systemml-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Niketan Pansare" <>
Subject Re: Discussion on GPU backend
Date Wed, 18 May 2016 05:50:06 GMT

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

The user also needs to set LD_LIBRARY_PATH that points to JCuda DLL or so
files. Please see ... For
example: the user can add following to
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)
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
4. MR
5. SPARK-GPU (requires GPU cluster)
6. MR-GPU (requires GPU cluster)


Niketan Pansare
IBM Almaden Research Center
E-mail: npansar At

From:	Luciano Resende <>
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 <> wrote:

> Hi all,
> I have updated the design document for our GPU backend in the JIRA
> The implementation
> details are based on the prototype I created and is available in PR
> 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
> 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
> 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
> will be created by ExecutionContextFactory depending on the
> 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

Luciano Resende

  • Unnamed multipart/related (inline, None, 0 bytes)
View raw message