This work is supported by Continuum Analytics the XDATA Program and the Data Driven Discovery Initiative from the Moore Foundation

To increase transparency I’m blogging weekly about the work done on Dask and related projects during the previous week. This log covers work done between 2017-01-17 and 2016-01-30. Nothing here is ready for production. This blogpost is written in haste, so refined polish should not be expected.

Themes of the last couple of weeks:

1. Micro-release of distributed scheduler
2. as_completed for asynchronous algorithms
3. Testing ZeroMQ and communication refactor

### Stability enhancements and micro-release

We’ve released dask.distributed version 1.15.2, which includes some important performance improvements for communicating multi-dimensional arrays, cleaner scheduler shutdown of workers for adaptive deployments, an improved as_completed iterator that can accept new futures in flight, and a few other smaller features.

The feature here that excites me the most is improved communication of multi-dimensional arrays across the network. In a recent blogpost about image processing on a cluster we noticed that communication bandwidth was far lower than expected. This led us to uncover a flaw in our compression heuristics. Dask doesn’t compress all data, instead it takes a few 10kB samples of the data, compresses them, and if that goes well, decides to compress the entire thing. Unfortunately, due to our mishandling of memoryviews we ended up taking much larger samples than 1kB when dealing with multi-dimensional arrays.

### XArray release

This improvement is particularly timely because a new release of XArray (a project that wraps around Dask.array for large labeled arrays) is now available with better data ingestion support for NetCDF data on distributed clusters. This opens up distributed array computing to Dask’s first (and possibly largest) scientific user community, atmospheric scientists.

### as_completed accepts futures.

In addition to arrays, dataframes, and the delayed decorator, Dask.distributed also implements the concurrent.futures interface from the standard library (except that Dask’s version parallelizes across a cluster and has a few other benefits). Part of this interface is the as_completed function, which takes in a list of futures and yields those futures in the order in which they finish. This enables the construction of fairly responsive asynchronous computations. As soon as some work finishes you can look at the result and submit more work based on the current state.

That has been around in Dask for some time.

What’s new is that you can now push more futures into as_completed

futures = client.map(my_function, sequence)

ac = as_completed(futures)
for future in ac:
result = future.result()  # future is already finished, so this is fast
if condition:
new_future = client.submit(function, *args, **kwargs)
ac.add(new_future)    # <<---- This is the new ability


So the as_completed iterator can keep going for quite a while with a set of futures always in flight. This relatively simple change allows for the easy expression of a broad set of asynchronous algorithms.

### ZeroMQ and Communication Refactor

As part of a large effort, Antoine Pitrou has been refactoring Dask’s communication system. One sub-goal of this refactor was to allow us to explore other transport mechanisms than Tornado IOStreams in a pluggable way.

One such alternative is ZeroMQ sockets. We’ve gotten both incredibly positive and incredibly negative praise/warnings about using ZeroMQ. It’s not a great fit because Dask mostly just does point-to-point communication, so we don’t benefit from all of ZeroMQ’s patterns, which now become more of a hindrance than a benefit. However, we were interested in the performance impact of managing all of our network communication in a completely separately managed C++ thread unaffected by GIL issues.

Whether or you hate or love ZeroMQ you can now pick and choose. Antoine’s branch allows for easy swapping between transport mechanisms and opens the possibility for more in the future like intra-process communication with Queues, MPI, etc.. This doesn’t affect most users, but some Dask deployments are on supercomputers with exotic networks capable of very fast speeds. The possibility that we might tap into Infiniband someday and have the ability to manage data locally without copies (Tornado does not achieve this) is very attractive to some user communities.

After very preliminary benchmarking we’ve found that ZeroMQ offers a small speedup, but results in a lack of stability in the cluster under complex workloads (likely our fault, not ZeroMQs, but still). ZeroMQ support is strictly experimental and will likely stay that way for a long time. Readers should not get excited about this.

In collaboration with researchers at Capital One we’ve been working on a set of parallel solvers for first and second order methods, such as are commonly used in a broad class of statistical and machine learning algorithms.

One challenge in this process has been building algorithms that are simultaneously optimal for both the single machine and distributed schedulers. The distributeed scheduler requires that we think about where data is, on the client or on the cluster, where for the single machine scheudler this is less of a problem. The distrbuted scheduler appropriately has a new verb, persist, which keeps data as a Dask collection, but triggers all of the internal computation

compute(dask array) -> numpy array


We have now mirrored this verb to the single machine scheduler in dask/dask #1927 and we get very nice performance on dask-glm’s algorithms in both cases now.

Working with the developers at Capital One has been very rewarding. I would like to find more machine learning groups that fit the following criteria:

1. Are focused on performance
2. Need parallel computing
3. Are committed to built good open source software
4. Are sufficiently expert in their field to understand correct algorithms

If you know such a person or such a group, either in industry or just a grad student in university, please encourage them to raise an issue at http://github.com/dask/dask/issues/new . I will likely write a larger blogpost on this topic in the near future.