Early Overclocking and Testing Methodology

As the first person (pretty much) with a Radeon Vega Frontier Edition in hand, overclocking is a bit of the wild west. Thanks to the Wattman portion of the AMD driver, overclocking is pretty straight forward, including access to the power target, fan speed, clock speed.

Rather than walk you through the steps of what occurred, let's present this in an efficient manner for brevity.

  • Peak clock adjustment: +7% (resulting 1682 MHz rated clock)
  • Set fan to 3000 RPM fixed, kept GPU under 80C
  • Resulting clock speed was about 60 MHz higher than stock settings
  • Set power target to +20%, was drawing ~350 watts

Increasing the fan speed over the default curve helped bring the GPU temperature down from the 85C maximum. AMD has set this to just 2000 RPM out of the box but I think a setting of 2800-3000 would be acceptable for noise level. However, even with the GPU temperature in the upper-70s, clock speeds never sustained or exceeded the 1600 MHz mark. (This is even with the "rated" clock speed reported by GPU-Z indicating a 1682 MHz clock speed.)

Even with our tweaks in place, I do not believe the clock speed improvements we see will result in much performance change in gaming. This also leaves us with a lot of questions to the value of the $500 more expensive water cooled version of the Frontier Edition, due in our offices next week.

Testing Suite and Methodology Update

If you have followed our graphics testing at PC Perspective you’ll know about a drastic shift we made in 2012 to support a technology we called Frame Rating. Frame Rating use the direct capture of output from the system into uncompressed video files and FCAT-style scripts to analyze the video to produce statistics including frame rates, frame times, frame time variance and game smoothness.

Readers and listeners might have also heard about the issues surrounding the move to DirectX 12 and UWP (Unified Windows Platform) and how it affected our testing methods. Our benchmarking process depends on a secondary application running in the background on the tested PC that draws colored overlays along the left-hand side of the screen in a repeating pattern to help us measure performance after the fact. The overlay we have been using supported DirectX 9, 10 and 11, but didn’t work with DX12 or UWP games.

We worked with NVIDIA to fix that and we have an overlay that behaves exactly in the same way as before, but it now will let us properly measure performance and smoothness on DX12 and UWP games. This is a big step to maintaining the detailed analytics of game performance that enable us to push both game developers and hardware vendors to perfect their products and create the best possible gaming experiences for consumers.

So, as a result, our testing suite has been upgraded with a new collection of games and tests. Included in this review are the following:

  • 3DMark Fire Strike Extreme and Ultra
  • Unigine Heaven 4.0
  • Dirt Rally (DX11)
  • Fallout 4 (DX11)
  • Grand Theft Auto V (DX11)
  • Hitman (DX12)
  • Rise of the Tomb Raider (DX12)
  • The Witcher 3 (DX11)

We have included racing games, third person, first person, DX11, DX12, UWP and some synthetics, going for a mix that I think encapsulates the gaming market of today and the future as best as possible. Hopefully we can finally end the bickering in comments about not using DX12 titles in our GPU reviews! (Ha, right.)

Our GPU testbed remains unchanged, including an 8-core Haswell-E processor and plenty of memory and storage.

  PC Perspective GPU Testbed
Processor Intel Core i7-5960X Haswell-E
Motherboard ASUS Rampage V Extreme X99
Memory G.Skill Ripjaws 16GB DDR4-3200
Storage OCZ Agility 4 256GB (OS)
Adata SP610 500GB (games)
Power Supply Corsair AX1500i 1500 watt
OS Windows 10 x64
Drivers AMD: 17.6 (Vega)
NVIDIA: 382.53

GPU-Z needs updating for the Radeon Vega Frontier Edition

For those of you that have never read about our Frame Rating capture-based performance analysis system, the following section is for you. If you have, feel free to jump straight into the benchmark action!!

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 a 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 files 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. 


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Previous example data

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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Previous example data

This graph takes a different subset of data points and plots them similarly to the FRAPS file above, but this time we are looking 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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Previous example data

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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Previous example data

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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Previous example data

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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Previous example data

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 the 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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Previous example data

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 call this data the PCPER ISU, which beer fans will appreciate as 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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