Testing Setup and GPU Selection

In case you haven’t figured it out yet, our testing selection for the graphics cards in this review is pretty specific. The new AMD Radeon R9 Nano, at $650, has some pretty big shoes to fill. In reality, the R9 Nano is only competing against a very small subset of the graphics market: other mini ITX graphics cards. That is basically limited to the likes of the GeForce GTX 970 Mini ITX offerings from ASUS and MSI. But we still want to find out where the R9 Nano sits in the overall performance field of the graphics market, so we have other AMD products in the mix as well: the Fury X, the Fury and the R9 290X. I didn’t include results from the GTX 980 or GTX 980 Ti due to space limits on our graphs and plots, so you’ll have to make relative performance estimates based on the results on the Fury and Fury X.

Testing Configuration

The specifications for our testing system haven't changed much.

Test System Setup
CPU Intel Core i7-3960X Sandy Bridge-E
Motherboard ASUS P9X79 Deluxe
Memory Corsair Dominator DDR3-1600 16GB
Hard Drive OCZ Agility 4 256GB SSD
Sound Card On-board
Graphics Card AMD Radeon R9 Nano 4GB
AMD Radeon R9 Fury X 4GB
AMD Radeon R9 Fury 4GB
AMD Radeon R9 290X 4GB
ASUS GTX 970 DC Mini 4GB
Graphics Drivers AMD: 15.8 Beta
NVIDIA: 355.65
Power Supply Corsair AX1200i
Operating System Windows 8.1 Pro x64


What you should be watching for

  1. R9 Nano vs GTX 970 DC Mini – This is the most important comparison – what does the best in small form factor GPUs from NVIDIA have it comparison to the new AMD Radeon R9 Nano?
     
  2. R9 Nano vs R9 Fury – We know that the R9 Nano has a fully enabled Fiji GPU but runs at a lower clock speed. How close in performance does it get to the full-sized, but cut back, R9 Fury?
     
  3. R9 Nano vs R9 290X – How does the 290 watt R9 290X from the Hawaii generation compare to the most power efficient flagship GPU that AMD has ever built?

 

Frame Rating: Our Testing Process

If you aren't familiar with it, you should probably do a little research into our testing methodology as it is quite different than others you may see online.  Rather than using FRAPS to measure frame rates or frame times, we are using an secondary PC to capture the output from the tested graphics card directly and then use post processing on the resulting video to determine frame rates, frame times, frame variance and much more. 

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This amount of data can be pretty confusing if you attempting to read it without proper background, but I strongly believe that the results we present paint a much more thorough picture of performance than other options.  So please, read up on the full discussion about our Frame Rating methods before moving forward!!

While there are literally dozens of file created for each “run” of benchmarks, there are several resulting graphs that FCAT produces, as well as several more that we are generating with additional code of our own. 

If you don't need the example graphs and explanations below, you can jump straight to the benchmark results now!!

 

The PCPER FRAPS File

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While the graphs above are produced by the default version of the scripts from NVIDIA, I have modified and added to them in a few ways to produce additional data for our readers.  The first file shows a sub-set of the data from the RUN file above, the average frame rate over time as defined by FRAPS, though we are combining all of the GPUs we are comparing into a single graph.  This will basically emulate the data we have been showing you for the past several years.

 

The PCPER Observed FPS File

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This graph takes a different subset of data points and plots them similarly to the FRAPS file above, but this time we are look at the “observed” average frame rates, shown previously as the blue bars in the RUN file above.  This takes out the dropped and runts frames, giving you the performance metrics that actually matter – how many frames are being shown to the gamer to improve the animation sequences. 

As you’ll see in our full results on the coming pages, seeing a big difference between the FRAPS FPS graphic and the Observed FPS will indicate cases where it is likely the gamer is not getting the full benefit of the hardware investment in their PC.

 

The PLOT File

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The primary file that is generated from the extracted data is a plot of calculated frame times including runts.  The numbers here represent the amount of time that frames appear on the screen for the user, a “thinner” line across the time span represents frame times that are consistent and thus should produce the smoothest animation to the gamer.  A “wider” line or one with a lot of peaks and valleys indicates a lot more variance and is likely caused by a lot of runts being displayed.

 

The RUN File

While the two graphs above show combined results for a set of cards being compared, the RUN file will show you the results from a single card on that particular result.  It is in this graph that you can see interesting data about runts, drops, average frame rate and the actual frame rate of your gaming experience. 

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For tests that show no runts or drops, the data is pretty clean.  This is the standard frame rate per second over a span of time graph that has become the standard for performance evaluation on graphics cards.

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A test that does have runts and drops will look much different.  The black bar labeled FRAPS indicates the average frame rate over time that traditional testing would show if you counted the drops and runts in the equation – as FRAPS FPS measurement does.  Any area in red is a dropped frame – the wider the amount of red you see, the more colored bars from our overlay were missing in the captured video file, indicating the gamer never saw those frames in any form.

The wide yellow area is the representation of runts, the thin bands of color in our captured video, that we have determined do not add to the animation of the image on the screen.  The larger the area of yellow the more often those runts are appearing.

Finally, the blue line is the measured FPS over each second after removing the runts and drops.  We are going to be calling this metric the “observed frame rate” as it measures the actual speed of the animation that the gamer experiences.

 

The PERcentile File

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Scott introduced the idea of frame time percentiles months ago but now that we have some different data using direct capture as opposed to FRAPS, the results might be even more telling.  In this case, FCAT is showing percentiles not by frame time but instead by instantaneous FPS.  This will tell you the minimum frame rate that will appear on the screen at any given percent of time during our benchmark run.  The 50th percentile should be very close to the average total frame rate of the benchmark but as we creep closer to the 100% we see how the frame rate will be affected. 

The closer this line is to being perfectly flat the better as that would mean we are running at a constant frame rate the entire time.  A steep decline on the right hand side tells us that frame times are varying more and more frequently and might indicate potential stutter in the animation.

 

The PCPER Frame Time Variance File

Of all the data we are presenting, this is probably the one that needs the most discussion.  In an attempt to create a new metric for gaming and graphics performance, I wanted to try to find a way to define stutter based on the data sets we had collected.  As I mentioned earlier, we can define a single stutter as a variance level between t_game and t_display. This variance can be introduced in t_game, t_display, or on both levels.  Since we can currently only reliably test the t_display rate, how can we create a definition of stutter that makes sense and that can be applied across multiple games and platforms?

We define a single frame variance as the difference between the current frame time and the previous frame time – how consistent the two frames presented to the gamer.  However, as I found in my testing plotting the value of this frame variance is nearly a perfect match to the data presented by the minimum FPS (PER) file created by FCAT.  To be more specific, stutter is only perceived when there is a break from the previous animation frame rates. 

Our current running theory for a stutter evaluation is this: find the current frame time variance by comparing the current frame time to the running average of the frame times of the previous 20 frames.  Then, by sorting these frame times and plotting them in a percentile form we can get an interesting look at potential stutter.  Comparing the frame times to a running average rather than just to the previous frame should prevent potential problems from legitimate performance peaks or valleys found when moving from a highly compute intensive scene to a lower one.

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While we are still trying to figure out if this is the best way to visualize stutter in a game, we have seen enough evidence in our game play testing and by comparing the above graphic to other data generated through our Frame rating system to be reasonably confident in our assertions.  So much in fact that I am going to going this data the PCPER ISU, which beer fans will appreciate the acronym of International Stutter Units.

To compare these results you want to see a line that is as close the 0ms mark as possible indicating very little frame rate variance when compared to a running average of previous frames.  There will be some inevitable incline as we reach the 90+ percentile but that is expected with any game play sequence that varies from scene to scene.  What we do not want to see is a sharper line up that would indicate higher frame variance (ISU) and could be an indication that the game sees microstuttering and hitching problems.

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