run_dfo_compare.py

string = "took {}s to run population size of {} for {} iterations"
population_size = 500
iteration_count = 100
solution_size = 64 * 64


# Naive DFO
with tf.device("/cpu:0"):
    start = time.time()
    naive_dfo(solution_size=solution_size,
              population_size=population_size, 
              iteration_count=iteration_count)
    end = time.time()
    time_taken = end - start
    print(" naive CPU: " + string.format(time_taken, population_size, iteration_count))

    
# CPU Vectorised DFO
tf.reset_default_graph()
with tf.device("/cpu:0"):
    start = time.time()
    vectorised_dfo(solution_size=solution_size,
                   population_size=population_size, 
                   iteration_count=iteration_count, 
                   print_results=False)
    end = time.time()
    time_taken = end - start
    print("vector CPU: " + string.format(time_taken, population_size, iteration_count))

    
# GPU Vectorised DFO
tf.reset_default_graph()
with tf.device("/gpu:0"):
    start = time.time()
    vectorised_dfo(solution_size=solution_size,
                   population_size=population_size, 
                   iteration_count=iteration_count, 
                   print_results=False)
    end = time.time()
    time_taken = end - start
    print("vector GPU: " + string.format(time_taken, population_size, iteration_count))

    
#---------------------------------------
#
#                Output
#
#---------------------------------------
#
#  naive CPU: took 321.1472821235657s to run population size of 500 for 100 iterations
# vector CPU: took 5.197434663772583s to run population size of 500 for 100 iterations
# vector GPU: took 0.4568960666656494s to run population size of 500 for 100 iterations