TensorFlow for MultiGPU

Question

如果有人能帮我理解这种情况，那就太好了。提前致谢。 我的设置： 操作系统：Ubuntu 16.04,2 Titan X GPU。 TensorFlow（版本0.12.1）使用pip在TF文档中安装在conda环境中。 Python 3.5。

代码： 我运行以下代码来测试我的2 GPU设置。每个人都有random_matrix = tf.zeros(...)和random_matrix = tf.random_uniform(...)。输出如下所示。

问题： 1）当我使用tf.zeros时。 CPU和GPU的时序相同。但是tf.random_uniform我发现GPU更快（正如我预期的那样）。为什么GPU tf.zeros会变慢？我错过了什么？ 2）我修复了全球种子和当地种子。为什么GPU内的输出与tf.random_uniform情况不同？

非常感谢您提前获得任何见解。

import sys
import numpy as np
import tensorflow as tf
from datetime import datetime

device_names = ["/cpu:0", "/gpu:0", "/gpu:1"]
shapes = [(3000, 3000), (6000, 6000), (9000, 9000), (12000, 12000)]

all_timings = []
tf.set_random_seed(1234)
for device_name in device_names:
    device_timings = []
    for shape in shapes:
        print("device_name:::::::::{}".format(device_name))
        with tf.device(device_name):
            # random_matrix = tf.zeros(shape)
            random_matrix = tf.random_uniform(shape=shape, 
                                              minval=0, 
                                              maxval=1, 
                                              seed=1234)
            result_op = tf.reduce_sum(tf.matmul(random_matrix,tf.transpose(random_matrix)))

        start_time = datetime.now()
        result = -1.0
        with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as session:
            result = session.run(result_op)
            time_diff = datetime.now() - start_time
            device_timings.append((device_name,
                                   shape,
                                   "time_taken (secs): {}".format(time_diff.total_seconds()),
                                   "result: {}".format(result)))
            print("++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\n")
all_timings.append(device_timings)

print("\n\n")
for device_timings in all_timings:
    for t in device_timings:
    print(t)
    print("---------------------------------------------------------\n\n")

使用tf.random_uniform（）进行计时：

('/cpu:0', (3000, 3000), 'time_taken (secs): 1.146831', 'result:     6754431488.0')
('/cpu:0', (6000, 6000), 'time_taken (secs): 2.816985', 'result: 54023852032.0')
('/cpu:0', (9000, 9000), 'time_taken (secs): 9.372665', 'result: 184425938944.0')
('/cpu:0', (12000, 12000), 'time_taken (secs): 21.718614', 'result: 439655661568.0')
--------------------------------------------------------


('/gpu:0', (3000, 3000), 'time_taken (secs): 0.39667', 'result: 6754406912.0')
('/gpu:0', (6000, 6000), 'time_taken (secs): 0.085984', 'result: 54006796288.0')
('/gpu:0', (9000, 9000), 'time_taken (secs): 0.221407', 'result: 182251880448.0')
('/gpu:0', (12000, 12000), 'time_taken (secs): 0.444187', 'result: 431996174336.0')
---------------------------------------------------------


('/gpu:1', (3000, 3000), 'time_taken (secs): 0.399159', 'result: 6754401792.0')
('/gpu:1', (6000, 6000), 'time_taken (secs): 0.102889', 'result: 54006857728.0')
('/gpu:1', (9000, 9000), 'time_taken (secs): 0.262842', 'result: 182251585536.0')
('/gpu:1', (12000, 12000), 'time_taken (secs): 0.469139', 'result: 431996141568.0')
---------------------------------------------------------

使用tf.zeros（）进行计时：

('/cpu:0', (3000, 3000), 'time_taken (secs): 1.040602', 'result: 0.0')
('/cpu:0', (6000, 6000), 'time_taken (secs): 2.760587', 'result: 0.0')
('/cpu:0', (9000, 9000), 'time_taken (secs): 9.134257', 'result: 0.0')
('/cpu:0', (12000, 12000), 'time_taken (secs): 21.410583', 'result: 0.0')
---------------------------------------------------------


('/gpu:0', (3000, 3000), 'time_taken (secs): 0.394707', 'result: 0.0')
(/gpu:0', (6000, 6000), 'time_taken (secs): 2.750311', 'result: 0.0')
('/gpu:0', (9000, 9000), 'time_taken (secs): 9.141721', 'result: 0.0')
('/gpu:0', (12000, 12000), 'time_taken (secs): 21.441183', 'result: 0.0')
 --------------------------------------------------------


('/gpu:1', (3000, 3000), 'time_taken (secs): 0.390197', 'result: 0.0')
('/gpu:1', (6000, 6000), 'time_taken (secs): 2.788815', 'result: 0.0')
('/gpu:1', (9000, 9000), 'time_taken (secs): 9.335516', 'result: 0.0')
('/gpu:1', (12000, 12000), 'time_taken (secs): 21.654866', 'result: 0.0')

Answer 1

我怀疑这与GPU内核优化有关。如果你预热＆＃34;你的GPU通过运行相同的计算形状，下一次执行要快得多。有一个PTX编译adds a couple of seconds在进程中GPU上第一次使用内核，但是你的运行时随着矩阵的大小而增加，或许还有一些特殊的正在进行的分析。

请注意，如果没有tf.OptimizerOptions.L0，它会变得难以置信地快速，因此也会发生一些缓存。

shape = (6000, 6000)
with tf.device("/gpu:0"):
    random_matrix_gpu = tf.zeros(shape)
    result_op_gpu = tf.reduce_sum(tf.matmul(random_matrix_gpu,tf.transpose(random_matrix_gpu)))
with tf.device("/cpu:0"):
    random_matrix_cpu = tf.zeros(shape)
    result_op_cpu = 

tf.reduce_sum(tf.matmul(random_matrix_cpu,tf.transpose(random_matrix_cpu)))
config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))
sess = tf.Session(config=config)

def profile(op, msg):
    start_time = time.time()
    sess.run(op)
    print(msg, time.time()-start_time)

profile(result_op_cpu, "cpu1")
profile(result_op_cpu, "cpu2")
profile(result_op_gpu, "gpu1")
profile(result_op_gpu, "gpu2")

我明白这一点：

cpu1 1.716048240661621
cpu2 1.509080171585083
gpu1 4.192790746688843
gpu2 0.13361549377441406

Answer 2

谢谢雅罗斯拉夫！我提供了我的运行代码和结果，以防其他人感兴趣。如果您尝试使用该代码，请耐心等待几分钟。

代码：

import sys
import numpy as np
import tensorflow as tf
from datetime import datetime


device_names = ["/cpu:0", "/gpu:0", "/gpu:1"]
shapes = [(3000, 3000), (6000, 6000), (9000, 9000), (12000, 12000)]
messages = ["RESULTS\n"]

def timing_run(matrix_type, config_name, warmup):
    configs = {"simple": tf.ConfigProto(log_device_placement=False),
               "optim": tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0)))}

    messages.append("matrix={}+config={}+warmup={}".format(matrix_type, config_name, warmup))
    tf.set_random_seed(1234)
    for device_name in device_names:
        for shape in shapes:
            with tf.device(device_name):
                if matrix_type == "random_uniform":
                    random_matrix = tf.random_uniform(shape=shape,
                                                      minval=0,
                                                      maxval=1,
                                                      seed=1234)
                else:
                    random_matrix = tf.zeros(shape)
                result_op = tf.reduce_sum(tf.matmul(random_matrix,tf.transpose(random_matrix)))

            session = tf.Session(config=configs[config_name])
            result1, result2 = -1.0, -1.0
            # warm up
            start_time1 = datetime.now()
            result1 = session.run(result_op)
            time_diff1 = datetime.now() - start_time1
            messages.append((device_name,
                             "shape = {}".format(shape),
                             "times = {} seconds".format(time_diff1.total_seconds()),
                             "result = {}".format(result1)))
            if warmup:
                # warmed up - runs if warmup=True.
                start_time2 = datetime.now()
                result2 = session.run(result_op)
                time_diff2 = datetime.now() - start_time2
                messages.append((device_name,
                                 "shape = {}".format(shape),
                                 "times = {} seconds".format(time_diff2.total_seconds()),
                                 "result = {}".format(result1),
                                 "*****WARMED UP*****"))
            session.close()
        messages.append("++++++++++++++++++++++++++++++++++++++++++++++++++++")
    messages.append("\n\n")



if __name__ == "__main__":
    timing_run(matrix_type="random_uniform", config_name="simple", warmup=False)
    timing_run(matrix_type="random_uniform", config_name="simple", warmup=True)
    timing_run(matrix_type="random_uniform", config_name="optim", warmup=False)
    timing_run(matrix_type="zeros", config_name="simple", warmup=False)
    timing_run(matrix_type="zeros", config_name="simple", warmup=True)
    timing_run(matrix_type="zeros", config_name="optim", warmup=False)

    # print timings
    for e in messages:
        print(e)

要点：

matrix=random_uniform+config=simple+warmup=False
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.428429 seconds', 'result = 6754431488.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.806464 seconds', 'result = 54023852032.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.36232 seconds', 'result = 184425938944.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 22.376751 seconds', 'result = 439655661568.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.392957 seconds', 'result = 6754390016.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.082889 seconds', 'result = 54006833152.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.221844 seconds', 'result = 182251814912.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 0.438476 seconds', 'result = 431995879424.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.483864 seconds', 'result = 6754393088.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.097571 seconds', 'result = 54006833152.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.250176 seconds', 'result = 182252044288.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.473314 seconds', 'result = 431996567552.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++



matrix=random_uniform+config=simple+warmup=True
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.475717 seconds', 'result = 6754431488.0')
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.379542 seconds', 'result = 6754431488.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.856803 seconds', 'result = 54023852032.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.798967 seconds', 'result = 54023852032.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.447787 seconds', 'result = 184425938944.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.385646 seconds', 'result = 184425938944.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.752967 seconds', 'result = 439655661568.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.832136 seconds', 'result = 439655661568.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.067066 seconds', 'result = 6754394624.0')
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.008072 seconds', 'result = 6754394624.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.123611 seconds', 'result = 54006833152.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.057391 seconds', 'result = 54006833152.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.248432 seconds', 'result = 182251913216.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.18535 seconds', 'result = 182251913216.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (12000, 12000)', 'times = 0.48081 seconds', 'result = 431996043264.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 0.412447 seconds', 'result = 431996043264.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.105071 seconds', 'result = 6754395648.0')
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.008107 seconds', 'result = 6754395648.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.137264 seconds', 'result = 54006849536.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.064462 seconds', 'result = 54006849536.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.280302 seconds', 'result = 182251831296.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.191399 seconds', 'result = 182251831296.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.509208 seconds', 'result = 431996534784.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.4263 seconds', 'result = 431996534784.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++



matrix=random_uniform+config=optim+warmup=False
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.552631 seconds', 'result = 6754431488.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.894024 seconds', 'result = 54023852032.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.394226 seconds', 'result = 184425938944.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.870817 seconds', 'result = 439655661568.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.107416 seconds', 'result = 6754392576.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.163633 seconds', 'result = 54006804480.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.304741 seconds', 'result = 182251667456.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 0.526494 seconds', 'result = 431995944960.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.119625 seconds', 'result = 6754394624.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.203158 seconds', 'result = 54006800384.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.317646 seconds', 'result = 182251978752.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.544184 seconds', 'result = 431996076032.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++



matrix=zeros+config=simple+warmup=False
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.632157 seconds', 'result = 0.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.901679 seconds', 'result = 0.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.345713 seconds', 'result = 0.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.707619 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.498451 seconds', 'result = 0.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 2.900121 seconds', 'result = 0.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 9.4296 seconds', 'result = 0.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 21.750406 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.523286 seconds', 'result = 0.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 2.887522 seconds', 'result = 0.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 9.377383 seconds', 'result = 0.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 21.639043 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++



matrix=zeros+config=simple+warmup=True
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.520212 seconds', 'result = 0.0')
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.000172 seconds', 'result = 0.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.914485 seconds', 'result = 0.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 0.000166 seconds', 'result = 0.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.346122 seconds', 'result = 0.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 0.000207 seconds', 'result = 0.0', '*****WARMED UP*****')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.715376 seconds', 'result = 0.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 0.0002 seconds', 'result = 0.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.556841 seconds', 'result = 0.0')
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.000234 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (6000, 6000)', 'times = 2.936608 seconds', 'result = 0.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.000244 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (9000, 9000)', 'times = 9.34956 seconds', 'result = 0.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.000246 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:0', 'shape = (12000, 12000)', 'times = 21.634354 seconds', 'result = 0.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 0.000221 seconds', 'result = 0.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.562244 seconds', 'result = 0.0')
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.000255 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (6000, 6000)', 'times = 2.961658 seconds', 'result = 0.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.000237 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (9000, 9000)', 'times = 9.308582 seconds', 'result = 0.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.000239 seconds', 'result = 0.0', '*****WARMED UP*****')
('/gpu:1', 'shape = (12000, 12000)', 'times = 21.707127 seconds', 'result = 0.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.000261 seconds', 'result = 0.0', '*****WARMED UP*****')
++++++++++++++++++++++++++++++++++++++++++++++++++++



matrix=zeros+config=optim+warmup=False
('/cpu:0', 'shape = (3000, 3000)', 'times = 0.560451 seconds', 'result = 0.0')
('/cpu:0', 'shape = (6000, 6000)', 'times = 2.978946 seconds', 'result = 0.0')
('/cpu:0', 'shape = (9000, 9000)', 'times = 9.3279 seconds', 'result = 0.0')
('/cpu:0', 'shape = (12000, 12000)', 'times = 21.694664 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:0', 'shape = (3000, 3000)', 'times = 0.249778 seconds', 'result = 0.0')
('/gpu:0', 'shape = (6000, 6000)', 'times = 0.365332 seconds', 'result = 0.0')
('/gpu:0', 'shape = (9000, 9000)', 'times = 0.663667 seconds', 'result = 0.0')
('/gpu:0', 'shape = (12000, 12000)', 'times = 1.032716 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++
('/gpu:1', 'shape = (3000, 3000)', 'times = 0.299856 seconds', 'result = 0.0')
('/gpu:1', 'shape = (6000, 6000)', 'times = 0.294592 seconds', 'result = 0.0')
('/gpu:1', 'shape = (9000, 9000)', 'times = 0.55067 seconds', 'result = 0.0')
('/gpu:1', 'shape = (12000, 12000)', 'times = 0.806868 seconds', 'result = 0.0')
++++++++++++++++++++++++++++++++++++++++++++++++++++