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From Nakul Jindal <naku...@gmail.com>
Subject Re: Build and distribution related issues for GPU support
Date Thu, 24 Nov 2016 07:34:12 GMT
@Matthias -
PTX (parallel thread execution) objects are intermediate compiled objects.

As of the current master, they are maintained under git version control.
This decision was agreed upon after discussing the hassle that a developer
of systemml without the nvidia cuda compiler might face.
It was decided that a person modifying the .cu files will be responsible
for regenerating the .ptx file and committing it to version control.
So far, between the active developers of systemml, this practice has not
disrupted their regular workflow.

About PTX version.
Newer PTX versions support newer architectures. As and when we upgrade to
newer CUDA versions, we shall use the cuda compiler that ships with that
version of the toolkit and compile the .cu files in the project and commit
the resulting .ptx files.

Thoughts, comments?

-Nakul






On Wed, Nov 23, 2016 at 2:43 PM, Matthias Boehm <mboehm7@googlemail.com>
wrote:

> thanks for sharing Nakul. Could you please also comment on the PTX story
> for custom kernels and different PTX versions?
>
> Regards,
> Matthias
>
>
> On 11/23/2016 10:13 PM, Nakul Jindal wrote:
>
>> Hi,
>>
>> SystemML has experimental GPU support, which we are working to solidify.
>> Currently, GPU is supported in CP (Standalone/Single Node) mode. It uses a
>> single GPU (even if the node has more than 1 GPU).
>>
>> Communication between the GPU and JVM happens through JCuda (MIT License)
>> -
>> a light java wrapper over CUDA that uses JNI. To that end, JCuda needs to
>> compile a platform specific shared library which is then used to
>> communicate with the locally installed Cuda.
>> To help with not having to compile a piece of C/C++ code each time, we use
>> a project Mavenized-Jcuda(MIT-License). This project internally has a
>> repository which contains compiled shared objects (for JCuda) for
>> different
>> platforms for different versions of Cuda.
>>
>>
>> For developers of SystemML (People who compile SystemML from source) :
>> As of today, one can checkout the master branch and follow a series of
>> setup steps to get SystemML in GPU mode running.
>> These are the steps -
>> https://github.com/apache/incubator-systemml/blob/master/
>> docs/devdocs/gpu-backend.md
>>
>> 1a)
>> Broadly,
>> 0. Compile systemml & mavenized jcuda.
>> 1. Mavenized JCuda jars are put into the classpath of SystemML.
>> 2. The native shared library should be put in the LD_LIBRARY_PATH or
>> java.library.path.
>> 3. SystemML should be run with the "-gpu" flag. Like so:
>> (In the incubator-systemml directory)
>>
>> bin/systemml "file.dml" -gpu force=true
>>
>> PR 291 (https://github.com/apache/incubator-systemml/pull/291) tries to
>> change this so that setup becomes simpler.  (Given that mavenized-jcuda is
>> available in one of the repositories specified in systemml's pom.xml)
>>
>> 1b)
>> 0. Compule systemml
>> 1. Run systemml
>>
>> bin/systemml "file.dml" -gpu force=true
>>
>>
>>
>> For users of SystemML:
>> We haven't yet decided on how to ship SystemML with GPU support. Here are
>> the 2 ways we can think of:
>>
>> 2a)
>> 0. User installs pre-requisites (java, cuda, etc)
>> 1. User "installs" Mavenized-JCuda or JCuda. (i.e. the package jars are
>> made available in the classpath). Also the relevant shared object library
>> files (.so, .dll) files are made available to the JVM through the
>> LD_LIBRARY_PATH environment variable or through java.library.path setting
>> variable. (Note this needs to happen if using cuda <8.0)
>> 2. Download and run the systemml jar.
>>
>> 2b)
>> We package JCuda/Mavenized-JCuda with the SystemML distribution. We
>> already
>> package ANTLR and Wink with our jar. Our other dependencies are "provided"
>> scope and are not pulled in by the maven shade plugin.
>> A separate jar will be released for every platform.
>> 0. User installs pre-requisites
>> 1. Download and run systemml jar
>>
>>
>> There is also the matter of running SystemML with GPU in distributed mode.
>> In hybrid_spark mode, with option 2a, we'd need to install
>> JCuda/Mavenized-JCuda on all the worker nodes.
>> With option 2b, we wouldn't need to.
>>
>>
>> Berthold, Niketan and I have had a discussion and agree on option 2a, for
>> now.
>>
>> Are there any thoughts? Inputs?
>>
>> -Nakul Jindal
>>
>>

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