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tf.linalg.eigh is slower on GPU than on CPU
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import tensorflow as tf | |
import timeit | |
assert tf.test.is_gpu_available() | |
# See https://www.tensorflow.org/tutorials/using_gpu#allowing_gpu_memory_growth | |
config = tf.ConfigProto() | |
config.gpu_options.allow_growth = True | |
def sym(x): | |
return 0.5 * (x + tf.matrix_transpose(x)) | |
with tf.device('/cpu:0'): | |
wv_cpu = tf.linalg.eigh(sym(tf.random.uniform((100000, 2, 2)))) | |
with tf.device('/device:GPU:0'): | |
wv_gpu = tf.linalg.eigh(sym(tf.random.uniform((100000, 2, 2)))) | |
with tf.Session(config=config) as sess: | |
cpu = lambda: sess.run(wv_cpu) | |
gpu = lambda: sess.run(wv_gpu) | |
# We run each op once to warm up; see: https://stackoverflow.com/a/45067900 | |
cpu() | |
gpu() | |
# Run the op several times. | |
cpu_time = timeit.timeit('cpu()', number=10, setup="from __main__ import cpu") | |
gpu_time = timeit.timeit('gpu()', number=10, setup="from __main__ import gpu") | |
print('CPU (s): ', cpu_time) | |
print('GPU (s): ', gpu_time) | |
print('CPU speedup over GPU: {}x'.format(int(gpu_time/cpu_time))) |
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