TensorFlow for MultiGPU

Question

誰かが私が状況を理解するのを助けることができれば、それは素晴らしいことでしょう。前もって感謝します。 マイセットアップ： OS：Ubuntu 16.04,2 Titan X GPUs。 TensorFlow（バージョン0.12.1）は、conda環境にインストールされ、pip docを使用してpipを使用します。 Python 3.5。

コード： 次のコードを実行して、2つのGPUセットアップをテストしました。一度それぞれrandom_matrix = tf.zeros(...)とrandom_matrix = tf.random_uniform(...)とします。出力は以下の通りです。

質問： 1）私はtf.zerosで動作します。 CPUとGPUのタイミングは同じです。しかし、tf.random_uniformと私はGPUが（私が予想したように）高速です参照してください。 tf.zerosがGPUで遅いのはなぜですか？私は何が欠けていますか？ 2）グローバルシードとローカルシードを修正しました。 tf.random_uniformのケースでGPU内の出力が異なるのはなぜですか？

事前に洞察力をお寄せいただきありがとうございます。 tf.random_uniformと

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") 

タイミング（）：tf.zerosと

('/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') 
--------------------------------------------------------- 

タイミング（）：

('/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

私はGPUカーネルの最適化に、これは、関連する本を疑います。同じ計算シェイプを実行してGPUを「プリ・ウォーミング」すると、次の実行がはるかに高速になります。プロセスのGPUでカーネルの最初の使い方にadds a couple of secondsというPTXコンパイルがありますが、マトリックスのサイズに合わせて実行時間が増えるというのは特筆すべきことです。何らかのプロファイリングが行われているのかもしれません。

tf.OptimizerOptions.L0がなければ、それはおそらく速くなるので、キャッシュも起こっていることに注意してください。

cpu1 1.716048240661621 
cpu2 1.509080171585083 
gpu1 4.192790746688843 
gpu2 0.13361549377441406 

Answer 2

おかげヤロスラフ：

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") 

は、私はこれを参照してください！誰かが興味を持っている場合に備えて、私は実行したコードと結果を提供します。コードを試してみたら、数分間お待ちください。

コード：

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) 

概要：あなたの提案ヤロスラフため

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') 
++++++++++++++++++++++++++++++++++++++++++++++++++++