Parallelize Simulations

engine/multiproc.py

@@ -0,0 +1,11 @@
+from pathos.multiprocessing import ProcessingPool as Pool
+
+
+def parallelize_simulations(f, states_list, configs, env_processes, T, N):
+    def process(config):
+        return f(states_list, config, env_processes, T, N)
+
+    with Pool(len(configs)) as p:
+        results = p.map(process, configs)
+
+    return results

engine/run.py

@@ -1,24 +1,29 @@
-from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+import pandas as pd
+from tabulate import tabulate
+
 from engine.configProcessor import generate_config, create_tensor_field
 from engine.mechanismExecutor import simulation
 from engine.utils import flatten
-from tabulate import tabulate
+from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
-import pandas as pd
+from engine.multiproc import parallelize_simulations
+
 
 def main():
     states_list = [state_dict]
     ep = list(exogenous_states.values())
-    configs = generate_config(state_dict, mechanisms, ep)
+    config = generate_config(state_dict, mechanisms, ep)
-    # print(len(configs))
-    print(tabulate(create_tensor_field(mechanisms, ep), headers='keys', tablefmt='psql'))
-    print
-    # p = pipeline(states_list, configs, env_processes, range(10))
     T = sim_config['T']
     N = sim_config['N']
+    configs = [config, config]
 
     # Dimensions: N x r x mechs
-    s = simulation(states_list, configs, env_processes, T, N)
 
-    result = pd.DataFrame(flatten(s))
+    if len(configs) > 1:
+     simulations = parallelize_simulations(simulation, states_list, configs, env_processes, T, N)
+    else:
+     simulations = [simulation(states_list, configs[0], env_processes, T, N)]
 
-    return result
+    for result in simulations:
+        print(tabulate(create_tensor_field(mechanisms, ep), headers='keys', tablefmt='psql'))
+        print
+        print(tabulate(pd.DataFrame(flatten(result)), headers='keys', tablefmt='psql'))