better graphs, better organization

test/halo/artless-final-results/create_chart.py

@@ -1,11 +1,77 @@
 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))
+
+
+# def pq(data):
+
 
 filename = 'cz308.output'
+filename = 'withmpi-cz40948.output'
+filename = 'tokam_id_16p.output'
 
 f = pd.read_csv(filename, sep=';')
 
-swap_di = {16:'persistant' , 32: 'nonpersistant'}
+swap_di = {8:'psblas', 16:'persistent' , 32: 'nonpersistent'}
+f['swap_mode'].replace(swap_di, inplace=True)
+
+
+q = f[['num_iterations','swap_mode','ave_halo_t_pi']]
+q_nonb_p2p = q[q.swap_mode=='psblas']
+q_per_nonb_col = q[q.swap_mode=='persistent']
+q_nonper_nonb_col = q[q.swap_mode=='nonpersistent']
+
+nonb_p2p_marker        = 'g+-'
+per_nonb_col_marker    = 'rs-'
+nonper_nonb_col_marker = 'bx-'
+
+x_axis_label = 'Number of Halo Communications'
+y_axis_label = 'Time per iteration (microseconds)'
+ave_halo_title     = "Average Halo Time per Communication (1-3000)"
+ave_halo_title_100 = "Average Halo Time per Communication (1-100)"
+
+
+# the whole scale
+plt.figure()
+plt.plot(q_nonb_p2p.num_iterations, q_nonb_p2p.ave_halo_t_pi, nonb_p2p_marker, label='Non-blocking Point-to-Point')
+plt.plot(q_per_nonb_col.num_iterations, q_per_nonb_col.ave_halo_t_pi, per_nonb_col_marker, label='Persistent Non-blocking Collective')
+plt.plot(q_nonper_nonb_col.num_iterations, q_nonper_nonb_col.ave_halo_t_pi, nonper_nonb_col_marker, label='Non-persistent Non-Blocking Collective')
+plt.legend()
+plt.title(ave_halo_title)
+plt.xlabel(x_axis_label)
+plt.ylabel(y_axis_label)
+
+# iterations below
+ni = 101
+q_nonb_p2p = q[(q.swap_mode=='psblas') & (q.num_iterations < ni)]
+q_per_nonb_col = q[(q.swap_mode=='persistent') & (q.num_iterations < ni)]
+q_nonper_nonb_col = q[(q.swap_mode=='nonpersistent') & (q.num_iterations < ni)]
+plt.figure()
+plt.plot(q_nonb_p2p.num_iterations, q_nonb_p2p.ave_halo_t_pi, nonb_p2p_marker, label='Non-blocking Point-to-Point')
+plt.plot(q_per_nonb_col.num_iterations, q_per_nonb_col.ave_halo_t_pi, per_nonb_col_marker, label='Persistent Non-blocking Collective')
+plt.plot(q_nonper_nonb_col.num_iterations, q_nonper_nonb_col.ave_halo_t_pi, nonper_nonb_col_marker, label='Non-persistent Non-Blocking Collective')
+plt.legend()
+plt.title(ave_halo_title_100)
+plt.xlabel(x_axis_label)
+plt.ylabel(y_axis_label)
+
+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)
+
 
-q = f[['np','ave_time','swap_mode']]
-q['swap_mode'].replace(swap_di, inplace=True)