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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

Using a Shared Network File System and a Job Scheduler

Some clusters benefit from a shared network file system (NFS) 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

>>> 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 connection information to 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.

Using MPI

You can launch a Dask network using mpirun or mpiexec and the dask-mpi command line executable.

mpirun --np 4 dask-mpi --scheduler-file /home/$USER/scheduler.json
from dask.distributed import Client
client = Client(scheduler_file='/path/to/scheduler.json')

This depends on the mpi4py 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 MPI implementation installed through conda and linked to mpi4py

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.

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

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 to the dask-worker, dask-scheduler, or dask-mpi commands

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

$ 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

No Local Storage

Users often exceed memory limits available to a specific Dask deployment. In normal operation Dask spills excess data to disk. However, in HPC systems the individual compute nodes often lack locally attached storage, preferring instead to store data in a robust high performance network storage solution. As a result when a Dask cluster starts to exceed memory limits its workers can start making many small writes to the remote network file system. This is both inefficient (small writes to a network file system are much slower than local storage for this use case) and potentially dangerous to the file system itself.

See this page for more information on Dask's memory policies. Consider changing the following values to your ~/.dask/config.yaml file

# Fractions of worker memory at which we take action to avoid memory blowup
# Set any of the lower three values to False to turn off the behavior entirely
worker-memory-target: false  # don't spill to disk
worker-memory-spill: false  # don't spill to disk
worker-memory-pause: 0.80  # fraction at which we pause worker threads
worker-memory-terminate: 0.95  # fraction at which we terminate the worker

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

Alternatively if you do have local storage mounted on your compute nodes you can point Dask workers to use a particular location in your filesystem using the --local-directory keyword:

dask-mpi ... --local-directory /scratch

Launch Many Small Jobs

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. So if we separate 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

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)

Concrete Example with PBS

The Pangeo project maintains instructions on how to deploy Dask on various HPC systems maintained by NCAR using the PBS job scheduler. Their more concrete instructions may not apply to your situation in particular, but it may be helpful to see a full solution.