Efficiency — Anaconda Enterprise 4.4 documentation

Anaconda Enterprise: Documentation

# Efficiency

Parallel computing done well is responsive and rewarding. However, several speed-bumps can get in the way. This section describes common ways to ensure performance.

## Leave data on the cluster

Wait as long as possible to gather data locally. If you want to ask a question of a large piece of data on the cluster it is often faster to submit a function onto that data then to bring the data down to your local computer.

For example if we have a numpy array on the cluster and we want to know its shape we might choose one of the following options:

1. Slow: Gather the numpy array to the local process, access the `.shape` attribute
2. Fast: Send a lambda function up to the cluster to compute the shape

    >>> x = client.submit(np.random.random, (1000, 1000))
    >>> type(x)
    Future

Slow

    >>> x.result().shape()  # Slow from lots of data transfer
    (1000, 1000)

Fast

    >>> client.submit(lambda a: a.shape, x).result()  # fast
    (1000, 1000)

## Use larger tasks

The scheduler adds about one millisecond of overhead per task or Future object. While this may sound fast it’s quite slow if you run a billion tasks. If your functions run faster than 100ms or so then you might not see any speedup from using distributed computing.

A common solution is to batch your input into larger chunks.

Slow

    >>> futures = client.map(f, seq)
    >>> len(futures)  # avoid large numbers of futures
    1000000000

Fast

    >>> def f_many(chunk):
    ...     return [f(x) for x in chunk]

    >>> from toolz import partition_all
    >>> chunks = partition_all(1000000, seq)  # Collect into groups of size 1000

    >>> futures = client.map(f_many, chunks)
    >>> len(futures)  # Compute on larger pieces of your data at once
    1000

## Adjust between Threads and Processes

By default a single `Worker` runs many computations in parallel using as many threads as your compute node has cores. When using pure Python functions this may not be optimal and you may instead want to run several separate worker processes on each node, each using one thread. When configuring your cluster you may want to use the options to the `dask-worker` executable as follows:

    $ dask-worker ip:port --nprocs 8 --nthreads 1

Note that if you’re primarily using NumPy, Pandas, SciPy, Scikit Learn, Numba, or other C/Fortran/LLVM/Cython-accelerated libraries then this is not an issue for you. Your code is likely optimal for use with multi-threading.

## Don’t go distributed

Consider the dask and concurrent.futures modules, which have similar APIs to distributed but operate on a single machine. It may be that your problem performs well enough on a laptop or large workstation.

Consider accelerating your code through other means than parallelism. Better algorithms, data structures, storage formats, or just a little bit of C/Fortran/Numba code might be enough to give you the 10x speed boost that you’re looking for. Parallelism and distributed computing are expensive ways to accelerate your application.