create unit cube charts

test/unitcube/results/create_unitcube_chart.py

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