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Improving performance

Once you start creating larger models, Nengo might become a little bit slow. This section walks you through different things that might help you to get the best performance out of Nengo.

In a nutshell

To improve build time

  1. Set a seed on the top level model to enable decoder caching:

net = nengo.Network(seed=1)
  1. Disable the operator graph optimizer:

with nengo.Simulator(net, optimize=False) as sim:
  1. Reduce the number of neurons in very large ensembles, or consider using the RandomizedSVD solver.

To improve run time

  1. Enable the operator graph optimizer (and install SciPy):

with nengo.Simulator(net, optimize=True) as sim:
  1. Consider switching to the nengo_ocl backend if you have a powerful GPU.

To lower peak memory consumption

  1. Disable the operator graph optimizer:

with nengo.Simulator(net, optimize=False) as sim:
  1. Reduce the number of neurons in very large ensembles. Consider replacing them with multiple smaller ensembles (EnsembleArray is useful for that).

  2. Reduce the number of probes or their sampling intervals (with the sample_every argument).

Build and run time performance

The two main determiners of how long your model takes to run are the build time and the run time. Build time is the time required to translate the model description into the actual neural network that gets simulated. A build happens when you create the simulator with nengo.Simulator(net). Run time is how long it takes to simulate this network for the desired amount of simulation time. A run happens when you call

Some of the techniques described below will influence one of these variables, while others will reduce one variable at the cost of increasing another. While the run time is usually the most important variable, sometimes the memory consumption can be the main problem.

Getting the best performance for your model depends on your model and your computing environment.

Decoder caching

Influences build time.

A significant amount of build time is spent on finding the NEF decoders. Because of that, it is possible to cache the decoders. The first build of a model will still take about the same time (technically a bit longer because the computed decoders will be stored), but all subsequent builds of the same model can load the stored decoders and will be faster.

To enable the decoder caching, set a seed on the network like so:

with nengo.Network(seed=1) as net:

There are a few configuration options for more advanced control of the cache. The most important might be the possibility to control the path where the cache files are stored. On high performance clusters, certain file systems might provide better performance.

Operator graph optimizer

Influences build time, run time, and memory consumption.

By default, Nengo optimizes its internal data structures (the “operator graph”) to access memory in a linear manner. However, this can increase build time significantly and in some cases it can be better to turn this optimization off to speed up the build at the cost of slowing the run. To turn the optimizer off, set the simulator’s optimize argument to False:

with nengo.Simulator(net, optimize=False) as sim:

Another situation where it is helpful to disable the optimizer is when the peak memory usage is too high. The optimizer can use up to three times as much memory as would be required without the optimizer. Note that limiting the number of optimization passes does not noticeably reduce memory consumption.


Influences run time.

To gain the maximum performance gain from the operator graph optimizer, install SciPy. When the operator graph optimizer is deactivated, installing SciPy has no effect on performance.


Improves run time.

If you have a powerful GPU, you have the option to switch to the nengo_ocl backend. It will utilize your GPU, which is optimized for the sorts of calculations done by Nengo. Build times with nengo_ocl are usually comparable to Nengo, but run times can be significantly faster.

Model structure

Influences build time, run time, and memory consumption.

Some aspects of the model structure, apart from the size of the model, influence performance. Ensembles with many neurons will take a long time to build and consume a lot of memory. Sometimes it is feasible to split large ensembles into multiple smaller ensembles (the EnsembleArray is helpful for that). Alternatively, using the RandomizedSVD decoder solver can reduce the build time.

However, be aware that many small ensembles will take longer to simulate if the operator graph optimizer is deactivated.

Limiting probed data

Influences memory consumption.

All data that gets probed in the model has to be stored in memory. Depending on how long the simulation runs and how many things are probed, this might consume a significant amount of memory. By reducing the number of probed objects, the memory consumption can be reduced. An alternative is to not record a value for every time step. Probes accept a sample_every= argument to reduce the number of recorded samples.

Note that in most cases, probing data does not noticeably affect run time.