Iris 2 CPU Optimization

April 29, 2016

Iris 2 is a powerful, one-of-a-kind tool that pairs high-quality digital signal processing for resampling, modeling, and effects along with remarkably flexible modulation options, resulting in a lot of complex math and data streaming that needs to happen quickly. This can tax your computer’s resources substantially, but here are some recommendations for maximizing Iris 2 performance on your machine:

 

Freezing or Bouncing Tracks

Freezing or bouncing your Iris 2 tracks offers you the most substantial gains in CPU performance. Check your audio editor’s user manual for instructions on how to do this.

 

Hide the UI

Iris 2 offers real-time metering for numerous parameters including modulation dots around modulatable knobs, morphing wavetable shapes, playheads moving across the spectrogram, and a spectrum analyzer. These can be resource-intensive. Hiding the Iris 2 UI during playback can conserve 2-3% of CPU processing power.

 

Buffer size

Set the buffer size of your audio editor (or the standalone Iris 2 application) to a higher amount (2,048 samples is recommended) during playback. This will increase latency during recording, but should only make things better during playback and mixing.

 

Avoid the most resource intensive filters

The New York and Tokyo Filter models can use up to 4% more than other filter types.

 

Lower your voice count

As more voices play back simultaneously (for example, if you’re holding down eight notes at once), the more of an impact this has on your CPU performance. Reduce voice count to only what you anticipate needing. For example, try to use Mono mode for bass or lead parts where you will not need polyphony, so that envelope releases silence previous voices.

 

Reduce envelope release times

Longer envelope release times mean more voices continue to play after you have released a key, and this will increase CPU usage.

 

Reduce sample distance from original pitch

Both Iris 2 algorithms (Radius RT and the Resampler) perform best within an octave of the original root note. The further away from the root note you get, the higher the impact on CPU performance.

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