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From Marco de Abreu <marco.g.ab...@googlemail.com.INVALID>
Subject MKLDNN performance in CI
Date Fri, 23 Nov 2018 01:21:48 GMT
Hello,

I have noticed that our Python tests have been increasing in duration
recently. In order to analyse this further, I created the PR [1] which
allows to record test durations. Please note that I did not dive deep on
these numbers and that they have to be taken with a grain of salt since
slaves have varying resource utilizations.

Please have a look at the two following logs:
Python3 CPU MKLDNN:
http://jenkins.mxnet-ci.amazon-ml.com/blue/rest/organizations/jenkins/pipelines/mxnet-validation/pipelines/unix-cpu/branches/PR-13377/runs/2/nodes/155/steps/409/log/?start=0
Python3 CPU Openblas:
http://jenkins.mxnet-ci.amazon-ml.com/blue/rest/organizations/jenkins/pipelines/mxnet-validation/pipelines/unix-cpu/branches/PR-13377/runs/2/nodes/152/steps/398/log/?start=0

If you scroll to the end (note that there are multiple test stages and
summaries being printed in these logs), you will find the following
statements:

Python3 CPU MKLDNN: "Ran 702 tests in 3042.102s"
Python3 CPU Openblas: "Ran 702 tests in 2158.458s"

This shows that the MKLDNN is generally being about 40% slower than the
Openblas backend. If we go into the details, we can see that some tests are
significantly slower:

Python3 CPU MKLDNN:

>[success] 20.78% test_random.test_shuffle: 630.7165s
>[success] 17.79% test_sparse_operator.test_elemwise_binary_ops: 540.0487s
>[success] 10.91% test_gluon_model_zoo.test_models: 331.1503s
>[success] 2.62% test_operator.test_broadcast_binary_op: 79.4556s
>[success] 2.45% test_operator.test_pick: 74.4041s
>[success] 2.39% test_metric_perf.test_metric_performance: 72.5445s
>[success] 2.38% test_random.test_negative_binomial_generator: 72.1751s
>[success] 1.84% test_operator.test_psroipooling: 55.9432s
>[success] 1.78% test_random.test_poisson_generator: 54.0104s
>[success] 1.72% test_gluon.test_slice_pooling2d_slice_pooling2d: 52.3447s
>[success] 1.60% test_contrib_control_flow.test_cond: 48.6977s
>[success] 1.41% test_random.test_random: 42.8712s
>[success] 1.03% test_operator.test_layer_norm: 31.1242s


Python3 CPU Openblas:
> [success] 26.20% test_gluon_model_zoo.test_models: 563.3366s
> [success] 4.34% test_random.test_shuffle: 93.3157s
> [success] 4.31% test_random.test_negative_binomial_generator: 92.6899s
> [success] 3.78% test_sparse_operator.test_elemwise_binary_ops: 81.2048s
>  [success] 3.30% test_operator.test_psroipooling: 70.9090s
>  [success] 3.20% test_random.test_poisson_generator: 68.7500s
>  [success] 3.10% test_metric_perf.test_metric_performance: 66.6085s
>  [success] 2.79% test_operator.test_layer_norm: 59.9566s
>  [success] 2.66% test_gluon.test_slice_pooling2d_slice_pooling2d: 57.1887s
>  [success] 2.62% test_operator.test_pick: 56.2312s
>  [success] 2.60% test_random.test_random: 55.8920s
>  [success] 2.19% test_operator.test_broadcast_binary_op: 47.1879s
> [success] 0.96% test_contrib_control_flow.test_cond: 20.6908s

Tests worth noting:
- test_random.test_shuffle: 700% increase - but I don't know how this may
be related to MKLDNN. Are we doing random number generation in either of
those backends?
- test_sparse_operator.test_elemwise_binary_ops: 700% increase
- test_gluon_model_zoo.test_models: 40% decrease - that's awesome and to be
expect :)
- test_operator.test_broadcast_binary_op: 80% increase
- test_contrib_control_flow.test_cond: 250% increase
- test_operator.test_layer_norm: 50% decrease - nice!

As I have stated previously, these numbers might not mean anything since
the CI is not a benchmarking environment (sorry if these are false
negatives), but I thought it might be worth mentioning so Intel could
follow up and dive deeper.

Does anybody here create 1:1 operator comparisons (e.g. running layer_norm
in the different backends to compare the performance) who could provide us
with those numbers?

Best regards,
Marco

[1]: https://github.com/apache/incubator-mxnet/pull/13377

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