swap_mode=='persistent') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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q_alltoallv = q[(q.swap_mode=='alltoallv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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q_ialltoallv = q[(q.swap_mode=='ialltoallv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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w1 = q[(q.swap_mode=='isend/irecv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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w2 = q[(q.swap_mode=='persistent') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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w3 = q[(q.swap_mode=='alltoallv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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w4 = q[(q.swap_mode=='ialltoallv') & (q.num_iterations > ni)].groupby('num_iterations', as_index=False).mean()
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isend_irecv_marker = 'gs-'
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persistent_col_marker = 'mo-'
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alltoallv_marker = 'b+-'
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ialltoallv_marker = 'rx-'
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isend_irecv_label = 'Isend/Irecv'
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persistent_col_label = 'Persistent Neighbor_alltoallv'
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alltoallv_label = 'Neighbor_alltoallv'
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ialltoallv_label = 'Ineighbor_alltoallv'
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x_axis_label = 'Number of Halo Communications'
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y_axis_label = 'Time per iteration (microseconds)'
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ave_halo_title = "Average Halo Time per Communication (200-3000)"
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ave_halo_title_100 = "Average Halo Time per Communication (1-100)"
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# y_axis_min = 700
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# y_axis_max =
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# the whole scale
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plt.figure()
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plt.plot(q_isend_irecv.num_iterations, q_isend_irecv.ave_halo_t_pi, isend_irecv_marker, label=isend_irecv_label)
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plt.plot(q_persistent.num_iterations, q_persistent.ave_halo_t_pi, persistent_col_marker, label=persistent_col_label)
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plt.plot(q_alltoallv.num_iterations, q_alltoallv.ave_halo_t_pi, alltoallv_marker, label=alltoallv_label)
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plt.plot(q_ialltoallv.num_iterations, q_ialltoallv.ave_halo_t_pi, ialltoallv_marker, label=ialltoallv_label)
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plt.legend()
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plt.title(ave_halo_title)
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plt.xlabel(x_axis_label)
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plt.ylabel(y_axis_label)
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plt.axis(ymax=200)
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# plt.axis(ymin=y_axis_min, ymax=y_axis_max)
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# iterations below
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ni = 101
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q_isend_irecv = q[(q.swap_mode=='isend/irecv') & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
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q_persistent = q[(q.swap_mode=='persistent') & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
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q_alltoallv = q[(q.swap_mode=='alltoallv') & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
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q_ialltoallv = q[(q.swap_mode=='ialltoallv') & (q.num_iterations < ni)].groupby('num_iterations', as_index=False).mean()
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plt.figure()
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plt.plot(q_isend_irecv.num_iterations, q_isend_irecv.ave_halo_t_pi, isend_irecv_marker, label=isend_irecv_label)
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plt.plot(q_persistent.num_iterations, q_persistent.ave_halo_t_pi, persistent_col_marker, label=persistent_col_label)
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plt.plot(q_alltoallv.num_iterations, q_alltoallv.ave_halo_t_pi, alltoallv_marker, label=alltoallv_label)
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plt.plot(q_ialltoallv.num_iterations, q_ialltoallv.ave_halo_t_pi, ialltoallv_marker, label=ialltoallv_label)
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plt.legend()
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plt.title(ave_halo_title_100)
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plt.xlabel(x_axis_label)
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plt.ylabel(y_axis_label)
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# plt.axis(ymin=y_axis_min)
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plt.show()
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# w = f[f.np==16]
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# q = f.loc[f.np==16, 'total_time':'ave_request_create_t']
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# w = f.loc[f.np==16, 'ave_alltoall_comm_t':'ave_request_create_t']
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e = f.loc[f.np==16, 'num_iterations':'ave_halo_t_pi']
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e2 = f.loc[f.np==16, 'ave_neighbors':'min_rcv']
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# p(w)
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p(e,10)
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p(e2,6)
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