Subject: Editorial | March 16, 2017 - 12:38 PM | Alex Lustenberg
Tagged: podcast, ryzen 5, ryzen, nvidia, mobileye, jetson, gtx 1080 ti, fcat vr, delidding
PC Perspective Podcast #441 - 03/16/17
Join us for NVIDIA GTX 1080 Ti, AMD Ryzen Scheduler Discussion, AMD Ryzen 5 Announcement, Intel Kaby Lake de-lidding, and more!
The URL for the podcast is: http://pcper.com/podcast - Share with your friends!
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Hosts: Ryan Shrout, Josh Walrath, Jeremy Hellstrom, Morry Teitelman, Ken Addison
Program length: 1:24:48
Background and setup
A couple of weeks back, during the excitement surrounding the announcement of the GeForce GTX 1080 Ti graphics card, NVIDIA announced an update to its performance reporting project known as FCAT to support VR gaming. The updated iteration, FCAT VR as it is now called, gives us the first true ability to not only capture the performance of VR games and experiences, but the tools with which to measure and compare.
Watch ths video walk through of FCAT VR with me and NVIDIA's Tom Petersen
I already wrote an extensive preview of the tool and how it works during the announcement. I think it’s likely that many of you overlooked it with the noise from a new GPU, so I’m going to reproduce some of it here, with additions and updates. Everyone that attempts to understand the data we will be presenting in this story and all VR-based tests going forward should have a baseline understanding of the complexity of measuring VR games. Previous tools don’t tell the whole story, and even the part they do tell is often incomplete.
If you already know how FCAT VR works from reading the previous article, you can jump right to the beginning of our results here.
Measuring and validating those claims has proven to be a difficult task. Tools that we used in the era of standard PC gaming just don’t apply. Fraps is a well-known and well-understood tool for measuring frame rates and frame times utilized by countless reviewers and enthusiasts, but Fraps lacked the ability to tell the complete story of gaming performance and experience. NVIDIA introduced FCAT and we introduced Frame Rating back in 2013 to expand the capabilities that reviewers and consumers had access to. Using more sophisticated technique that includes direct capture of the graphics card output in uncompressed form, a software-based overlay applied to each frame being rendered, and post-process analyzation of that data, we could communicate the smoothness of a gaming experience, better articulating it to help gamers make purchasing decisions.
For VR though, those same tools just don’t cut it. Fraps is a non-starter as it measures frame rendering from the GPU point of view and completely misses the interaction between the graphics system and the VR runtime environment (OpenVR for Steam/Vive and OVR for Oculus). Because the rendering pipeline is drastically changed in the current VR integrations, what Fraps measures is completely different than the experience the user actually gets in the headset. Previous FCAT and Frame Rating methods were still viable but the tools and capture technology needed to be updated. The hardware capture products we used since 2013 were limited in their maximum bandwidth and the overlay software did not have the ability to “latch in” to VR-based games. Not only that but measuring frame drops, time warps, space warps and reprojections would be a significant hurdle without further development.
NVIDIA decided to undertake the task of rebuilding FCAT to work with VR. And while obviously the company is hoping that it will prove its claims of performance benefits for VR gaming, it should not be overlooked the investment in time and money spent on a project that is to be open sourced and free available to the media and the public.
NVIDIA FCAT VR is comprised of two different applications. The FCAT VR Capture tool runs on the PC being evaluated and has a similar appearance to other performance and timing capture utilities. It uses data from Oculus Event Tracing as a part of the Windows ETW and SteamVR’s performance API, along with NVIDIA driver stats when used on NVIDIA hardware to generate performance data. It will and does work perfectly well on any GPU vendor’s hardware though with the access to the VR vendor specific timing results.