increase resolution

numerical_examples/cost/plot_cost_comparison.py

@@ -15,7 +15,7 @@
 print("Found ", len(file_timings), " file(s).")
 timings = []
 for filename in file_timings:
-    timings.append(np.load(filename))
+    timings.append(np.load(filename, allow_pickle=True))
 T = timings[0]
 array_size = T.shape[0]
 
@@ -34,7 +34,7 @@
 ylabel('Runtime per gradient [s]', fontsize=10)
 
 tight_layout()
-savefig('figure_1a.png', dpi=300, format='png')
+savefig('figure_11a.png', dpi=600, format='png')
 
 ####################################################################################################
 
@@ -97,7 +97,7 @@ def model_idle_time(timings, num_nodes, sort=False):
 legend(['EC2 cluster', 'AWS Batch'], fontsize=9)
 ax1.set_xlim([1, array_size])
 fig.tight_layout()  # otherwise the right y-label is slightly clipped
-savefig('figure_1b.png', dpi=300, format='png')
+savefig('figure_11b.png', dpi=600, format='png')
 
 
 # Plot runtime as function of the number of cluster nodes
@@ -109,6 +109,5 @@ def model_idle_time(timings, num_nodes, sort=False):
 xlabel('No. of instances', fontsize=10)
 ylabel('Time-to-solution [s]', fontsize=10)
 tight_layout()
-savefig('figure_1c.png', dpi=300, format='png')
-
+savefig('figure_11c.png', dpi=600, format='png')
 show()

numerical_examples/resilience/plot_resilience_modeled.py

@@ -67,15 +67,15 @@ def model_runtimes(container_times, schedule, restart=True, restart_time=120):
 ####################################################################################################
 
 # Model resilience for saving in memory or w/ opt. checkpointing
-case = 'memory'
-#case = 'checkpointing'
+#case = 'memory'
+case = 'checkpointing'
 
 # Load timings w/o interruptions
 path = os.getcwd()
 if case == 'memory':
-    T = np.load(path + '/timings_memory.dat')
+    T = np.load(path + '/timings_memory.dat', allow_pickle=True)
 else:
-    T = np.load(path + '/timings_checkpointing.dat')
+    T = np.load(path + '/timings_checkpointing.dat', allow_pickle=True)
 batchsize = T.shape[0]
 
 container_times = (T[:,2] - T[:,1])/1e3
@@ -129,6 +129,6 @@ def model_runtimes(container_times, schedule, restart=True, restart_time=120):
 plt.tight_layout()
 
 if case == 'memory':
-    savefig('resilience_save_in_memory.png', dpi=300, format='png')
+    savefig('figure_12a.png', dpi=600, format='png')
 else:
-    savefig('resilience_save_in_checkpointing_2.png', dpi=300, format='png')
+    savefig('figure_12b.png', dpi=600, format='png')

numerical_examples/strong_scaling/plot_omp_timings.py

@@ -75,13 +75,13 @@
 timings_optimum = []
 
 for filename in file_timings_default:
-    timings_default.append(np.load(path_default + filename))
+    timings_default.append(np.load(path_default + filename, allow_pickle=True))
 for filename in file_timings_no_ht:
-    timings_no_ht.append(np.load(path_no_ht + filename))
+    timings_no_ht.append(np.load(path_no_ht + filename, allow_pickle=True))
 for filename in file_timings_ec2:
-    timings_ec2.append(np.load(path_ec2 + filename))
+    timings_ec2.append(np.load(path_ec2 + filename, allow_pickle=True))
 for filename in file_timings_optimum:
-    timings_optimum.append(np.load(path_optimum + filename))
+    timings_optimum.append(np.load(path_optimum + filename, allow_pickle=True))
 
 # Timings
 # create=0; start=1; end=2; var=3;
@@ -194,7 +194,7 @@
 ax.tick_params(axis='x', labelsize=10)
 plt.legend(['Batch default', 'Batch no HT', 'EC2 metal', 'Optimum'], loc='lower left', fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_omp_speedup.png', dpi=300, format='png')
+savefig('strong_scaling_omp_speedup.png', dpi=600, format='png')
 
 # Kernel runtimes
 fig, ax = plt.subplots(figsize=(3.33, 3))
@@ -215,6 +215,6 @@
 plt.yticks(np.array([250, 500, 1000, 2000]), ('250', '500', '1000', '2000'), size=10)
 plt.legend(['Batch default', 'Batch no HT', 'EC2 metal', 'Optimum'], loc='upper right', fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_omp_times.png', dpi=300, format='png')
+savefig('strong_scaling_omp_times.png', dpi=600, format='png')
 
 plt.show()

numerical_examples/strong_scaling_mpi/plot_mpi_strong_scaling.py

@@ -52,13 +52,13 @@
 timings_c5n_metal = []
 
 for filename in file_timings_r5:
-    timings_r5.append(np.load(path_r5 + filename))    # timings: list of num_files entries: 1 x 6
+    timings_r5.append(np.load(path_r5 + filename, allow_pickle=True))    # timings: list of num_files entries: 1 x 6
 for filename in file_timings_r5_metal:
-    timings_r5_metal.append(np.load(path_r5_metal + filename))
+    timings_r5_metal.append(np.load(path_r5_metal + filename, allow_pickle=True))
 for filename in file_timings_c5n:
-    timings_c5n.append(np.load(path_c5n + filename))
+    timings_c5n.append(np.load(path_c5n + filename, allow_pickle=True))
 for filename in file_timings_c5n_metal:
-    timings_c5n_metal.append(np.load(path_c5n_metal + filename))
+    timings_c5n_metal.append(np.load(path_c5n_metal + filename, allow_pickle=True))
 
 # Timings
 # create=0; start=1; end=2; var=3; devito=4; script=5
@@ -160,7 +160,7 @@
 ax.set_ylim([0.15, 1.1])
 plt.legend(['r5.24xlarge', 'r5.metal', 'c5n.18xlarge', 'c5n.metal'], loc='lower left', fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_mpi_speedup_single_thread.png', dpi=300, format='png')
+savefig('strong_scaling_mpi_speedup_single_thread.png', dpi=600, format='png')
 
 # Timings plot
 fig, ax = plt.subplots(figsize=(3.33, 3))
@@ -179,7 +179,7 @@
 ax.tick_params(axis='x', labelsize=10)
 plt.legend(['r5.24xlarge', 'r5.metal','c5n.18xlarge', 'c5n.metal'], loc='upper right', fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_mpi_runtime_single_thread.png', dpi=300, format='png')
+savefig('strong_scaling_mpi_runtime_single_thread.png', dpi=600, format='png')
 
 # Breakdown of timings c5n
 fig, ax = plt.subplots(figsize=(3.33, 3))
@@ -199,7 +199,7 @@
 ax.set_ylim([30, 1400])
 plt.legend(['Job', 'Container', 'Python', 'Kernel'], fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_breakdown_c5n.png', dpi=300, format='png')
+savefig('strong_scaling_breakdown_c5n.png', dpi=600, format='png')
 
 # Cost r5 vs c5n
 r5_on_demand = 6.048/60/60
@@ -236,6 +236,6 @@
 ax.tick_params(axis='x', labelsize=10)
 plt.legend(['r5 on-demand', 'r5 spot', 'c5n on-demand', 'c5n spot'], fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_cost_single_thread.png', dpi=300, format='png')
+savefig('strong_scaling_cost_single_thread.png', dpi=600, format='png')
 
 plt.show()

numerical_examples/strong_scaling_mpi/plot_mpi_strong_scaling_var.py

@@ -36,10 +36,10 @@
 timings_c5n = []
 
 for filename in file_timings_r5:
-    timings_r5.append(np.load(path_r5 + filename))    # timings: list of num_files entries: 1 x 6
+    timings_r5.append(np.load(path_r5 + filename, allow_pickle=True))    # timings: list of num_files entries: 1 x 6
 
 for filename in file_timings_c5n:
-    timings_c5n.append(np.load(path_c5n + filename))
+    timings_c5n.append(np.load(path_c5n + filename, allow_pickle=True))
 
 # Timings
 # create=0; start=1; end=2; var=3; devito=4; script=5
@@ -106,7 +106,7 @@ def autolabel(rects, labels, scale):
 autolabel(bar2, labels_r5, 7)
 
 plt.tight_layout()
-savefig('strong_scaling_runtime_max_threads.png', dpi=300, format='png')
+savefig('strong_scaling_runtime_max_threads.png', dpi=600, format='png')
 
 # Cost r5 vs c5n
 # Cost plot (N. Virigina, May 13, 2019, 10:04 PM)
@@ -145,6 +145,6 @@ def autolabel(rects, labels, scale):
 ax.set_ylim([0, .5])
 plt.legend(['r5 on-demand', 'r5 spot', 'c5n on-demand', 'c5n spot', ], fontsize=9)
 plt.tight_layout()
-savefig('strong_scaling_cost_max_thread.png', dpi=300, format='png')
+savefig('strong_scaling_cost_max_thread.png', dpi=600, format='png')
 
 plt.show()

numerical_examples/weak_scaling/plot_weak_scaling_results.py

@@ -25,7 +25,7 @@
 print("Found ", len(file_timings), " file(s).")
 timings = []
 for filename in file_timings:
-    timings.append(np.load(path + filename))
+    timings.append(np.load(path + filename, allow_pickle=True))
 
 # Timings
 # create=0; start=1; end=2; var=3;
@@ -76,7 +76,7 @@ def confidence_interval(x, z=1.645):
 ax.set_ylim([0, 1000])
 plt.legend(['Full job', 'Container', 'Reduction'], fontsize=9)
 plt.tight_layout()
-savefig('weak_scaling_gradients.png', dpi=300, format='png')
+savefig('weak_scaling_gradients.png', dpi=600, format='png')
 
 # Average startup time
 startup_times = job_time - container_time - reduction_time
@@ -88,7 +88,7 @@ def confidence_interval(x, z=1.645):
 ax.tick_params(axis='y', labelsize=10)
 ax.tick_params(axis='x', labelsize=10)
 plt.tight_layout()
-savefig('job_runtime_first.png', dpi=300, format='png')
+savefig('job_runtime_first.png', dpi=600, format='png')
 
 # Average container runtime and cost
 fig, ax1 = plt.subplots(figsize=(3.66, 3))
@@ -109,7 +109,7 @@ def confidence_interval(x, z=1.645):
 fig.tight_layout()  # otherwise the right y-label is slightly clipped
 axx2 = gca()
 ax2.set_ylim([0, axx2.get_ylim()[1]*1.15])
-savefig('container_runtime_cost.png', dpi=300, format='png')
+savefig('container_runtime_cost.png', dpi=600, format='png')
 
 # Plot additional reduction time
 fig, ax = plt.subplots(figsize=(3.33, 3))
@@ -120,6 +120,6 @@ def confidence_interval(x, z=1.645):
 ax.tick_params(axis='y', labelsize=10)
 ax.tick_params(axis='x', labelsize=10)
 plt.tight_layout()
-savefig('reduction_runtime_mean.png', dpi=300, format='png')
+savefig('reduction_runtime_mean.png', dpi=600, format='png')
 
 plt.show()