You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@spark.apache.org by cfangmac <cf...@aliyun.com.INVALID> on 2019/12/09 07:49:01 UTC

Spark3.0 gpu support

Hi everyone，

 

Recently I use the master branch of Apache Spark from github and try to use the function of GPU-aware scheduling.

 

I setup a standalone cluster and set some GPU related config options，such as，

A） spark.worker.resourceFile，which is followd by a json format file that contains gpu addresses;

B） spark.worker.resource.gpu.amount, which specified the gpu amount for each worker;

C） spark.executor.resource.gpu.amount, which specified the gpu amount for each executor;

D）spark.task.resource.gpu.amount, which specified the gpu request from each task;

 

Then I run a k-means training program which I thought would require many mathematical operations and gpu is thought to be helpful to accelerate the training. I got the web page as follow and it seems those gpu options are configured correctly, however I used the gpu monitor tool and found that those gpus  seems does not be used, that is to say the training program is still run on cpu other than gpu.

 

Now I am confused about two points，

1， is there something I missed that caused the fail to use gpu？

2， After the task is deserialized in executor, how does a jvm(Java/Scala) program run on gpu？

Does the spark executor use JNI + cuda/opencl or other tools? 

 

 

Thanks，

Chao Fang

FW: Spark3.0 gpu support

Posted by cfangmac <cf...@aliyun.com.INVALID>.
 

 

 

 

 

发件人: cfangmac <cf...@aliyun.com>
日期: 2019年12月9日 星期一 下午3:49
收件人: <de...@spark.apache.org>
主题: Spark3.0 gpu support

 

Hi everyone，

 

Recently I use the master branch of Apache Spark from github and try to use the function of GPU-aware scheduling.

 

I setup a standalone cluster and set some GPU related config options，such as，

A） spark.worker.resourceFile，which is followd by a json format file that contains gpu addresses;

B） spark.worker.resource.gpu.amount, which specified the gpu amount for each worker;

C） spark.executor.resource.gpu.amount, which specified the gpu amount for each executor;

D）spark.task.resource.gpu.amount, which specified the gpu request from each task;

 

Then I run a k-means training program which I thought would require many mathematical operations and gpu is thought to be helpful to accelerate the training. I got the web page as follow and it seems those gpu options are configured correctly, however I used the gpu monitor tool and found that those gpus  seems does not be used, that is to say the training program is still run on cpu other than gpu.

 

Now I am confused about two points，

1， is there something I missed that caused the fail to use gpu？

2， After the task is deserialized in executor, how does a jvm(Java/Scala) program run on gpu？

Does the spark executor use JNI + cuda/opencl or other tools? 

 

 

Thanks，

Chao Fang

Re: Spark3.0 gpu support

Posted by Sean Owen <sr...@gmail.com>.
Spark itself does not use GPUs at all. This functionality is for scheduling
workloads that do.
It does use BLAS, but unless you have a BLAS library that uses GPUs, it
wouldn't cause Spark to use them.

On Mon, Dec 9, 2019 at 1:50 AM cfangmac <cf...@aliyun.com.invalid>
wrote:

> Hi everyone，
>
>
>
> Recently I use the master branch of Apache Spark from github and try to
> use the function of GPU-aware scheduling.
>
>
>
> I setup a standalone cluster and set some GPU related config options，such
> as，
>
> A） spark.worker.resourceFile，which is followd by a json format file that
> contains gpu addresses;
>
> B） spark.worker.resource.gpu.amount, which specified the gpu amount for
> each worker;
>
> C） spark.executor.resource.gpu.amount, which specified the gpu amount for
> each executor;
>
> D）spark.task.resource.gpu.amount, which specified the gpu request from
> each task;
>
>
>
> Then I run a k-means training program which I thought would require many
> mathematical operations and gpu is thought to be helpful to accelerate the
> training. I got the web page as follow and it seems those gpu options are
> configured correctly, however I used the gpu monitor tool and found that
> those gpus  seems does not be used, *that is to say the training program
> is still run on cpu other than gpu.*
>
>
>
> Now I am confused about two points，
>
> 1， is there something I missed that caused the fail to use gpu？
>
> 2， After the task is deserialized in executor, how does a jvm(Java/Scala)
> program run on gpu？
>
> Does the spark executor use JNI + cuda/opencl or other tools?
>
> [image: p4239]
>
>
>
>
>
> Thanks，
>
> Chao Fang
>
>
>
> ---------------------------------------------------------------------
> To unsubscribe e-mail: dev-unsubscribe@spark.apache.org