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From Luciano Resende <luckbr1...@gmail.com>
Subject Re: Discussion on GPU backend
Date Wed, 18 May 2016 17:55:48 GMT
Well, there is no requirement for building platform specific binaries, so
one option would be to build for "Linux" and provide user documentation in
case there is interest around platform specific binaries (e.g. this is how
Hadoop does with regards of Windows support and required DLLs)

On Wed, May 18, 2016 at 10:51 AM, Deron Eriksson <deroneriksson@gmail.com>
wrote:

> Hi,
>
> I'm wondering what would be a good way to handle JCuda in terms of the
> build release packages. Currently we have 11 artifacts that we are
> building:
>    systemml-0.10.0-incubating-SNAPSHOT-inmemory.jar
>    systemml-0.10.0-incubating-SNAPSHOT-javadoc.jar
>    systemml-0.10.0-incubating-SNAPSHOT-sources.jar
>    systemml-0.10.0-incubating-SNAPSHOT-src.tar.gz
>    systemml-0.10.0-incubating-SNAPSHOT-src.zip
>    systemml-0.10.0-incubating-SNAPSHOT-standalone.jar
>    systemml-0.10.0-incubating-SNAPSHOT-standalone.tar.gz
>    systemml-0.10.0-incubating-SNAPSHOT-standalone.zip
>    systemml-0.10.0-incubating-SNAPSHOT.jar
>    systemml-0.10.0-incubating-SNAPSHOT.tar.gz
>    systemml-0.10.0-incubating-SNAPSHOT.zip
>
> It looks like JCuda is platform-specific, so you typically need different
> jars/dlls/sos/etc for each platform. If I'm understanding things correctly,
> if we generated Windows/Linux/LinuxPowerPC/MacOS-specific SystemML
> artifacts for JCuda, we'd potentially have an enormous number of artifacts.
>
> Is this something that could be potentially handled by specific profiles in
> the pom so that a user might be able to do something like "mvn clean
> package -P jcuda-windows" so that a user could be responsible for building
> the platform-specific SystemML jar for jcuda? Or is this something that
> could be handled differently, by putting the platform-specific jcuda jar on
> the classpath and any dlls or other needed libraries on the path?
>
> Deron
>
>
>
> On Tue, May 17, 2016 at 10:50 PM, Niketan Pansare <npansar@us.ibm.com>
> wrote:
>
> > Hi Luciano,
> >
> > Like all our backends, there is no change in the programming model. The
> > user submits a DML script and specifies whether she wants to use an
> > accelerator. Assuming that we compile jcuda jars into SystemML.jar, the
> > user can use GPU backend using following command:
> > spark-submit --master yarn-client ... -f MyAlgo.dml -accelerator -exec
> > hybrid_spark
> >
> > The user also needs to set LD_LIBRARY_PATH that points to JCuda DLL or so
> > files. Please see *https://issues.apache.org/jira/browse/SPARK-1720*
> > <https://issues.apache.org/jira/browse/SPARK-1720> ... For example: the
> > user can add following to spark-env.sh
> > export LD_LIBRARY_PATH=<path to jcuda so>:$LD_LIBRARY_PATH
> >
> > The first version of GPU backend will only accelerate CP. In this case,
> we
> > have four types of instructions:
> > 1. CP
> > 2. GPU (requires GPU on the driver)
> > 3. SPARK
> > 4. MR
> >
> > Note, the first version will require the CUDA/JCuda dependency to be
> > installed on the driver only.
> >
> > The next version will accelerate our distributed instructions as well. In
> > this case, we will have six types of instructions:
> > 1. CP
> > 2. GPU
> > 3. SPARK
> > 4. MR
> > 5. SPARK-GPU (requires GPU cluster)
> > 6. MR-GPU (requires GPU cluster)
> >
> > Thanks,
> >
> > Niketan Pansare
> > IBM Almaden Research Center
> > E-mail: npansar At us.ibm.com
> > http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
> >
> > [image: Inactive hide details for Luciano Resende ---05/17/2016 09:13:24
> > PM---Great to see detailed information on this topic Niketan,]Luciano
> > Resende ---05/17/2016 09:13:24 PM---Great to see detailed information on
> > this topic Niketan, I guess I have missed when you posted it in
> >
> > From: Luciano Resende <luckbr1975@gmail.com>
> > To: dev@systemml.incubator.apache.org
> > Date: 05/17/2016 09:13 PM
> > Subject: Re: Discussion on GPU backend
> > ------------------------------
> >
> >
> >
> > Great to see detailed information on this topic Niketan, I guess I have
> > missed when you posted it initially.
> >
> > Could you elaborate a little more on what is the programming model for
> when
> > the user wants to leverage GPU ? Also, today I can submit a job to spark
> > using --jars and it will handle copying the dependencies to the worker
> > nodes. If my application wants to leverage GPU, what extras dependencies
> > will be required on the worker nodes, and how they are going to be
> > installed/updated on the Spark cluster ?
> >
> >
> >
> > On Tue, May 3, 2016 at 1:26 PM, Niketan Pansare <npansar@us.ibm.com>
> > wrote:
> >
> > >
> > >
> > > Hi all,
> > >
> > > I have updated the design document for our GPU backend in the JIRA
> > > https://issues.apache.org/jira/browse/SYSTEMML-445. The implementation
> > > details are based on the prototype I created and is available in PR
> > > https://github.com/apache/incubator-systemml/pull/131. Once we are
> done
> > > with the discussion, I can clean up and separate out the GPU backend
> in a
> > > separate PR for easier review :)
> > >
> > > Here are key design points:
> > > A GPU backend would implement two abstract classes:
> > >    1.   GPUContext
> > >    2.   GPUObject
> > >
> > >
> > >
> > > The GPUContext is responsible for GPU memory management and gets
> > call-backs
> > > from SystemML's bufferpool on following methods:
> > >    1.   void acquireRead(MatrixObject mo)
> > >    2.   void acquireModify(MatrixObject mo)
> > >    3.   void release(MatrixObject mo, boolean isGPUCopyModified)
> > >    4.   void exportData(MatrixObject mo)
> > >    5.   void evict(MatrixObject mo)
> > >
> > >
> > >
> > > A GPUObject (like RDDObject and BroadcastObject) is stored in
> > CacheableData
> > > object. It contains following methods that are called back from the
> > > corresponding GPUContext:
> > >    1.   void allocateMemoryOnDevice()
> > >    2.   void deallocateMemoryOnDevice()
> > >    3.   long getSizeOnDevice()
> > >    4.   void copyFromHostToDevice()
> > >    5.   void copyFromDeviceToHost()
> > >
> > >
> > >
> > > In the initial implementation, we will add JCudaContext and
> JCudaPointer
> > > that will extend the above abstract classes respectively. The
> > JCudaContext
> > > will be created by ExecutionContextFactory depending on the
> > user-specified
> > > accelarator. Analgous to MR/SPARK/CP, we will add a new ExecType: GPU
> and
> > > implement GPU instructions.
> > >
> > > The above design is general enough so that other people can implement
> > > custom accelerators (for example: OpenCL) and also follows the design
> > > principles of our CP bufferpool.
> > >
> > > Thanks,
> > >
> > > Niketan Pansare
> > > IBM Almaden Research Center
> > > E-mail: npansar At us.ibm.com
> > > http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
> > >
> >
> >
> >
> > --
> > Luciano Resende
> > http://twitter.com/lresende1975
> > http://lresende.blogspot.com/
> >
> >
> >
> >
>



-- 
Luciano Resende
http://twitter.com/lresende1975
http://lresende.blogspot.com/

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