The FTI (Frame Time Interpreter) from igor’sLAB
And yet this still doesn’t generate chart graphics. This is precisely why I have programmed quite flexible software for internal use, which has been able to evaluate the log files from FrameView correctly and appropriately for all my charts for years. Today I would like to explain and justify why an important distinction needs to be made here. While most tools such as CapFrameX still use the MsBetweenPresents metric, you simply have to move with the times. Because PresentMon offers much more in the log files than just the pure render time of the GPU. I use two metrics here for good reason and I can only hope that all the tools will also follow suit with the introduction of NVIDIA’s MFG at the latest, because a lot of things weren’t quite correct even today. The picture below shows my in-house software, which then dutifully fills my Excel charts card by card (which I’ll show you in a moment):
The metrics MsBetweenPresents and MsBetweenDisplayChange are two important metrics in the area of graphics processing unit (GPU) performance evaluation and play a central role in analyzing image output processes and their impact on the user experience. To explain the difference between these two metrics in detail, it is necessary to look at the entire frame generation and display process, from the processing of a frame by the GPU to the actual display on the screen.
MsBetweenPresents: Time between two frame presentations
The MsBetweenPresents metric measures the time between two consecutive presentations of a frame by the GPU. This occurs when the GPU receives a new image for processing and transfers it to the render queue. The metric is an indicator of the frequency with which the application creates frames and provides them to the GPU. These are the values I use to evaluate the average FPS. Let’s take a look at this mode in my software and note the results:
MsBetweenPresents features:
- Focus on GPU queue: the metric measures the time at which a new frame is passed for processing, regardless of when it is actually displayed.
- Application performance: MsBetweenPresents reflects the efficiency of the render pipeline and how quickly the application is able to produce and pass frames to the GPU.
- Suitability for CPU/GPU analysis: This metric is particularly useful for identifying bottlenecks caused by the application or the GPU queue. For example, if the application is delivering frames too slowly, MsBetweenPresents will increase even if the GPU is performing well.
Limitation of MsBetweenPresents: MsBetweenPresents does not take into account when a frame actually becomes visible on the screen. This means that this metric does not provide any information about the synchronization with the display or the perceived fluidity by the user. It is therefore more focused on analyzing the render pipeline.
MsBetweenDisplayChange: Time between actual image changes
The MsBetweenDisplayChange metric captures the time between two visible changes of the displayed image content on the screen. This is the moment when the display actually outputs a new image that has been finalized by the GPU. I use this value for the variances and the P1 Low (including my percentile curves) because it accurately reflects the display. Again, to illustrate the display in the interpreter and you can see very clearly that the output image appears “rounder”, i.e. smoother than the previous curve of the usual programs actually implied.
Features of MsBetweenDisplayChange:
- Focus on the display output: this metric not only includes the render time, but also takes into account the entire process up to the display on the screen. This also includes synchronization with the refresh rate of the monitor.
- Importance for the user experience: It is particularly relevant for the evaluation of perceived fluidity and image consistency. A low and consistent MsBetweenDisplayChange time contributes to a smooth viewing experience, while fluctuations indicate stuttering or uneven frame pacing.
- Integration of flip metering: With modern hardware and technologies such as DLSS 4, the frame pacing logic is often supported by special hardware mechanisms such as flip metering, which ensure that the frames are displayed evenly. MsBetweenDisplayChange directly reflects these optimizations.
Limitation of MsBetweenDisplayChange: This metric alone does not provide direct insight into the efficiency of the render pipeline or the time it takes an application to produce frames. It is primarily focused on the visible display and synchronization.
Technical comparison of the two metrics
Aspect | MsBetweenPresents | MsBetweenDisplayChange |
---|---|---|
Measuring point | Time at which a new frame is transferred to the GPU queue | Time at which a new frame becomes visible on the display |
Focus | GPU queue and application performance | Image output and perceived fluidity |
Relevance for user experience | Indirectly, as it affects the render pipeline | Direct, as it affects the visible display |
Usage | Analysis of render performance and pipeline bottlenecks | Evaluation of image consistency and quality |
Importance of hardware optimizations | Less relevant | Very relevant (e.g. through flip metering) |
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