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From dusenberr...@gmail.com
Subject Re: Discussion on GPU backend
Date Wed, 25 May 2016 16:02:29 GMT
Yeah to do this in the most "Apache Way (TM)", as well as to maintain sanity, we should definitely
use JIRA issues (ideally actual "sub tasks") and PRs to split up major features. It would
also be great to split it up into chunks of varying complexity that do not block others, so
that we could gather more contributors of various SystemML experience levels. The JIRA issues
should be used to divvy up tasks, and PRs should be used to propose an implementation for
that task, which would be followed by the usual comments from other contributors. 

As for a few other best practices with PRs, the PRs should also be merged with a "Closes #172."
line appended to the end, where the number reflects the GitHub PR number, so that the conversations
on a PR are linked to the final merged commit. Also, any necessary rebasing on a PR should
be done by simply overwriting that PR branch (which exists on the contributor's fork of SystemML),
which allows GitHub to keep the same PR open, and thus the entire conversation can be followed.


Excited about the GPU work!

-Mike

--

Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry

Sent from my iPhone.


> On May 25, 2016, at 8:08 AM, Niketan Pansare <npansar@us.ibm.com> wrote:
> 
> Thanks Berthold and Matthias for your suggestions. It is important to note whether we
go with (A) or (B), the initial PR will be squashed in one commit and individual commits by
external contributor will be lost in the process. However, since we are planning to go with
option (3), the impact won't be too severe.
> 
> Matthias: Here are my thoughts regarding the unknowns for GPU backend:
> 1. Handling of native libraries:
> Both JCuda and Nvidia provide shared libraries/DLL for most OS/platforms along with installation
instructions.
> 
> For deployment:
> As per the previous email, the native libraries will be treated as an external dependency,
just like hadoop/spark. For example: if someone executes: "hadoop jar SystemML.jar -f test.dml
-exec hybrid_spark", she will get "Class Not Found" exception. In similar fashion, if the
user doesnot include JCu*.jar or provide native libraries (JCu*.dll/so or CUDA or CuDNN) and
supplies "-accelerator" flag, a "Class not found" or "Cannot load .." exception will be thrown
respectively. If user doesnot supply "-accelerator" flag, SystemML will proceed will normal
execution as it does today. 
> 
> For dev:
> We are planning to host jcu*.jar into one of maven repository. Once that's done, the
"system" scope in pom will be replaced by "provided" scope and the jcu*.jars will be deleted
from PR. Like deployment, it is responsibility of the developer to install native libraries
if she intends to work on GPU backend.
> 
> For testing:
> The user can set the environment variable "CUDA_PATH" and set TEST_GPU flag to enable
GPU tests (Please see https://github.com/apache/incubator-systemml/pull/165/files#diff-bcda036e4c3ff62cb2648acbbd19f61aR113).
The PR will be accompanied by additional tests which will be enabled only when TEST_GPU is
set. Having TEST_GPU flag allows users without Nvidia GPU to run the integration test. Like
deployment, it is responsibility of the developer to install native libraries for testing
with TEST_GPU flag. 
> 
> The first version will not contain custom native kernels. 
> 
> 2. I can add the summary of the performance comparisons in the PR :)
> 
> 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
> 
> Berthold Reinwald---05/25/2016 06:03:55 AM---the discussion is less about (1), (2), or
(3). As practiced so far, (3) is the way to go.
> 
> From: Berthold Reinwald/Almaden/IBM@IBMUS
> To: dev@systemml.incubator.apache.org
> Date: 05/25/2016 06:03 AM
> Subject: Re: Discussion on GPU backend
> 
> 
> 
> 
> the discussion is less about (1), (2), or (3). As practiced so far, (3) is 
> the way to go.
> 
> The question is about (A) or (B). Curious was the Apache suggested 
> practice is.
> 
> Regards,
> Berthold Reinwald
> IBM Almaden Research Center
> office: (408) 927 2208; T/L: 457 2208
> e-mail: reinwald@us.ibm.com
> 
> 
> 
> From:   Matthias Boehm/Almaden/IBM@IBMUS
> To:     dev@systemml.incubator.apache.org
> Date:   05/24/2016 09:10 PM
> Subject:        Re: Discussion on GPU backend
> 
> 
> 
> Generally, I think we should really stick to (3) as done in the past, 
> i.e., bring up major features in the roadmap discussions, create jira 
> epics and try to break them into rather isolated tasks. This works for 
> almost any major/minor feature. The only exception are features, where it 
> is initially unknown if the potential benefits outweigh the increased 
> complexity (or other disadvantages). Here, prototypes are required but 
> everybody should be free to choose a way of maintaining them. I also don't 
> expect too much collaboration here because of the unknown status. Once the 
> initial unknowns are resolved, we should come back to (3) tough.
> 
> Regarding the GPU backend, the unknowns to resolve are (1) the handling of 
> native libraries/kernels for deployment/test/dev, and (2) performance 
> comparisons on selected algorithms (prototypes, not fully integrated), 
> data sizes, and platforms. Once we have answers to these questions, we can 
> create all the tasks for optimizer/runtime integration. 
> 
> Regards,
> Matthias 
> 
> 
> Niketan Pansare---05/24/2016 11:55:19 AM---Hi all, Since there is interest 
> in collaborating on GPU backend, I wanted to know
> 
> From: Niketan Pansare/Almaden/IBM@IBMUS
> To: dev@systemml.incubator.apache.org
> Date: 05/24/2016 11:55 AM
> Subject: Re: Discussion on GPU backend
> 
> 
> 
> Hi all,
> 
> Since there is interest in collaborating on GPU backend, I wanted to know 
> what is the preferred way to go ahead with a new feature (i.e. GPU 
> backend) ? This discussion is also generally applicable to other major 
> features (for example: Flink backend, Deep Learning support, OpenCL 
> backend, new data types, new built-in functions, new algorithms, etc).
> 
> The first point of discussion is what would qualify as a "major feature" 
> and how we integrate it into SystemML ? Here are three options that could 
> serve as a potential requirement:
> 1. The feature has to be fully functional and fully optimized. For 
> example: in the case of additional backends, the PR can only be merged in 
> if and only if, all the instructions (CP or distributed) has been 
> implemented and is at least as optimized as our existing alternate 
> backends. In the case of algorithms or the built-in functions, the PR can 
> only be merged in if and only if, it runs on all the backends for all 
> datasets and is comparable in performance and accuracy with an external ML 
> libraries.
> 2. The feature has to be fully functional. In this case, the PR can only 
> be merged in if and only if all the instructions (CP or distributed) has 
> been implemented. However, the first version of the new backend need not 
> perform faster than our existing alternate backends.
> 3. Increment addition but with unit testcases that addresses quality and 
> stability concerns. In this case, a PR can be merged if a subset of 
> instructions has been implemented along with set of unit test cases 
> suggested by our committers. The main benefit here is quick-feedback 
> iterations from our committers, whereas the main drawback is an 
> intermediate state where we don't fully support the given backend for 
> certain scenario. 
> 
> If we decide to go with option 1 or 2, then potentially there will be a 
> lot of code to review at the end and ideally we should give opportunity 
> for our committers to provide early review comments on the feature. This 
> will mitigate the risk of having to re-implement the entire feature. The 
> options here are:
> A. Create a branch on https://github.com/apache/incubator-systemml. This 
> allows people to collaborate as well as allows committers to look at the 
> code.
> B. Create a branch on a fork and have a PR up to allow committers to raise 
> concerns and provide suggestions. This is done for 
> https://github.com/apache/incubator-systemml/pull/165 and 
> https://github.com/apache/incubator-systemml/pull/119. To collaborate, the 
> person creating PR will act as committer for the feature and will accept 
> PR on its branch and will be responsible for resolving conflicts and 
> keeping the PR in sync with the master.
> 
> If we decide to go with the option 3 (i.e. incremental addition), the 
> option B seems to be logical choice as we already do this for other 
> features.
> 
> My goal here is not to create a formal process but instead to avoid any 
> potential misunderstanding/confusion and also to follow recommended Apache 
> practices.
> 
> Please email back with your thoughts :)
> 
> 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
> 
> Deron Eriksson ---05/18/2016 11:22:26 AM---Hi Niketan, Good idea, I think 
> that would be the cleanest solution for now. Since JCuda
> 
> From: Deron Eriksson <deroneriksson@gmail.com>
> To: dev@systemml.incubator.apache.org
> Date: 05/18/2016 11:22 AM
> Subject: Re: Discussion on GPU backend
> 
> 
> 
> Hi Niketan,
> 
> Good idea, I think that would be the cleanest solution for now. Since 
> JCuda
> doesn't appear to be in a public maven repo, it adds a layer of difficulty
> to clean integration via maven builds.
> 
> Deron
> 
> 
> On Wed, May 18, 2016 at 10:55 AM, Niketan Pansare <npansar@us.ibm.com>
> wrote:
> 
> > Hi Deron,
> >
> > Good points. I vote that we keep JCUDA and other accelerators we add as 
> an
> > external dependency. This means the user will have to ensure JCuda.jar 
> in
> > the class path and JCuda.DLL/JCuda.so in the LD_LIBRARY_PATH.
> >
> > I don't think JCuda.jar is platform-specific.
> >
> > 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 Deron Eriksson ---05/18/2016 10:51:17
> > AM---Hi, I'm wondering what would be a good way to handle JCuda]Deron
> > Eriksson ---05/18/2016 10:51:17 AM---Hi, I'm wondering what would be a 
> good
> > way to handle JCuda in terms of the
> >
> > From: Deron Eriksson <deroneriksson@gmail.com>
> > To: dev@systemml.incubator.apache.org
> > Date: 05/18/2016 10:51 AM
> > Subject: Re: Discussion on GPU backend
> > ------------------------------
> >
> >
> >
> > 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/
> > >
> > >
> > >
> > >
> >
> >
> >
> >
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 

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