Question

我想使用同态（paillier）加密来加密300个数字的列表。这在我的笔记本上大约需要3000毫秒，在我的覆盆子pi上需要更长的时间。我想加快速度，所以我尝试使用多线程：

from multiprocessing.dummy import pool as ThreadPool

def test_1_performance_of_encryption(self):

    print("Test: Blind encryption performance test")
    print("-----------------------------")
    print()
    # Only blinds are encrypted
    for y in range(0, ((NUMBER_OF_RUNS//SENSOR_SAMPLES_PER_BLIND)*NUM_OF_DATA_PROCESSORS)):
        print("Round {}:".format(y+1))
        print("Encrypting {} blinds...".format(NUM_OF_SENSOR_SAMPLES))
        encrypted_blinds.clear()
        millis_start = int(round(time.time() * 1000))

        for x in range(0, NUM_OF_SENSOR_SAMPLES):
            encrypted_blinds.append(public_keys[0].encrypt(blinds[x]))

        millis_end = int(round(time.time() * 1000))
        time_elapsed_enc.append(millis_end - millis_start)
        print("Time elapsed: {}ms".format(time_elapsed_enc[y]))

    print("Test finished. Time elapsed:")
    print("Min: {} | Max: {} | Avg: {}".format(min(time_elapsed_enc), max(time_elapsed_enc),
                                               (sum(time_elapsed_enc)/len(time_elapsed_enc))))
    print()

@profile
def test_1a_performance_of_encryption_multithreaded(self):

    print("Test: Blind encryption performance test with {} threads".format(NUM_OF_THREADS))
    for y in range(0, ((NUMBER_OF_RUNS//SENSOR_SAMPLES_PER_BLIND)*NUM_OF_DATA_PROCESSORS)):
        print("Round {}:".format(y+1))
        print("Encrypting {} blinds...".format(len(blinds)))
        millis_start = int(round(time.time() * 1000))

        encrypted_blinds_multithreaded = pool.map(public_keys[0].encrypt, blinds)

        millis_end = int(round(time.time() * 1000))
        time_elapsed_enc_multithread.append(millis_end - millis_start)
        print("Time elapsed: {}ms".format(time_elapsed_enc_multithread[y]))

    print("Test finished. Time elapsed:")
    print("Min: {} | Max: {} | Avg: {}".format(min(time_elapsed_enc_multithread), max(time_elapsed_enc_multithread),
                                               (sum(time_elapsed_enc_multithread) / len(time_elapsed_enc_multithread))))
    print()

但是，这两项测试的完成时间或多或少完全相同。虽然单线程方法使用100％的一个核心，但多线程版本使用所有这些，但仍然会创建一个1的负载（等于1核心，100％）。我在这里做错了吗？我已经阅读了这个问题并得到了答案：Python multiprocessing.Pool() doesn't use 100% of each CPU，但是我不相信这里的原因是进程间通信，因为它完全以1的负载结算会非常奇怪......

编辑：我使用multiprocessing.dummy代替multiprocessing。我认为这使用了多个线程而不是进程，并且由于名为GIL（全局解释器锁）的东西，多个线程无法在Python中并行执行。我通过将multiprocessing.dummy更改为multiprocessing来修复此问题。我现在有n个进程和100％的CPU使用率。

使用multiprocessing.pool的Python多处理不使用100％CPU

0 个答案: