What's the effect of CNMeM enabled but 'cuDNN not available' in Theano?
<p>I have the following code based on <a href="http://deeplearning.net/software/theano/tutorial/using_gpu.html" rel="nofollow">Theano example</a>:</p>

<pre><code>from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
    r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print('Used the cpu')
else:
    print('Used the gpu')
</code></pre>

<p>Now when I test the code with two modes:</p>

<p>GPU mode, I get this:</p>

<pre><code>$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python gpu.py
Using gpu device 0: Tesla C2075 (CNMeM is enabled with initial size: 95.0% of memory, cuDNN not available)
[GpuElemwise{exp,no_inplace}(&lt;CudaNdarrayType(float32, vector)&gt;), HostFromGpu(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.475526 seconds
Result is [ 1.23178029  1.61879349  1.52278066 ...,  2.20771813  2.29967761
  1.62323296]
Used the gpu
</code></pre>

<p>CPU mode, I get this:</p>

<pre><code>$ THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python gpu.py
[Elemwise{exp,no_inplace}(&lt;TensorType(float32, vector)&gt;)]
Looping 1000 times took 5.221368 seconds
Result is [ 1.23178029  1.61879337  1.52278066 ...,  2.20771813  2.29967761
  1.62323284]
Used the cpu
</code></pre>

<p>Notice two things, GPU is indeed faster than CPU (0.47sec vs 5 sec). But at the same time at GPU I get the cuDNN not available message.</p>

<p>My question is this. What's the effect of the absence of cuDNN? Is it harmful?</p>