Entries for tag "rendering", ordered from most recent. Entry count: 181.
# Secrets of Direct3D 12: Do RTV and DSV descriptors make any sense?
This article is intended for programmers who use Direct3D 12. We will explore the topic of descriptors, especially Render Target View (RTV) and Depth Stencil View (DSV) descriptors. To understand the article, you should already know what they are and how to use them. For learning the basics, I recommend my earlier article “Direct3D 12: Long Way to Access Data” where I described resource binding model in D3D12. Current article is somewhat a follow-up to that one. I also recommend checking the official “D3D12 Resource Binding Functional Spec”.
What is a “descriptor”? My personal definition would be that generally in computing, a descriptor is a small data structure that points to some larger data and describes its parameters. While a “pointer”, “identifier”, or “key” is typically just a single number that points or identifies the main object, a “descriptor” is typically a structure that also carries some parameters describing the object.
Descriptors in D3D12 are also called “views”. They mean the same thing. Functions like
CreateRenderTargetView setup a descriptor. Note this is different from Vulkan, where a “view” and a “descriptor” are different entities. The concept of “view” is also present in relational databases. Just like in databases, a “view” points to the target data, but also specifies a way to look at them. In D3D12 it means, for example, that an SRV descriptor pointing to a texture can reinterpret its pixel format (e.g. with or without
_SRGB), limit access to only selected range of mip levels or array slices.
Let’s talk about Constant Buffer View (CBV), Shader Resource View (SRV), or Unordered Access View (UAV) descriptors first. If created inside GPU-accessible descriptor heaps (class
D3D12_DESCRIPTOR_HEAP_FLAG_SHADER_VISIBLE), they can be bound to the graphics pipeline, as I described in details in my previously mentioned article. Being part of GPU memory has some implications:
# Doing dynamic resolution scaling? Watch out for texture memory size!
This article is intended for graphics programmers, mostly those who use Direct3D 12 or Vulkan and implement dynamic resolution scaling. Before we go to the main topic, some introduction first…
Nowadays, more and more games offer some kind resolution scaling. It means rendering the 3D scene in a resolution lower than the display resolution and then upscaling it using some advanced shader, often combined with temporal antialiasing and sharpening. It may be one of the solutions provided by GPU vendors (FSR from AMD, XeSS from Intel, DLSS from NVIDIA) or a custom solution (like TSR in Unreal Engine). It is an attractive option for gamers to have a good FPS increase with only minor image quality degradation. It is becoming more important as monitor resolutions increase to 4K or even more, high-end graphics cards are still expensive, and advanced rendering techniques like ray tracing encourage to favor “better pixels” over “more pixels”. See also my old article: “Scaling is everywhere, pixel-perfect is the past”.
Dynamic resolution scaling is an extension to this idea that allows rendering each frame in a different resolution, lower or higher, as a trade-off between quality and performance, to maintain desired framerate even in more complex scenes with many objects, characters, and particle effects visible on the screen. If you are interested in this technique, I strongly recommend checking a recent article from Martin Fuller from Microsoft: “Dynamic Resolution Scaling (DRS) Implementation Best Practice”, which provides many practical implementation tips.
One of the topics we need to handle when implementing dynamic resolution scaling is the creation and usage of textures that need different resolution every frame, especially render target, depth-stencil, and UAV, used temporarily between render passes. One solution could be to create these textures in the maximum resolution and use only part of them when necessary using a limited viewport. However, Martin gives multiple reasons why this option may cause some problems. A simpler and safer solution is to create a separate texture for each possible resolution, with a certain step. In modern graphics APIs (Direct3D 12 and Vulkan) they can be placed in the same memory, which we call memory aliasing.
Here comes the main question I want to answer in this article: What size of the memory heap should we use when allocating memory for these textures? Can we just take maximum dimensions of a texture (e.g. 4K resolution: 3840 x 2160), call
device->GetResourceAllocationInfo(), inspect returned
D3D12_RESOURCE_ALLOCATION_INFO::SizeInBytes and use it as
D3D12_HEAP_DESC::SizeInBytes? A texture with less pixels should always require less memory, right?
WRONG! Direct3D 12 doesn’t define such a requirement and graphics drivers from some GPU vendors really return smaller size required for a texture with larger dimensions, for some specific dimensions and pixel formats. For example, on AMD Radeon RX 7900 XTX, a render target with format
Why does this happen? It is because textures are not necessarily stored in the GPU memory in a way we imagine them: pixel-after-pixel, row major order. They often use some optimization techniques like pixel swizzling or compression. By “compression”, I don’t mean texture formats like BC or ASTC, which we must use explicitly. I also don’t mean compression like in ZIP file format or zlib/deflate algorithm that decrease data size. Quite the opposite: this kind of compression increases texture size by adding extra metadata, which allow to speed things up by saving memory bandwidth in certain cases. This is done mostly on render target and depth-stencil textures. For more information about it, see my old article: “Texture Compression: What Can It Mean?”. I’m talking about the meaning of the word “compression” number 4 from that article – compression formats that are internal, specific to certain graphics cards, and opaque for us – programmers who just use the graphics API. Problem is that a specific compression format for a texture is selected by the driver based on various heuristics (like render target / depth-stencil / UAV / other flags, pixel format, and… dimensions). This is why a texture with larger dimensions may unexpectedly require less memory.
To research this problem in details, I’ve written a small testing program and I performed tests on graphics cards from various vendors. It was a modification of my small Windows console app D3d12info that goes through the list of all
DXGI_FORMAT enum values, calls
CheckFeatureSupport to check which ones are supported as a render target or depth-stencil. For those that do, I called
GetResourceAllocationInfo to get memory requirements for a texture with this pixel format, with increasing dimensions, where height goes from 32 to 2160 with a step of 8, and width is calculated using a formula for 16:9 aspect ratio: width = height * 16 / 9.
Here are the results. Please remember these are just 3 specific graphics cards. The results may be different on a different GPU and even with a different version of the graphics driver.
On NVIDIA GeForce RTX 3080 with driver 545.84, I found no cases where a texture with larger dimensions requires less memory, so NVIDIA (or at least this specific card) is not affected by the problem described in this article.
On AMD Radeon RX 7900 XTX with driver 23.9.3, I found following data points where memory requirements are non-monotonic – one for each of the following formats:
DXGI_FORMAT_R16G16B16A16_FLOAT/UNORM/UINT/SNORM/SINT: 256x144 = 458,752 B, 270x152 = 393,216 B
DXGI_FORMAT_R32G32_FLOAT/UINT/SINT: 256x144 = 458,752 B, 270x152 = 393,216 B
DXGI_FORMAT_R8G8_UNORM/UINT/SNORM/SINT: 512x288 = 458,752 B, 526x296 = 393,216 B
DXGI_FORMAT_R16_FLOAT/UNORM/UINT/SNORM/SINT: 512x288 = 458,752 B, 526x296 = 393,216 B
DXGI_FORMAT_R8_UNORM/UINT/SNORM/SINT: 256x144 = 131,072 B, 270x152 = 65,536 B
DXGI_FORMAT_A8_UNORM: 256x144 = 131,072 B, 270x152 = 65,536 B
DXGI_FORMAT_B5G6R5_UNORM: 512x288 = 458,752 B, 526x296 = 393,216 B
DXGI_FORMAT_B5G5R5A1_UNORM: 512x288 = 458,752 B, 526x296 = 393,216 B
DXGI_FORMAT_B4G4R4A4_UNORM: 512x288 = 458,752 B, 526x296 = 393,216 B
On Intel Arc A770, with driver 184.108.40.20687, almost every format used as a render target (but none of depth-stencil formats) has multiple steps where the size decreases, and it has them at larger dimensions than AMD. For example, the most “traditional” one –
What to do with this knowledge? The conclusion is that if we implement dynamic resolution scaling and we want to create textures with different dimensions aliasing in memory, required size of this memory is not necessarily the size of the largest texture in terms of dimensions. To be safe, we should query for memory requirements of all texture sizes we may want to use and calculate their maximum. In practice, it should be enough to query resolutions starting from e.g. 75% of the maximum. Because tested GPUs always have only a single step down, an even more efficient, but not fully future-proof solution could be to start from the full resolution, go down until we find a different memory size (no matter if higher or lower), and take maximum of these two.
So far, I focused only on DirectX 12. Is Vulkan also affected by this problem? In the past, it could be. Vulkan has similar concept of querying for memory requirements of a texture using function
vkGetImageMemoryRequirements. It used to have an even bigger problem. To understand it, we must recall that in D3D12, we query for memory requirements (size and alignment) given structure
D3D12_RESOURCE_DESC which describes parameters of a texture to be created. In (the initial) Vulkan API, on the other hand, we need to first create the actual
VkImage object, and then query for its memory requirements. Question is: Given two textures created with exactly same parameters (width, height, pixel format, number of mip levels, flags, etc.), do they always return the same memory requirements?
In the past, it wasn’t required by the Vulkan specification and I saw some drivers for some GPUs that really returned different sizes for two identical textures! It could cause problems, e.g. when defragmenting video memory in Vulkan Memory Allocator library. Was it a bug, or another internal optimization done by the driver, e.g. to avoid some memory bank conflicts? I don’t know. Good news is that since then, Vulkan specification was clarified to require that functions like
vkGetImageMemoryRequirements always return the same size and alignment for images created with the same parameters, and new drivers comply with that, so the problem is gone now. Vulkan 1.3 also got a new function
vkGetDeviceImageMemoryRequirements that takes
VkImageCreateInfo with image creation parameters instead of an already created image object, just like D3D12 does from the beginning.
Going back to the main question of this article: When VK_KHR_maintenance4 extension is enabled (which has been promoted to core Vulkan 1.3), the problem does not occur, as Vulkan specification says: "For a VkImage, the size memory requirement is never greater than that of another VkImage created with a greater or equal value in each of extent.width, extent.height, and extent.depth; all other creation parameters being identical.", and the same for buffers.
Big thanks to my friends: Bartek Boczula for discussions about this topic and inspiration to write this article, as well as Szymon Nowacki for testing on the Intel card! Also thanks to Constantine Shablia from Collabora for pointing me to the answer on Vulkan.
# ShaderCrashingAssert - a New Small Library
Last Thursday (August 17th) AMD released a new tool for post-mortem analysis of GPU crashes: Radeon GPU Detective. I participated in this project, but because this is my personal blog and because it is weekend now, I am wearing my hobby developer hat and I want to present a small library that I developed yesterday:
ShaderCrashingAssert provides an assert-like macro for HLSL shaders that triggers a GPU memory page fault. Together with RGD, it can help with shader debugging.
# Impressions from Vulkanised 2023 Conference
Last week I attended Vulkanised conference. It is an official conference of Vulkan API. It took place 7-9 February 2023 in Munich, Germany. It was my first time at this conference. My attendance was part of my job at AMD and I co-presented with Valve about using Radeon Developer Tools on RADV (Linux AMD driver) and Steam Deck. Here, on my blog, I would like to share my personal impressions from the event.
Overall, it was well organized. There were over 200 attendees, 3 days full of talks, most of them short (20-30 minutes, some of them even 10 minutes!), happening on just one scene (apart from full-day Vulkan tutorial for beginners, happening on the first day in parallel with normal talks), with lunch break and coffee breaks in between, so everyone could see everything without a need to choose from the timetable which talks to attend. It was intense. Every evening we went for some good food and beer, which I enjoy a lot every time I visit Munich/Bavaria/Germany.
In terms of people attending, a conference like this differs completely from game developer conferences that I usually attend. On one hand, everyone there was a programmer who knows and uses Vulkan, so everyone was on the same page. On gamedev conferences, there are people from different fields, as game development is multidisciplinary - graphics and music artists, designers, programmers, business people etc. On the other hand, there were not so many people from game industry there, and if anyone, they were mostly from the world of mobile GPUs, not PC or console. It was interesting to talk with developers from various industries, using GPUs and Vulkan for different applications, like scientific computations and visualizations or even… software for cloth design for fashion business.
There were many interesting talks. I think the most valuable ones were about components of the Vulkan ecosystem that are useful to every developer, like Vulkan validation layers, VkConfigurator, Vulkan loader, or GFXReconstruct (which also added support for Direct3D 12 recently, by the way!). There were long and extensive talks teaching two recent big additions to the API: mesh shaders and Vulkan Video. Vulkan Video seems to be especially complicated, partially because it requires some knowledge of video encoding/decoding, which is something different from 3D rendering. I used to work for television, so it was not that obscure for me. But this new part of the API is also very low level. The decision to make encoding/decoding of every frame stateless, with all the state of the video stream managed by the user, makes the API surface very extensive.
Talk about Diligent Engine was interesting. I didn’t look at the project itself, but the presentation looked convincing that this is a good multi-platform 3D graphics library implemented on top of various graphics APIs. Another interesting project presentation was about VkFFT - a C library that calculates FFT on the GPU using one of many supported APIs (not only Vulkan) with state-of-the-art performance. It is implemented by assembling a string with the source code of a kernel optimized for a specific case.
Presentations about game optimization for mobile GPUs were very interesting to me. Optimizing games is what I do in my everyday job, although I work with “large” PC GPUs. I consider such talks with a collection of tips and recommendations exceptionally valuable. From these presentations, I could learn what things work fast on smartphone and tablet chips, which are different from PC and console chips. They said that on these platforms, energy consumption and bandwidth to and from memory is the most important. Because mobile GPUs are tile-based, a large amount of vertices or fat vertex format is very slow, which is not the case on PC. Also because of that, they recommend to group as many passes as possible as sub-passes of a single Vulkan render pass, even to a degree that rendering of 3D objects could be grouped together with screen-space postprocessing effects. Again, it isn’t a thing that we normally do on PCs. It was also interesting to see how they measure performance. While I always disable V-sync and just measure FPS in games, they seem to give multiple columns with results, including FPS, but also GPU utilization %, which is likely used when reaching 60 FPS with V-sync always enabled.
But more than any specific presentation, it was interesting for me to hear some general ideas about Vulkan, often repeated by multiple people. There were people from Khronos and LunarG there (the company that develops Vulkan SDK), so we could hear from and ask questions to people who really make this API. There was a discussion panel with many prominent participants who shared their voice on these topics. Noone said “what happens on Vulkanised stays on Vulkanised”, so here are some things I remember. Disclaimer: These are my personal, subjective impressions. I might remember something wrong. Please feel free to leave a comment with your own thoughts below this article.
Some profound things have been said about Vulkan. Someone said it’s not a graphics API, more like a Hardware Abstraction Layer (HAL) or an API for programming accelerators. They said it is a “design by compromise” rather than “design by committee”. They said we should think of Vulkan as not only the specification, by the entire ecosystem, including libraries, tools, code samples, learning materials, etc. I was pleased to hear that Vulkan Memory Allocator that I maintain was often mentioned as one of the examples. An open question is how many of these 3rd party components should be considered “canonical”. Many are already included in Vulkan SDK, but should official samples use them as well? Currently, they don’t, as they teach raw Vulkan. Someone also said that these ecosystem components should be properly funded. Another question was about the direction Vulkan should go. One person said it should probably become even more low-level, with app-space libraries on top of it more widely used.
It was surprising to see that there are solutions to run Vulkan above and below every other graphics API, which makes Vulkan a common ground across systems and APIs:
Among problems that developers have with using Vulkan and potential areas of development for the future, I noticed several common themes:
Overall, participation in Vulkansed conference was a great experience for me. I wish I will come back there. But Vulkan, even with its unprecedented openness, portability, and universality, is just part of the entire world of 3D graphics programming. On a conference dedicated to Vulkan I wouldn’t say loud that Direct3D 12 is more popular among PC game developers and it is not without a reason, or that maybe both these “explicit” APIs are at the worst possible level of abstraction - low level enough to be difficult to learn, to use, and easy to create bugs, while high-level enough to still hide hardware details crucial to squeezing maximum performance. But this is a separate topic…
When attending any event, I always pay attention to the quality of the audio-video system. On Vulkanised, it was very good. I especially liked the acoustics of the room, which clearly someone paid attention to when designing the interior. But there were some issues with presentation video that I don’t see too often. I blogged before about 3 Rules to Make You Image Looking Good on a Projector, where I mentioned potential problems with contrast, reproduction of colors or thin lines. Another time I described a possibility that edges of the screen may be cropped. But this conference had a different problem. Instead of connecting their laptops to a HDMI cable, speakers were asked to join an online meeting via Google Meet and share their screen there, with presentation on the big screen by another participant of that virtual call, streaming the content. We were in a Google office, after all :) This surely helped them record the presentations easily, but it also made any video or animation degraded to what looked like 2 FPS.
For more photos, see the official gallery 2023 Vulkanised by Khronos.
# Hello World Under the Microscope - New Article Published
A Python program that prints "Hello World" on the console - what can be simpler than this? The entire program is:
Yet, together with my friends, we wrote a long article about it! When the topic is described by two security researchers skilled in reverse engineering and knowledgeable about the internals of Python interpreter, Windows operating system, and its console, together with a graphics programmer that knows how graphics and text get displayed on the screen, from Direct3D API down to the internals of a graphics card and pixels on the screen, the result is an in-depth description of the long journey this simple command makes in a computer.
The article was originally published in Polish in issue 100 (1/2022) of the Programista magazine in February 2022. Now, we prepared an English translation, and we are allowed to publish it for free on the Internet, so here it is: Hello World under the microscope. You can also download the original Polish version as PDF file or order printed version of the magazine.
# An Idea for Visualization of Frame Times
In real-time graphics applications like games, we usually measure performance as the average number of frames per second (FPS). Showing this average is a good estimate of how well the application performs, how heavy is the per-frame workload, how fast is the system where it executes, and, most importantly, whether the performance suffices for showing a smooth, good looking animation, as opposed to a "slideshow". But this is not a complete story. If some frames take an exceptionally long time, then even if others are very short, an unpleasant hitching may be visible to the player, while average FPS still looks fine. Therefore it is worth to visualize duration of individual frames on a graph, to see if they are stable.
One idea for such a graph is to draw a line connecting data points (frames), where X axis is the frame index and Y axis is the frame duration (dt), like on these pictures: "GPU Reviews: Why Frame Time Analysis is important", page 3. If such graph is shown in real time, there is one problem with it: it doesn't move at a constant pace, as the horizontal axis is expressed in frames, not seconds, so an exceptionally long frame will have the same width as super short frame. As the result, the graph will move faster the higher is the framerate.
A better idea might be to move data points horizontally with time, so that a very long frame will generate a spike on the graph with previous point many pixels away on the horizontal axis. This is what AMD OCAT tool seems to be doing. However, it results in a long, oblique line on the graph.
Overlay shown by OCAT tool
Some time ago I came up with another kind of graph. It shows every frame as a rectangle, with all its parameters: width, height, and color, dependent on the frame duration:
frameWidth = dt / (1/120). Rectangle left and right edges also need to be aligned to
ceil(), respectively, so that every frame is visible as at least 1-pixel wide.
frameHeightFactor = (log2(dt) - log2(1/120)) / (log2(1/15) - log2(1/120)). This factor then needs to be clamped to 0..1 and stretched to some range of minimum..maximum heights, depending on the intended looks of the graph, e.g. 2..64 pixels. This way, frames of a game running at 120 FPS will have minimum height, 60 FPS will be at 33%, 30 FPS - 66%, and for 15 FPS or less they will have maximum height.
I think that with this kind of graph, both average framerate and outstanding extra-long frames are clearly visible at a first glance. You can see full example source code doing all this, implemented in C++ here: Game.cpp - RegEngine - sawickiap - GitHub. It uses GLM for math functions and Dear ImGui for 2D rendering.
For example, a game with V-sync on, running at steady 60 FPS, has the graph looking like this:
While a heavier GPU workload making the game running at around 38 FPS looks like this. The graph also shows an extra-long frame that froze the entire game because of loading something from the disk, and another hitch caused by pressing PrintScreen key.
# Vulkan Memory Allocator 3.0.0 and D3D12 Memory Allocator 2.0.0
Yesterday we released new major version of Vulkan Memory Allocator 3.0.0 and D3D12 Memory Allocator 2.0.0, so if you are coding with Vulkan or Direct3D 12, I recommend to take a look at these libraries. Because coding them is part of my job, I won't describe them in detail here, but just refer to my article published on GPUOpen.com: "Announcing Vulkan Memory Allocator 3.0.0 and Direct3D 12 Memory Allocator 2.0.0". Direct links:
Vulkan Memory Allocator
D3D12 Memory Allocator
# Creative Use of GPU Fixed-Function Hardware
I recently broke my rule of posting on my blog at least once a month as I had some other topics and problems to handle in my life, but I'm still alive, still doing graphics programming for a living, so I hope to get back to blogging now. This post is more like a question rather than an answer. It is about creative use of GPU fixed-function hardware. Warning: It may be pretty difficult for beginners, full of graphics programming terms you should already know to understand it. But first, here is some background:
I remember the times when graphics cards were only configurable, not programmable. There were no shaders, only a set of parameters that could control pre-defined operations - transform of vertices, texturing and lighting of pixels. Then, shaders appeared. They evolved by supporting more instructions to be executed and a wider variety of instructions available. At some point, even before the invention of compute shaders, the term “general-purpose computing on GPU” (GPGPU) appeared. Developers started encoding some data as RGBA colors of texture pixels and drawing full-screen quads just to launch calculation of some non-graphical tasks, implemented as pixel shaders. Soon after, compute shaders appeared, so they no longer need to pretend anything - they can now spawn a set of threads that can just read and write memory freely through Direct3D unordered access views aka Vulkan storage images and buffers.
GPUs seem to become more universal over time, with more and more workloads done as compute shaders these days. Will we end up with some generic, highly parallel compute machines with no fixed-function hardware? I don’t know. But Nanite technology from the new Unreal Engine 5 makes a step in this direction by implementing its own rasterizer for some of its triangles, in form of a compute shader. I recommend a good article about it: “A Macro View of Nanite – The Code Corsair” (it seems the link is broken already - here is a copy on Wayback Machine Internet Archive). Apparently, for tiny triangles of around single pixel size, custom rasterization is faster than what GPUs provide by default.
But in the same article we can read that Epic also does something opposite in Nanite: they use some fixed-function parts of the graphics pipeline very creatively. When applying materials in screen space, they render a full-screen pass per each material, but instead of drawing just a full-screen triangle, they do a regular triangle grid with quads covering tiles of NxN pixels. They then perform a coarse-grained culling of these tiles in a vertex shader. In order to reject one, they output vertex position = NaN, which makes a triangle incorrect and not spawning any pixels. Then, a more fine-grained culling is performed using Z-test. Per-pixel material identifier is encoded as depth in a depth buffer! This can be fast, as modern GPUs apply “HiZ” - an internal optimization to reject whole groups of pixels that fail Z-test even before their pixel shaders are launched.
This reminded me of another creative use of the graphics pipeline I observed in one game a few years ago. That pass was calculating luminance histogram of a scene. They also rendered a regular grid of geometry in screen space, but with “point list” topology. Each vertex was sampling and calculating average luminance of its region. On the other end, the histogram texture of Nx1 pixels was bound as a render target. Measured luminance of a region was returned as vertex position, while incrementation of the specific place on the histogram was ensured using additive blending. I suspect this is not the most optimal way of doing this, a compute shader using atomics could probably do it faster, but it surely was very creative and took me some time to figure out what that pass is really doing and how is it doing it.
After all, GPUs have many fixed-function elements next to their shader cores. Vertex fetch, texture sampling (with mip level calculation, trilinear and anisotropic filtering), tessellation, rasterization, blending, all kinds of primitive culling and pixel testing, even vertex homogeneous divide... Although not included in the calculation of TFLOPS power, these are real transistors with compute capabilities, just very specialized. Do you know any other smart, creative uses of them?