GPUs EMBO 2016

CSCS Lugano is the Nacional Supercomputing Center of Switzerland, which kindly provides the course with 20 accounts. Each account should be able to submit jobs to a single node connected to a K20 GPU and four CPU cores.

Connecting with CSCS

First you need to connect to the gate node ela using your cscs credentials from the credentials handout.

ssh -Y stud01@ela.cscs.ch

and then you can connect to the computing machine called daint, again you will be requested to type in your credentials.

stud01@ela2:~> ssh -Y daint

Using Dynamo

We are using a slightly older version of Dynamo on the supercomputer GPUs for compatibility reasons

Transferring projects

In this example, we show how to transfer a project from a local machine into the remote system.

On the local machine

1. tar your project in Dynamo (in Dynamo wizard >> Tools >> Create a tarball
2. rsync -avr my_project.tar stud##@ela.cscs.ch:~/
3. Also rsync your data to CSCS
4. Untar your Dynamo project

You will need the Dynamo terminal for this:

dynamo &

dvuntar myProject

On CSCS,

1. type
   salloc --gres=gpu:1
   to get a node with a gpu. It can take some time till the system allocates you a node. You can allocate up to two nodes.
   you can check the GPU on your node by:
   srun nvidia-smi
2. type
   source ~/bin/dynamoFlorida/dynamo_activate_linux_shipped_MCR.sh
   to activate Dynamo in your shell.
3. open Dynamo with dynamo &
4. open your project, and re-unfold it (make sure standalone GPU is selected and make sure your data is in the same relative location as on the local machine)

   Note

   if the graphical interface is too slow, you can use the command line instead:

   open a Dynamo console in your shell with dynamo x

   dvput my_project -destination system_gpu

   dvunfold my_project

5. run your alignment by typing srun my_project.exe

Creating tutorial data sets

We can use the system terminal as an equivalent of the Matlab terminal using the Dynamo standalone. This is an example on how to use it to create a phantom project like the one we did yesterday.

• open a Dynamo console by typing:

dynamo x

in a linux shell (you'll need to source Dynamo activation script on that shell beforehand).

• create a tutorial project. For this, type inside the Dynamo console:

dtutorial myTest -p ptest -M 128

• tune the project to work in a GPU

dvput ptest -destination system_gpu

• unfold the project

dvunfold ptest.exe inside the Dynamo console

• run the project with srun

srun ptest.exe in a terminal shell, i.e., not inside the Dynamo console

• when it finishes, the averages can be also accessed programmatically with the database tool. For instance, to access the last computed average and view it with dview, type:

ddb ptest:a -v

Note about performance You will notice that the project stops at several points during execution. Those are the points where the project accesses the MCR libraries. This overhead is a constant, and is a very small fraction of the computing time for a real project with thousands of particles.

We are using an old Dynamo version. Modern Dynamo versions don't access the MCR library several times.