技术的进步是不可阻挡的，图形硬件更是如此。每年卡片的速度都会显着加快，并为花哨的图形技巧带来一套全新的首字母缩略词。
查看 PC 游戏的视觉设置，您会遇到一个单词沙拉，其中包含(word salad)MSAA、FXAA、SMAA和WWJD等美味金块。好吧，也许不是最后一个。
如果您是新Nvidia GeForce RTX卡的幸运拥有者，您现在还可以选择启用名为DLSS的功能。它是深度学习超级采样(Deep Learning Super Sampling)的缩写，是Nvidia RTX卡中下一代硬件(generation hardware)功能的重要组成部分。
在撰写本文时，只有这些卡具有运行DLSS所需的硬件：
- RTX 2060
- RTX 2060 超级
- RTX 2070
- RTX 2070 超级
- RTX 2080
- RTX 2080 超级
- RTX 2080 钛
所讨论的特定硬件被称为“张量(Tensor)”核心(” core)，每个型号都有不同数量的这些专用处理器。
张量核心旨在加速机器学习任务，DLSS就是一个例子。如果您不使用(t use) DLSS，则卡的那部分将保持空闲状态。这意味着如果DLSS可用， 您并没有使用闪亮的新GPU的全部容量，但仍处于关闭状态。(GPU)
不仅如此。要了解DLSS带来的价值，我们必须简单地离题一些相关的概念。
快速了解内部分辨率和升级(A Quick Detour Into Internal Resolutions & Upscaling)
现代电视和显示器(Modern TVs and monitors)具有所谓的“原生”分辨率(resolution)。这仅仅意味着屏幕具有特定数量的物理像素。如果您在该屏幕上显示的图像与确切的原始分辨率不同，则必须将其“放大”或缩小以使其适合。
因此，例如，如果您将高清图像输出到4K 显示器(4K display)，它会看起来非常块状和锯齿状。就像您将数码照片放大得太远一样。然而在实践中，高清视频(HD video)在 4K 电视上看起来还不错，虽然可能不如原生 4K 素材那么清晰。这是因为电视有一个被称为“升频器”的硬件，它可以处理和过滤低分辨率图像以使其看起来可以接受。
问题是升级硬件的质量在显示器品牌和型号之间差异很大。这(Which)就是为什么GPU(GPUs)通常带有自己的缩放技术(scaling technology)。
设计用于输出到 4K 显示器的“专业”控制台以原生 4K 图像呈现它，因此根本不会发生显示器升级。这意味着游戏的开发者可以完全控制最终的图像质量(image quality)。
但是，大多数主机游戏不会以原生 4K 分辨率进行渲染。它们具有较低的“内部”分辨率，从而减轻了(” resolution)GPU的压力。然后使用控制台的内部缩放技术放大该图像，使其在(scaling technology)高分辨率屏幕(high-resolution screen)上看起来尽可能好。
实际上，DLSS是一种复杂的方法，它以低于原始分辨率的分辨率渲染PC 游戏，然后使用(PC game)DLSS 技术(DLSS technology)将其放大以用于连接的显示器。从理论上讲，这会显着提高性能。
虽然这听起来很像 4K 游戏机上正在发生的事情，但DLSS确实很特别。这一切都归功于“深度学习”。
什么是“深度学习”？(What’s The “Deep Learning” Bit About?)
深度学习是一种使用模拟神经网络的机器学习技术。(machine learning technique)换句话说，您大脑中的神经元如何学习(brain learn)并为复杂问题创建解决方案的数字近似值。
除其他外，这项技术可以让计算机识别人脸，让机器人理解和导航周围的世界。它还对最近一连串的 deepfakes 负责。这就是DLSS的秘诀。
神经网络需要“训练”，这基本上是展示某事物应该是什么样子的网络示例。如果你想教网络如何识别一张脸，你可以向它展示数百万张脸，让它学习构成一张典型脸的特征和模式。如果它正确地吸取了教训，你可以给它看任何一张有脸的图像，它会立即把它挑出来。
Nvidia所做的就是在支持 DLSS 的游戏中以令人难以置信的高分辨率图像训练他们的深度(DLSS)学习软件(learning software)。当使用超级计算机级别的图形性能进行渲染时，神经网络会学习游戏“应该”的样子。
然后它采用较低的内部分辨率框架(resolution frame)，并且由于没有更好的词，“想象”如果一台比你的计算机强大得多的计算机渲染场景会是什么样子。如果这对您来说听起来有点像黑魔法，那么您并不孤单！
何时使用 DLSS(When To Use DLSS)
首先(First)，您只能在支持DLSS的游戏中使用 DLSS，这是一个快速增长的列表，谢天谢地。每个标题对DLSS(DLSS)也有自己的要求，例如以最低分辨率渲染，因为这就是神经网络所训练的内容。
然而，英伟达的大脑不会(Nvidia doesn)停止学习(stop learning)，您卡上的DLSS功能(DLSS feature)将不断更新，扩展每个标题的支持和质量(support and quality)。
确定是否应该在游戏中使用DLSS的最佳方法是观察结果。将其与传统的放大或抗锯齿进行比较，看看哪个更令人愉快。性能也是一个重要的决定因素(deciding factor)。如果您的目标是每秒 60 帧，但无法达到目标，DLSS是一个不错的选择。
但是，如果您获得高帧速率，DLSS实际上可以减慢速度。这是因为张量核心需要固定的时间来处理每一帧。现在他们不能足够快地完成高帧率播放(frame rate play)。
本质上，当使用目标帧速率(target frame rate)约为每秒 60 帧的高分辨率显示器(high-resolution display)（例如 4K、超宽或 1440p 分辨率）时， DLSS最有用。在激活RTX(RTX)卡的另一个主要技巧(party trick)——光线(– ray)追踪时，它也非常有用。DLSS可以很好地抵消光线追踪的性能损失，(performance loss)最终结果(end result)有时非常壮观。
这是您在决定是否使用DLSS之前至少需要知道的。请记住，这项技术正在迅速变化，所以如果你(Just)不喜欢今天的结果，几个月后再回来，你可能最后会被吹走。
What Is DLSS and Should You Use It In Games
The march of technоlogy is inexorable and nowhere іs this more true than with graphics hardware. Every уear cards get signifіcantly faster and bring a whole new set of acronyms for fancy graphical tricks.
Looking at the visual settings for PC games, you’ll encounter a word salad that contains such tasty nuggets as MSAA, FXAA, SMAA and WWJD. OK, maybe not that last one.
If you are the lucky owner of a new Nvidia GeForce RTX card, you can now also choose to enable something called DLSS. It’s short for Deep Learning Super Sampling and is a big part of the next generation hardware features found in Nvidia RTX cards.
At the time of writing, only these cards have the required hardware to run DLSS:
- RTX 2060
- RTX 2060 Super
- RTX 2070
- RTX 2070 Super
- RTX 2080
- RTX 2080 Super
- RTX 2080 Ti
The specific hardware in question is referred to as a “Tensor” core, with each model having a different number of these specialized processors.
Tensor cores are designed to accelerate machine learning tasks, which DLSS is an example of. If you don’t use DLSS, that part of the card remains idle. This means you aren’t using the full capacity of your shiny new GPU if DLSS is available, but remains off.
There’s more to it than that though.To understand what value DLSS brings to the table, we have to digress briefly into a few related concepts.
A Quick Detour Into Internal Resolutions & Upscaling
Modern TVs and monitors have what’s known as a “native” resolution. This simply means that the screen has a specific number of physical pixels. If the image you are displaying on that screen differs from the exact native resolution, it has to be “scaled” up or down to make it fit.
So if you output an HD image to a 4K display, for example, it’s going to look quite blocky and jagged. Just as if you’ve zoomed a digital photo in too far. In practice however, HD video looks just fine on a 4K TV, if perhaps a little less sharp than native 4K footage. That’s because the TV has a piece of hardware known as an “upscaler” that processes and filters the lower-resolution image to look acceptable.
The problem is that the quality of the upscaling hardware varies wildly between display brands and models. Which is why GPUs often come with their own scaling technology.
The “pro” consoles that are designed to output to a 4K display present it with a native 4K image, so that no display upscaling happens at all. This means the developers of games have complete control of the final image quality.
However, most console games do not render at a native 4K resolution. They have a lower “internal” resolution, which puts less stress on the GPU. That image is then scaled up to look as good as possible on the high-resolution screen using the console’s internal scaling technology.
In effect, DLSS is a sophisticated method that renders a PC game at a lower than native resolution and then uses the DLSS technology to upscale it for the connected display. In theory this leads to a significant boost in performance.
While that sounds a lot like what’s happening on 4K consoles, under the hood DLSS is really something special. All thanks to “deep learning”.
What’s The “Deep Learning” Bit About?
Deep learning is a machine learning technique that uses a simulated neural net. In other words, a digital approximation of how the neurons in your brain learn and create solutions to complex problems.
It’s the technology that, among other things, allows computers to recognize faces and lets robots understand and navigate the world around them. It’s also responsible for the recent spates of deepfakes. That’s the secret sauce of DLSS.
Neural networks require “training” which is basically showing the net examples of what something should be like. If you want to teach the net how to recognize a face, you show it millions of faces, letting it learn the features and patterns that make up a typical face. If it learns the lesson properly, you can show it any image with a face in it, and it will pick it out instantly.
What Nvidia have done is to train their deep learning software on incredibly high-resolution images from the games that support DLSS. The neural network learns what the game “should” look like when rendered using supercomputer-level graphics performance.
It then takes that lower internal resolution frame and, for lack of a better word, “imagines” what it would have looked like if a much, much more powerful computer than yours had rendered the scene. If that sounds a little like black magic to you well, you’re not alone!
When To Use DLSS
First of all, you can only use DLSS in games that support it, which is a list that’s growing quickly, thankfully. Each title also has its own requirements for DLSS, such as rendering at a minimum resolution, because that’s what the neural net has been trained on.
However, the big brain at Nvidia doesn’t stop learning and the DLSS feature on your card will keep getting updates, expanding per-title support and quality.
The best way to figure out if you should use DLSS in your games is to eyeball the result. Compare it to traditional upscaling or anti-aliasing to see which is more pleasant. Performance is also an important deciding factor. If you are targeting 60 frames per second, but can’t get there, DLSS is a good choice.
If you are getting high frame rates however, DLSS can actually slow things down. That’s because the tensor cores need a fixed amount of time to process each frame. Right now they can’t do it quickly enough for high frame rate play.
Essentially, DLSS is most useful when using a high-resolution display (e.g. 4K, ultrawide or 1440p resolutions) with a target frame rate at around 60 frames per second. It’s also incredibly useful when activating the other main party trick of RTX cards – ray tracing. DLSS can offset the performance loss of ray tracing quite well, with an end result that is at times spectacular.
That’s the least you need to know before deciding to go with DLSS or not. Just remember that this technology is changing rapidly, so if you don’t like the results today, come back in a few months and you just might just be blown away at last.
