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Nvidia's Mesh Shading Technique Improves Rendering Performance Threefold on GeForce RTX 2080
By Neil Soutter on November 5th, 2018 at 02:24pm - original article from game-debate

It’s kind of odd that millions of early adopters have Nvidia’s GeForce RTX 20 Series graphics cards in their hands and we’re still yet to learn what these GPUs worth hundreds of dollars are actually capable of. Roblox technical director Arseny Kapoulkine has shed some light on the capabilities of the GeForce RTX 2080 though, claiming “Modern GPUs are crazy. But this GPU is especially so.

More specifically, Kapoulkine has been messing around with Mesh shading using the Nvidia RTX series RTX 2080 GPU in conjunction with the Vulkan API, discovering this method is three times faster than traditional rasterization methods.

“Modern GPUs are crazy. But this GPU is especially so,” wrote Kapoulkine in a tweet. “Scene has 100 Happy Buddhas (100*1.087M tri). Traditional raster takes 17.2 ms. RTX code from my stream (with some perf fixes/tweaks) renders this in 6.3 ms - almost 3x faster. This is 17B triangles per second. 2080 non-Ti.”

GeForce RTX 2080 Mesh Shading

Mesh Shading is a new programmable geometric shading technique introduced for the Turing GPUs that allows for the rendering of highly complex geometry. In theory, this allows for far more detailed objects and scenes composed of hundreds or thousands of unique objects, as well as faster level-of-detail techniques to improve LOD and draw distance.

Using this technique, Kapoulkine discovered a 3x bump in performance using the RTX series compared to traditional rendering methods. He was messing apart with a scene featuring 100 Buddha statues, each composed of 1.087 million triangles. The GeForce RTX 2080 could render these 17 billion triangles in 6.ms, compared to 17.2ms through traditional rasterization techniques.

The downside to Mesh Shading is that it's currently supported exclusively by the GeForce RTX 20 series. That means it's unlikely we're going to see many games developed with this feature in mind, at least until the capabilities become more widespread.

Source: DSOG