futureObjs.append(du.reduce_pilot("KMeans.averagePoints", number_of_cores_per_compute_unit=1))
for f in futureObjs:
result_du = f.result()
new_centers.append(result_du)
reduce_end_time = time.time()
print "wait for all reduce objects.................", str(round(reduce_end_time-map_end_time, 2))
total_reduce_time = total_reduce_time + (reduce_end_time-map_end_time)
du_centers = DistributedInMemoryDataUnit(name="Centers-%d"%(iteration+1), coordination=df).merge(new_centers)
iteration_end = time.time()
time_measures["Iteration-%d"%iteration] = iteration_end - iteration_start
end = time.time()
time_measures["Runtime"] = end-start
time_measures["average_map"] = total_map_time/NUM_ITERATIONS
time_measures["average_reduce"] = total_reduce_time/NUM_ITERATIONS
du_points.delete()
du_centers.delete()
#################################################################################################################
line = (str(r),"DIDU-KMeans", str(inputFiles[ex]), str(number_of_data_points), str(number_of_centroids_points), str(time_measures["Runtime"]),
str(m), str(time_measures["average_map"]), str(time_measures["average_reduce"]))
output_data.write(",".join(line) + "\n")
output_data.flush()
except:
e = sys.exc_info()[0]
output_data.close()
pilot_compute_service.cancel()
