yt Testing and Parallelism
@ Sam Skillman | Monday, Oct 24, 2011 | 4 minute read | Update at Monday, Oct 24, 2011

A few of us worked this past week on a couple yt projects and made what we think is significant progress. Two of the items we focused on were testing and parallelism.

For testing, we’ve broadened the test suite to include many more functions and derived quantities. We now have 548 tests that include (off and on-axis) slices, (off and on- axis) projections, phase distributions, halo finding, volume rendering, and geometrical region cuts such as rectangular solids, spheres, and disks. We use both plain and derived fields for these tests so that it covers as many bases as possible. With this framework, we are now able to keep a gold standard of the test results for any dataset, then test later changes against this standard. These tests can test for bitwise identicality or allow for some tolerance. For a full list of tests, you can run python yt/tests/ -l, and use --help to look at the usage. We will soon be updating the documentation to provide more information on how to set up the testing framework, but I think all of us agree that this will make it much easier to test our changes to make sure bugs have not crept in.

The second big change I’d like to talk about is the way we now handle parallelism in yt. Previously, methods that employed parallelism through MPI calls would first inherit from ParallelAnalysisInterface, which had access to a ton of mpi functions that all work off of MPI.COMM_WORLD. In our revamp we wanted to accomplish two things: 1) merge duplicate mpi calls that were only different by the type of values they work on and do overall cleanup. 2) Allow for nested levels of parallelism where two (or more) separate communicators are able to use barriers and collective operations such as allreduce. To do this, we worked in a two-step process. First we took things like:

def _mpi_allsum(self, data):
def _mpi_Allsum_double(self, data):
def _mpi_Allsum_long(self, data):
def _mpi_allmax(self, data):
def _mpi_allmin(self, data):

and packed it into a single function:

def mpi_allreduce(self, data, dtype=None, op='sum'):

When a numpy array is passed to this new mpi_allreduce, dtype is determined from the array properties. If the data is a dictionary, then it is passed to mpi4py’s allreduce function that acts on dictionaries. This greatly reduced the number of lines in parallel_analysis_interface (1376 to 915), even after adding in additional functionality.

The second step was bundling all of these functions into a new class called Communicator. This Communicator object is initialized with an MPI communicator that no longer is restricted to COMM_WORLD. Using this as the fundamental MPI object, we then built a CommunicationSystem object that manages these communicators. A global communication_system instance is created, that is initialized with COMM_WORLD at the top of the system if the environment is mpi4py-capable. If not, an empty communicator is created that has passthroughs for all the mpi functions.

Using this new framework we are now able to take advantage of multiple communicators. There are two use cases that we have implemented so far:

Parallel Objects

parallel_objects is a method in for iterating over a set of objects such that a group of processors work on each object. This could be used, for example, to run N projections each with M processors, allowing for a parallelism of NxM.


workgoups allows users to set up multiple MPI communicators with a non-uniform number of processors to each work on a separate task. This capability lives within the ProcessorPool and Workgroup objects in

These are just the first two that we tried out and we are very excited about the new possibilities.

With these changes, there was one implementation change that has already come up once in the mailing list. When you implement a new class that you’d like to have access to the communication objects, you must first inherit ParallelAnalysisInterface, and then make sure that __init__ makes a call to: ParallelAnalysisInterface.__init__()

At that point, your new class will have access to the mpi calls through the self.comm object. For example, to perform a reduction one would do:

self.comm.mpi_allreduce(my_data, op='sum')

As I said before, this will be documented soon, but hopefully this will help for now.

Sam, Britton, Cameron, Jeff, and Matt

yt extension modules

yt has many extension packages to help you in your scientific workflow! Check these out, or create your own.


ytini is set of tools and tutorials for using yt as a tool inside the 3D visual effects software Houdini or a data pre-processor externally to Houdini.


Trident is a full-featured tool that projects arbitrary sightlines through astrophysical hydrodynamics simulations for generating mock spectral observations of the IGM and CGM.


pyXSIM is a Python package for simulating X-ray observations from astrophysical sources.


Analyze merger tree data from multiple sources. It’s yt for merger trees!


yt_idv is a package for interactive volume rendering with yt! It provides interactive visualization using OpenGL for datasets loaded in yt. It is written to provide both scripting and interactive access.


widgyts is a jupyter widgets extension for yt, backed by rust/webassembly to allow for browser-based, interactive exploration of data from yt.


yt_astro_analysis is the yt extension package for astrophysical analysis.

Make your own!!

Finally, check out our development docs on writing your own yt extensions!

Contributing to the Blog

Are you interested in contributing to the yt blog?

Check out our post on contributing to the blog for a guide!

We welcome contributions from all members of the yt community. Feel free to reach out if you need any help.

the yt data hub

The yt hub at has a ton of resources to check out, whether you have yt installed or not.

The collections host all sorts of data that can be loaded with yt. Some have been used in publications, and others are used as sample frontend data for yt. Maybe there’s data from your simulation software?

The rafts host the yt quickstart notebooks, where you can interact with yt in the browser, without needing to install it locally. Check out some of the other rafts too, like the widgyts release notebooks – a demo of the widgyts yt extension pacakge; or the notebooks from the CCA workshop – a user’s workshop on using yt.

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