did scaling plot with astro2 data

tests/population/scaling/plot_scaling_new.py

@@ -8,9 +8,16 @@ import math
 
 scaling_result_dir = 'scaling_results'
 
+filenames = [
+    'astro2_2500_systems.json',
+    'astro2_3000_systems.json',
+]
+
 result_jsons = []
-result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_100_systems.json'))
-result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_2500_systems.json'))
+for filename in filenames:
+    result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), filename))
+# result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_100_systems.json'))
+# result_jsons.append(os.path.join(os.path.abspath(scaling_result_dir), 'david-Lenovo-IdeaPad-S340-14IWL_2500_systems.json'))
 
 fig, ax1 = plt.subplots()
 ax2 = ax1.twinx()
@@ -68,16 +75,19 @@ ax1.set_title(
     )
 )
 
-ax1.plot([0, max(cpus)], [0, max(cpus)], label='100% scaling')
+# ax1.plot([1, max(cpus)], [1, max(cpus)], label='100% scaling')
 
 ax1.set_xlabel("Amount of cores used")
 ax1.set_ylabel("Speed up ratio (time_linear/time_parallel)")
 
-ax1.set_xlim(0, max(cpus) + 4)
+# ax1.set_xlim(0, max(cpus) + 4)
 # ax2.set_ylim(0, 1)
 
 ax1.grid()
 ax1.legend(loc=4)
+ax1.set_xscale('log')
+ax2.set_xscale('log')
+
 # fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "png")))
 # fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "pdf")))
 # fig.savefig(os.path.join(img_dir, "speedup_scaling_{}.{}".format(name_testcase, "eps")))

tests/population/scaling/scaling_script.py

@@ -32,7 +32,7 @@ for i in range(1, int(amount_of_cpus/stepsize) + 1 ):
 
 # set some info
 amt_repeats = 5
-resolution = {'M_1': 5, 'per': 5}
+resolution = {'M_1': 50, 'per': 60}
 total_systems = int(np.prod([el for el in resolution.values()]))
 result_dir = 'scaling_results'
 testcase = 'linear vs MP batched'
@@ -98,27 +98,27 @@ for repeat in range(amt_repeats):
 
 result_dict['linear'] = linear_times
 
-# #######################################################################################
-# # MP runs
-# mp_dict = {}
-# for cpu_amt in cpu_list:
+#######################################################################################
+# MP runs
+mp_dict = {}
+for cpu_amt in cpu_list:
 
-#     mp_times = []
+    mp_times = []
 
-#     test_pop.set(amt_cores=cpu_amt)
+    test_pop.set(amt_cores=cpu_amt)
 
-#     # 
-#     for repeat in range(amt_repeats):
-#         total_mp_start = time.time()
-#         evolve_mp_time = test_pop.evolve_population_mp_chunks()
-#         total_mp = time.time() - total_mp_start
+    # 
+    for repeat in range(amt_repeats):
+        total_mp_start = time.time()
+        evolve_mp_time = test_pop.evolve_population_mp_chunks()
+        total_mp = time.time() - total_mp_start
 
-#         print("MP ({} nodes) run with {} systems: {} of which {} spent on evolving the systems".format(cpu_amt, total_systems, total_mp, evolve_mp_time))
-#         mp_times.append(total_mp)
+        print("MP ({} nodes) run with {} systems: {} of which {} spent on evolving the systems".format(cpu_amt, total_systems, total_mp, evolve_mp_time))
+        mp_times.append(total_mp)
 
-#     mp_dict[cpu_amt] = mp_times
+    mp_dict[cpu_amt] = mp_times
 
-# result_dict['mp'] = mp_dict
+result_dict['mp'] = mp_dict
 
 # Write to file
 with open('scaling_results/{}_{}_systems.json'.format(hostname, total_systems), 'w') as f: