Frame Rating Dissected: Full Details on Capture-based Graphics Performance Testing
How Games Work
Because of the complexity and sheer amount of data we have gathered using our Frame Rating performance methodology, we are breaking it up into several articles that each feature different GPU comparisons. Here is the schedule:
- 3/27: Frame Rating Dissected: Full Details on Capture-based Graphics Performance Testing
- 3/27: Radeon HD 7970 GHz Edition vs GeForce GTX 680 (Single and Dual GPU)
- 3/30: AMD Radeon HD 7990 vs GeForce GTX 690 vs GeForce GTX Titan
- 4/2: Radeon HD 7950 vs GeForce GTX 660 Ti (Single and Dual GPU)
- 4/5: Radeon HD 7870 GHz Edition vs GeForce GTX 660 (Single and Dual GPU)
- 4/16: Frame Rating: Visual Effects of Vsync on Gaming Animation
The process of testing games and graphics has been evolving even longer than I have been a part of the industry: 14+ years at this point. That transformation in benchmarking has been accelerating for the last 12 months. Typical benchmarks test some hardware against some software and look at the average frame rate which can be achieved. While access to frame time has been around for nearly the full life of FRAPS, it took an article from Scott Wasson at the Tech Report to really get the ball moving and investigate how each frame contributes to the actual user experience. I immediately began research into testing actual performance perceived by the user, including the "microstutter" reported by many in PC gaming, and pondered how we might be able to test for this criteria even more accurately.
The result of that research is being fully unveiled today in what we are calling Frame Rating – a completely new way of measuring and validating gaming performance.
The release of this story for me is like the final stop on a journey that has lasted nearly a complete calendar year. I began to release bits and pieces of this methodology starting on January 3rd with a video and short article that described our capture hardware and the benefits that directly capturing the output from a graphics card would bring to GPU evaluation. After returning from CES later in January, I posted another short video and article that showcased some of the captured video and stepping through a recorded file frame by frame to show readers how capture could help us detect and measure stutter and frame time variance.
Finally, during the launch of the NVIDIA GeForce GTX Titan graphics card, I released the first results from our Frame Rating system and discussed how certain card combinations, in this case CrossFire against SLI, could drastically differ in perceived frame rates and performance while giving very similar average frame rates. This article got a lot more attention than the previous entries and that was expected – this method doesn’t attempt to dismiss other testing options but it is going to be pretty disruptive. I think the remainder of this article will prove that.
Today we are finally giving you all the details on Frame Rating; how we do it, what we learned and how you should interpret the results that we are providing. I warn you up front though that this is not an easy discussion and while I am doing my best to explain things completely, there are going to be more questions going forward and I want to see them all! There is still much to do regarding graphics performance testing, even after Frame Rating becomes more common. We feel that the continued dialogue with readers, game developers and hardware designers is necessary to get it right.
Below is our full video that features the Frame Rating process, some example results and some discussion on what it all means going forward. I encourage everyone to watch it but you will definitely need the written portion here to fully understand this transition in testing methods. Subscribe to your YouTube channel if you haven't already!
How Games Work
Before we dive into why I feel that our new Frame Rating testing method is the best for the gamer, there is some necessary background information that you must understand. While we all play games on a near daily basis, most of us don’t fully grasp the complexity and detail that goes into producing an image that makes its way from code a developer writes to the pixels on your monitor. The below diagram attempts to simplify the entire process from the game engine to the display.
In the image above we have defined a few important variables based on time that will help us explain graphics anomalies. The first is t_game and it refers the internal time that the game engine is using to keep track of its internal simulations and interactions. This is where processes like the physics simulations, user interface, artificial intelligence and more are handled. Different game engines keep time in different ways, but they usually fall into two categories: fixed or variable time steps. In a fixed time method the game engine cycles the environment in its internal simulation on a regular, fixed iteration. This is more predictable but it also forces other systems to sync up to it (drivers, GPU rendering) which may cause some issues. The variable time step method allows a lot more flexibility, but it can be more complicated for developers to maintain a fluid feeling simulation because of simply not knowing when the next simulation update will take place.
Following that is t_present, the point at which the game engine and graphics card communicate to say that they are ready to pass information for the next frame to be rendered and displayed. What is important about this time location is that this is where FRAPS gets its time stamps and data and also where the overlay that we use for our Frame Rating method is inserted. What you should notice right away though is that there is quite a bit more work that occurs AFTER the t_present command is sent and before the user actually sees any result. This in particular is where capture method’s advantages stem from.
After DirectX calls the game engine to recieve its time, the graphics driver gets its hands on it for the first time. The driver maps the DX calls to its own specific hardware and then starts the rendering process on a GPU. Once the frame is rendered, we will define another variable, t_render, which is reported when the image is ready to be sent to a display. Finally, t_display will be defined as the time in which data from the frame is on the display, whether that be a complete frame (Vsync enabled for example) or a partial frame.
You can likely already see where the differences between FRAPS data measurement and Frame Rating measurement start to develop. Using capture hardware and analysis tools that we’ll detail later, we are recording the output from the graphics card directly as if it were a monitor. We are essentially measuring frames at the t_display level rather than the t_present point, giving us data that has been filtered through the entire game engine, DirectX, driver, rendering and display process.
It is also useful to discuss stutter and how it relates to these time definitions. Despite some readers opinions, stutter in game play is related to the smoothness of animation and not hard wired to low frame rate. If you have a steady frame rate of 25 FPS you can still have an enjoyable experience (as evident by the 24 FPS movies we all watch at the theater). Instead, we should view stutter as a variance level between t_game and t_display; if the total display time runs at 50ms (20 FPS) you won’t have stuttering in animation (in most cases) but if total display time shifts between 20ms and 50ms you definitely will.
In our second video on Frame Rating, we looked at a capture from Dishonored in a frame by frame analysis and saw a rather large stutter. I think a better term for that is a hitch, a large single frame rate issue that isn’t indicative of a microstutter smoothness problem. Using the above variables, a hitch would be a single large instance of t_game – t_display. As you’ll see in our results analysis pages (including games like Skyrim) this happens fairly often, even in some games that are smooth otherwise.
Our 2nd video on Frame Rating from a couple months ago...
There is also a completely different discussion on the advantages and differences of capturing data from FRAPS versus capturing data with our Frame Rating methodology. I believe that the advantages of hardware capture outweigh the concerns currently, but in reality the data that FRAPS generates isn’t that important, it just happens to be the closest data point to another metric we would love to know more about: game time.
Game time is the internal game clock that the software engine uses to keep track of the physical world. This clock could be based on a fixed time span for each tick or it could be variable or timed off of another source (like the OS or GPU driver). An Intel GPU engineer, Andrew Lauritzen, recently made a great post over on the Beyond3D.com forums about game time, back pressure on the game pipeline and much more. Here is a short portion of that post, but I would encourage everyone to read the entirety completely:
1) Smooth motion is achieved by having a consistent throughput of frames all the way from the game to the display.
2) Games measure the throughput of the pipeline via timing the back-pressure on the submission queue. The number they use to update their simulations is effectively what FRAPS measures as well.
3) A spike anywhere in the pipeline will cause the game to adjust the simulation time, which is pretty much guaranteed to produce jittery output. This is true even if frame delivery to the display (i.e. rendering pipeline output) remains buffered and consistent. i.e. it is never okay to see spikey output in frame latency graphs.
4) The converse is actually not true: seeing smooth FRAPS numbers does not guarantee you will see smooth display, as the pipeline could be producing output to the display at jittery intervals even if the input is consistent. This is far less likely though since GPUs typically do relatively simple, predictable work.
Clearly the best case for evaluating overall gaming performance will be to have access to the internal game and measure it in comparison the output from Frame Rating, the actual frames on your display. Differences there could be analyzed to find exact bottlenecks in the pipeline from game code to display. The problem is no game engine developers allow access to the information currently and the number of different engines in use today makes it difficult for even the likes of NVIDIA and AMD to gather data reliably. There is opportunity for change here if an API were to exist (in DirectX for example) that would give all game engines reliable time iterations that we would then have access to.
You may notice that there is a lot of “my” and “our” in this story while also seeing similar results from other websites being released today. While we have done more than a year’s worth of the testing and development on our own tools to help expedite a lot of this time consuming testing, some of the code base and applications were developed with NVIDIA and thus were distributed to other editors recently.
NVIDIA was responsible for developing the color overlay that sits between the game and DirectX (in the same location of the pipeline as FRAPS essentially) as well as the software extractor that reads the captured video file to generate raw information about the lengths of those bars in an XLS file. Obviously, NVIDIA has a lot to gain from this particular testing methodology: its SLI technology looks much better than AMD’s CrossFire when viewed in this light, highlighting the advantages that SLI’s hardware frame metering bring to the table.
The next question from our readers should then be: are there questions about the programs used for this purpose? After having access to the source code and applications for more than 12 months I can only say that I have parsed through it all innumerable times and I have found nothing that NVIDIA has done that is disingenuous. Even better, we are going to be sharing all of our code from the Perl-based parsing scripts (that generate the data in the graphs you’ll see later from the source XLS file) as well as a couple of examples of the output XLS files.
Not only do we NEED to have these tools vetted by other editors, but we also depend on the community to keep us on our toes as well. When we originally talked with NVIDIA about this project the mindset from the beginning was merely to get the ball rolling and let the open source community and enthusiast gamers look at every aspect of the performance measurement. That is still the goal – with only one minor exception: NVIDIA doesn’t want the source code of the overlay to leak out simply because of some potential patent/liability concerns. Instead, we are hoping to have ANOTHER application built to act as the overlay; it may be something that Beepa and the FRAPS team can help us with.