# Precise Control of Timing¶

Shady pushes most of the computational burden of drawing onto the graphics processor. The few remaining necessary CPU operations are performed by an engine, the default implementation of which is an “accelerated” binary written in C++. This provides a significant boost in performance over Python-based engine implementations. Unfortunately, resources are still limited and it is perfectly possible to overload Shady and cause its animations fail to keep up with real time. Shady animations tend to be accurate in the long term, because by design they are all functions of “wall time” rather than frame number or frame-to-frame “delta”. But an excessive amount of Shady content (or load on the GPU and CPU from other sources) will make your animations “skip” frames and hence become uneven in time, and eventually reduce your effective frame rate. Even rare “frame skips” may be problematic in certain scenarios (e.g. if you are conducting motion perception experiments) because of the temporally-broad-band artifacts they introduce.

This document outlines the principal hazards for Shady timing performance, and describes some tips for avoiding them where possible.

## General system settings¶

• Configure the desired frame rate on the display you will use for Shady. (e.g. on Windows 10: right-click on Desktop -> Display settings -> Display adapter properties -> “Monitor” tab -> Refresh rate). At the same time, take the opportunity to ensure your screen resolution is set to maximum (this is important for displays that consist of discrete physical pixels, but does not apply if you are using a cathode ray tube display).
• If there is a manufacturer-specific control panel for your graphics card, (for example, “NVidia Control Panel”) then it may expose “vertical sync” as an option (possibly also called “vertical blanking” or “VBL”). Ensure that this is enabled.
• (Windows) If you have multiple displays, ensure that the screen Shady will appear on is selected as the “Main display” in your display settings. We’ve found this is particularly important when running different screens using different graphics cards, as it ensures that the correct display’s intrinsic frame rate is respected. (This does unfortunately mean that the taskbar will need to be on the same screen as Shady.)
• Like most applications, Shady runs more smoothly when its window is in the foreground and fills the screen. For timing-critical applications, do not use a custom window size or a window frame if you want the best performance, and always make sure Shady is the focused window (this is easy to forget when working with the interactive console).

## Vertical sync issues¶

• As per above, ensure that this setting is enabled if your manufacturer-specific graphics card settings expose it (as in, for example, the “NVidia Control Panel”).
• Use the .SetSwapInterval() method if you need to force a Shady.World to slow down its frame rate (e.g. to drop from 60 to 30 frames per second while remaining precisely regular). Note that this only works with the “accelerated” engine and only on some platforms/GPUs—see the docstring for SetSwapInterval().
• Use Shady’s “tearing test” to check that vertical sync is working: if parts of the vertical stripe appear out of sync with the rest, or you can perceive torn or ragged edges, vertical sync is probably not functioning. You can launch the test from the command line with python -m Shady tearing or from inside Python with Shady.TearingTest( world ).

## Optimization¶

Though it may seem obvious to say it, you can save time between frames by optimizing any Python code you are using to perform animation updates, aiming to make it maximally efficient. Ensure that your numerical array operations are vectorized, that you’re not reinventing wheels that Shady can do in its accelerated engine or on the GPU (e.g. texture color/contrast modulation using a spatial function), and that you are not computing things unnecessarily. The demo dots2 allows you to compare three different implementations of the same multi-stimulus animation, and to observe that that numpy vectorization (“batch” mode) significantly improves timing performance.

Below are some more specific tips for analyzing and improving your code.

### Diagnostic tools¶

Frame interval gauge:

The Shady.FrameIntervalGauge() stimulus shows your frame-to-frame interval graphically in real time. Each minor grid unit represents 1 millisecond, and the red lines are spaced every 10 milliseconds. Ideally, the gauge should hover around 1000/f milliseconds, where f is your optimal frame rate (e.g. ~16.7 ms for a 60 Hz display). Watch out for spikes, which indicate transient performance drops (frame skips). If these spikes occur unpredictably, check that your operating system hasn’t decided to start a system process behind the scenes (Windows is particularly obnoxious about this). The gauge itself will have a tiny effect on your performance (far less than a text display would, in Shady—see below).

NB: the frame interval gauge requires the third-party package numpy

Post-hoc timing plots:

Use the Shady.PlotTimings() method to show a history of Shady’s frame-to-frame intervals, along with optional additional information about the timings of specific aspects of the engine and individual stimuli. The information is enriched if the debugTiming attribute is set to True for your World and Stimulus instances. You can also plot timings from a Shady log file with Shady.Utilities.PlotTimings(logfilename)—or the same thing can be invoked from outside Python with python -m Shady timings LOGFILENAME.

NB: timing plots require the third-party packages numpy and matplotlib.

Unsurprisingly, drawing too many stimuli at once will slow you down— particularly when they overlap, which will require blending operations and/or cause Shady to draw the same pixels multiple times. Keep in mind that drawing a few large stimuli is faster than drawing more numerous smaller stimuli, even if the total number of pixels is the same, due to the parallel architecture of GPU computations. If you have reached your system’s draw limit, consider whether you could:

• combine multiple linked stimuli into one larger stimulus;
• disable stimuli whenever you expect them to be out of sight—you can disable rendering by setting visible=False, or disable rendering and inter- frame property updates by calling Leave();
• use other Shady tricks (such as property sharing) to reduce the number of update computations per frame;
• use a single multi-shape Stimulus (as exemplified in the demos dots1, dots3 and dots4) rather than multiple separate Stimulus instances (as in dots2).
Text properties:
Beware of changing the text properties of your stimuli too often if using Shady.Text functionality. Shady is smart enough that it does not re- compute the pixel values of a text stimulus unless there is an actual de-facto change to the text content or style. However, when this does occur, Shady must re-render the texture on the CPU and send the result to the GPU. This is out of step with Shady’s usual approach of doing all pixel processing on the GPU, and is almost as expensive as creating a new stimulus every time you change the text content. One workaround for rapidly switching between a number of pre-determined text objects is to create them in advance, each as a separate page of the same Stimulus instance, and switch between them.
Video file playback:
Similarly, be mindful of the cost of video playback using Shady.Video. If you’re working from a video file, each new frame must be decoded on the CPU. In addition, regardless of whether it came from a file or from a live camera, the frame must be sent from CPU to GPU. Unfortunately, we currently have no workaround for this other than rendering the particular frames you need as a multi-frame Stimulus, and this may not be feasible for long videos. Lowering the video file’s resolution, and/or restricting the video.aperture, may reduce the impact on performance somewhat.
External operations:

Be mindful of the impact of concurrent non-Shady CPU and GPU operations, both inside and outside of Python.

If a significant number of concurrent operations are being performed inside the same instance of Python that is running Shady, you should read the note at the end of Shady.Documentation.Concurrency about multi-threading in Python—the short version is that you may really want to run multiple processes of Python, and give Shady its own process, rather than using Python’s controversial (arguably illusory) multi-threading.

## Order of Operations¶

In certain advanced setups, you may wish to configure your system to respond to the physical state of the screen (for example, by making your Python code wait until a stimulus has triggered a light sensor stuck to the screen). In such cases, you may need to know that Shady’s engine calls your Python callbacks to prepare frame n+1 before the preceding frame n has been physically displayed. The explanation is as follows.

At its simplest level of abstraction, the main Shady loop consists of three operations. Since these are performed in a never-ending cycle, this means that (up to cyclical reordering) there are two potential orders in which to do them:

Order A:

• A1: Prepare parameters (Python callback)
• A2: Send draw commands to GPU
• A3: Display completed frame (swap buffers)

Order B:

• B1: Send draw commands to GPU
• B2: Prepare parameters (Python callback)
• B3: Display completed frame (swap buffers)

While it may seem more intuitive to use order A, Shady actually uses order B. This is for reasons of efficiency. Sending draw comments (B1) takes very little time on the CPU, but it initiates a series of potentially time-consuming operations on the GPU. Displaying the completed frame (B3) must wait until the GPU has finished. What should we be doing on the CPU in the meantime, while the GPU is busy? The most efficient use of resources is to have both CPU and GPU busy at the same time. Hence, the Python callback is called while the GPU is busy drawing, but before the frame is displayed. Therefore, the cycle looks like this:

• B1: Send draw commands to GPU for frame 0
• B2: Prepare parameters for frame 1
• B3: Display frame 0
• B1: Send draw commands to GPU for frame 1
• B2: Prepare parameters for frame 2
• B3: Display frame 1
• B1: Send draw commands to GPU for frame 2
• B2: Prepare parameters for frame 3
• B3: Display frame 2

This means that the Python callback (B2) for frame n+1 must return before you get to physically see frame n. Provided your callbacks meet their deadlines, that’s usually no problem, but if you specifically intervene to make a callback return late, or to wait for a physical light signal that is supposed to originate on frame n, then you will see strange effects. The more intuitive ordering:

• A1: Prepare parameters for frame 0
• A2: Send draw commands to GPU for frame 0
• A3: Display frame 0
• A1: Prepare parameters for frame 1
• A2: Send draw commands to GPU for frame 1
• A3: Display frame 1
• A1: Prepare parameters for frame 2
• A2: Send draw commands to GPU for frame 2
• A3: Display frame 2

…is less efficient: on each frame, we now have to wait for the Python callback (A1) to finish before we can even start (A2) the GPU operations. CPU and GPU are now operating in series, not in parallel.

In fact, the assumption we made earlier, i.e. that the “swap buffers” operation (A3/B3) is synchronous and only returns once the frame is fully composed and swapped into visibility, is only true for some graphics cards/drivers. For others, the CPU’s “swap buffers” call merely adds yet another instruction to the GPU’s queue, then returns immediately. In the latter case, order A could in principle be just as efficient as order B (of course, at some point the CPU’s OpenGL API calls must wait for the GPU to catch up, and presumably that would happen, at latest, during the “send draw commands” stage for the subsequent frame, but so far it’s unclear to us whether in general there is a consistent moment or operation at which this is guaranteed to happen). However, because of the synchronous behaviour on some graphics cards, Shady’s strategy is to try to make this behaviour uniform across as many graphics cards as possible, by forcing A3/B3 to wait synchronously for the vertical blanking interrupt. So the accelerated Shady engine actually has explicit code for waiting until the frame has been displayed, and this works on several platforms such as macOS and NVidia-on-Windows (but can still fail on a few—mostly on Linux in our experience so far). An advantage of this approach is that it makes timing diagnostics easier to interpret (the CPU can measure a clear “time zero” for every frame), and the disadvantages are negligible provided we stick to order B.