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https://issues.apache.org/jira/browse/FLINK-5782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15862395#comment-15862395
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Kate Eri edited comment on FLINK-5782 at 2/11/17 1:05 PM:
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2) I think here you have got me wrong, I agree that sparse vectors support
should be implemented in ND4J.
4) persist method is for batch, integration with DL4J also for batch and mostly
batch for now is focus of interest. In scope of this ticket I don't propose GPU
optimizations for streaming, I think for this should be created separated
ticket.
5) thanks, i will take a look at this ticket. And I planned to ask ND4J about
this issue.
was (Author: kateri):
2) I think here you have got me wrong, I agree that sparse vectors support
should be implemented in ND4J.
4) persist method is for batch, integration with DL4J also for batch and mostly
batch for now is focus of interest, GPU for DL also for batch. In scope of this
ticket I don't propose GPU optimizations for streaming, I think for this should
be created separated ticket.
5) thanks, i will take a look at this ticket. And I planned to ask ND4J about
this issue.
> Support GPU calculations
> ------------------------
>
> Key: FLINK-5782
> URL: https://issues.apache.org/jira/browse/FLINK-5782
> Project: Flink
> Issue Type: Improvement
> Components: Core
> Affects Versions: 1.3.0
> Reporter: Kate Eri
> Priority: Minor
>
> This ticket was initiated as continuation of the dev discussion thread: [New
> Flink team member - Kate Eri (Integration with DL4J
> topic)|http://mail-archives.apache.org/mod_mbox/flink-dev/201702.mbox/browser]
>
> Recently we have proposed the idea to integrate
> [Deeplearning4J|https://deeplearning4j.org/index.html] with Apache Flink.
> It is known that DL models training is resource demanding process, so
> training on CPU could converge much longer than on GPU.
> But not only for DL training GPU usage could be supposed, but also for
> optimization of graph analytics and other typical data manipulations, nice
> overview of GPU related problems is presented [Accelerating Spark workloads
> using
> GPUs|https://www.oreilly.com/learning/accelerating-spark-workloads-using-gpus].
> Currently the community pointed the following issues to consider:
> 1) Flink would like to avoid to write one more time its own GPU support,
> to reduce engineering burden. That’s why such libraries like
> [ND4J|http://nd4j.org/userguide] should be considered.
> 2) Currently Flink uses [Breeze|https://github.com/scalanlp/breeze], to
> optimize linear algebra calculations, ND4J can’t be integrated as is, because
> it still doesn’t support [sparse arrays|http://nd4j.org/userguide#faq]. Maybe
> this issue should be simply contributed to ND4J to enable its usage?
> 3) The calculations would have to work with both available and not
> available GPUs. If the system detects that GPUs are available, then ideally
> it would exploit them. Thus GPU resource management could be incorporated in
> [FLINK-5131|https://issues.apache.org/jira/browse/FLINK-5131] (only
> suggested).
> 4) It was mentioned that as far Flink takes care of shipping data around
> the cluster, also it will perform its dump out to GPU for calculation and
> load back up. In practice, the lack of a persist method for intermediate
> results makes this troublesome (not because of GPUs but for calculating any
> sort of complex algorithm we expect to be able to cache intermediate results).
> That’s why the Ticket
> [FLINK-1730|https://issues.apache.org/jira/browse/FLINK-1730] must be
> implemented to solve such problem.
> 5) Also it was recommended to take a look at Apache Mahout, at least to
> get the experience with GPU integration and check its
> https://github.com/apache/mahout/tree/master/viennacl-omp
> https://github.com/apache/mahout/tree/master/viennacl
> 6) Also experience of Netflix regarding this question could be considered:
> [Distributed Neural Networks with GPUs in the AWS
> Cloud|http://techblog.netflix.com/search/label/CUDA]
> This is considered as master ticket for GPU related ticktes
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