High Performance Computers
==========================

Relevant Machines
-----------------

This page includes instructions and guidelines when deploying Dask on high
performance supercomputers commonly found in scientific and industry research
labs.  These systems commonly have the following attributes:

1.  Some mechanism to launch MPI applications or use job schedulers like
    SLURM, SGE, TORQUE, LSF, DRMAA, PBS, or others
2.  A shared network file system visible to all machines in the cluster
3.  A high performance network interconnect, such as Infiniband
4.  Little or no node-local storage


Where to start
--------------

Most of this page documents various ways and best practices to use Dask on an
HPC cluster.  This is technical and aimed both at users with some experience
deploying Dask and also system administrators.

The preferred and simplest way to run Dask on HPC systems today both for new,
experienced users or administrator is to use
`dask-jobqueue <https://jobqueue.dask.org>`_.

However, dask-jobqueue is slightly oriented toward interactive analysis usage,
and it might be better to use tools like dask-mpi in some routine batch
production workloads.


Dask-jobqueue and Dask-drmaa
----------------------------

`dask-jobqueue <https://jobqueue.dask.org>`_ provides cluster managers for PBS,
SLURM, LSF, SGE and other resource managers. You can launch a Dask cluster on
these systems like this.

.. code-block:: python

   from dask_jobqueue import PBSCluster

   cluster = PBSCluster(cores=36,
                        memory="100GB",
                        project='P48500028',
                        queue='premium',
                        interface='ib0',
                        walltime='02:00:00')

   cluster.scale(100)  # Start 100 workers in 100 jobs that match the description above

   from dask.distributed import Client
   client = Client(cluster)    # Connect to that cluster

Dask-jobqueue provides a lot of possibilities like adaptive dynamic scaling
of workers, we recommend reading the `dask-jobqueue documentation
<https://jobqueue.dask.org>`_ first to get a basic system running and then
returning to this documentation for fine-tuning if necessary.


Using MPI
---------

You can launch a Dask cluster using ``mpirun`` or ``mpiexec`` and the
`dask-mpi <http://mpi.dask.org/en/latest/>`_ command line tool.

.. code-block:: bash

   mpirun --np 4 dask-mpi --scheduler-file /home/$USER/scheduler.json

.. code-block:: python

   from dask.distributed import Client
   client = Client(scheduler_file='/path/to/scheduler.json')

This depends on the `mpi4py <https://mpi4py.readthedocs.io/>`_ library.  It only
uses MPI to start the Dask cluster and not for inter-node communication. MPI
implementations differ: the use of ``mpirun --np 4`` is specific to the
``mpich`` or ``open-mpi`` MPI implementation installed through conda and linked
to mpi4py.

.. code-block:: bash

   conda install mpi4py

It is not necessary to use exactly this implementation, but you may want to
verify that your ``mpi4py`` Python library is linked against the proper
``mpirun/mpiexec`` executable and that the flags used (like ``--np 4``) are
correct for your system.  The system administrator of your cluster should be
very familiar with these concerns and able to help.

In some setups, MPI processes are not allowed to fork other processes. In this
case, we recommend using ``--no-nanny`` option in order to prevent dask from
using an additional nanny process to manage workers.

Run ``dask-mpi --help`` to see more options for the ``dask-mpi`` command.


Using a Shared Network File System and a Job Scheduler
------------------------------------------------------

.. note:: This section is not necessary if you use a tool like dask-jobqueue.

Some clusters benefit from a shared File System (NFS, GPFS, Lustre or alike),
and can use this to communicate the scheduler location to the workers::

   dask-scheduler --scheduler-file /path/to/scheduler.json  # writes address to file

   dask-worker --scheduler-file /path/to/scheduler.json  # reads file for address
   dask-worker --scheduler-file /path/to/scheduler.json  # reads file for address

.. code-block:: python

   >>> client = Client(scheduler_file='/path/to/scheduler.json')

This can be particularly useful when deploying ``dask-scheduler`` and
``dask-worker`` processes using a job scheduler like
SGE/SLURM/Torque/etc.  Here is an example using SGE's ``qsub`` command::

    # Start a dask-scheduler somewhere and write the connection information to a file
    qsub -b y /path/to/dask-scheduler --scheduler-file /home/$USER/scheduler.json

    # Start 100 dask-worker processes in an array job pointing to the same file
    qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /home/$USER/scheduler.json

Note, the ``--scheduler-file`` option is *only* valuable if your scheduler and
workers share a network file system.


High Performance Network
------------------------

Many HPC systems have both standard Ethernet networks as well as
high-performance networks capable of increased bandwidth.  You can instruct
Dask to use the high-performance network interface by using the ``--interface``
keyword with the ``dask-worker``, ``dask-scheduler``, or ``dask-mpi`` commands or
the ``interface=`` keyword with the dask-jobqueue ``Cluster`` objects:

.. code-block:: bash

   mpirun --np 4 dask-mpi --scheduler-file /home/$USER/scheduler.json --interface ib0

In the code example above, we have assumed that your cluster has an Infiniband
network interface called ``ib0``. You can check this by asking your system
administrator or by inspecting the output of ``ifconfig``

.. code-block:: bash

	$ ifconfig
	lo          Link encap:Local Loopback                       # Localhost
				inet addr:127.0.0.1  Mask:255.0.0.0
				inet6 addr: ::1/128 Scope:Host
	eth0        Link encap:Ethernet  HWaddr XX:XX:XX:XX:XX:XX   # Ethernet
				inet addr:192.168.0.101
				...
	ib0         Link encap:Infiniband                           # Fast InfiniBand
				inet addr:172.42.0.101

https://stackoverflow.com/questions/43881157/how-do-i-use-an-infiniband-network-with-dask


Local Storage
-------------

Users often exceed memory limits available to a specific Dask deployment.  In
normal operation, Dask spills excess data to disk, often to the default
temporary directory.

However, in HPC systems this default temporary directory may point to an
network file system (NFS) mount which can cause problems as Dask tries to read
and write many small files.  *Beware, reading and writing many tiny files from
many distributed processes is a good way to shut down a national
supercomputer*.

If available, it's good practice to point Dask workers to local storage, or
hard drives that are physically on each node.  Your IT administrators will be
able to point you to these locations.  You can do this with the
``--local-directory`` or ``local_directory=`` keyword in the ``dask-worker``
command::

   dask-mpi ... --local-directory /path/to/local/storage

or any of the other Dask Setup utilities, or by specifying the
following :doc:`configuration value <../../configuration>`:

.. code-block:: yaml

   temporary-directory: /path/to/local/storage

However, not all HPC systems have local storage.  If this is the case then you
may want to turn off Dask's ability to spill to disk altogether.  See `this
page <https://distributed.dask.org/en/latest/worker.html#memory-management>`_
for more information on Dask's memory policies.  Consider changing the
following values in your ``~/.config/dask/distributed.yaml`` file to disable
spilling data to disk:

.. code-block:: yaml

   distributed:
     worker:
       memory:
         target: false  # don't spill to disk
         spill: false  # don't spill to disk
         pause: 0.80  # pause execution at 80% memory use
         terminate: 0.95  # restart the worker at 95% use

This stops Dask workers from spilling to disk, and instead relies entirely on
mechanisms to stop them from processing when they reach memory limits.

As a reminder, you can set the memory limit for a worker using the
``--memory-limit`` keyword::

   dask-mpi ... --memory-limit 10GB


Launch Many Small Jobs
----------------------

.. note:: This section is not necessary if you use a tool like dask-jobqueue.

HPC job schedulers are optimized for large monolithic jobs with many nodes that
all need to run as a group at the same time.  Dask jobs can be quite a bit more
flexible: workers can come and go without strongly affecting the job.  If we
split our job into many smaller jobs, we can often get through the job
scheduling queue much more quickly than a typical job.  This is particularly
valuable when we want to get started right away and interact with a Jupyter
notebook session rather than waiting for hours for a suitable allocation block
to become free.

So, to get a large cluster quickly, we recommend allocating a dask-scheduler
process on one node with a modest wall time (the intended time of your session)
and then allocating many small single-node dask-worker jobs with shorter wall
times (perhaps 30 minutes) that can easily squeeze into extra space in the job
scheduler.  As you need more computation, you can add more of these single-node
jobs or let them expire.


Use Dask to co-launch a Jupyter server
--------------------------------------

Dask can help you by launching other services alongside it.  For example, you
can run a Jupyter notebook server on the machine running the ``dask-scheduler``
process with the following commands

.. code-block:: python

   from dask.distributed import Client
   client = Client(scheduler_file='scheduler.json')

   import socket
   host = client.run_on_scheduler(socket.gethostname)

   def start_jlab(dask_scheduler):
       import subprocess
       proc = subprocess.Popen(['/path/to/jupyter', 'lab', '--ip', host, '--no-browser'])
       dask_scheduler.jlab_proc = proc

   client.run_on_scheduler(start_jlab)