title: Testing GPU with tensorflow matrix multiplication
body: <p>As many machine learning algorithms rely to matrix multiplication(or at least can be implemented using matrix multiplication) to test my GPU is I plan to create matrices a , b , multiply them and record time it takes for computation to complete.</p>
  
  <p>Here is code that will generate two matrices of dimensions 300000,20000 and multiply them :</p>
  
  <pre><code>import tensorflow as tf
  import numpy as np
  
  init = tf.global_variables_initializer()
  sess = tf.Session()
  sess.run(init)
  
  
  #a = np.array([[1, 2, 3], [4, 5, 6]])
  #b = np.array([1, 2, 3])
  
  a = np.random.rand(300000,20000)
  b = np.random.rand(300000,20000)
  
  println("Init complete");
  
  result = tf.mul(a , b)
  v = sess.run(result) 
  
  print(v)
  </code></pre>
  
  <p>Is this a sufficient test to compare performance of GPU's ? What other factors should I consider ?</p>