Getting started with D3D12

Welcome to a short introduction to Direct3D 12 (also know as DX12, DirectX12 and D3D12) – the new graphics API from Microsoft, which brings new concepts to the table that have been introduced with Mantle. These new APIs could be classified as “explicit” APIs, as they have very few things that happen automatically unlike previous APIs like Direct3D 11 and OpenGL 4. In this blog post, I’ll introduce the basic concepts behind these new APIs. To follow along, I’d recommend that you check out my tiny D3D12 sample application which illustrates the techniques.

Some kind of motivation

So why did these new APIs emerge? Let’s start with a motivating example. In D3D11, you can map a buffer for writing and specify the discard flag. That flag is actually a serious problem for the GPU. Let’s assume for a moment that the buffer hasn’t been used yet, and that a frame where it will be used is queued and being processed by the GPU. The driver can’t simply overwrite the buffer in GPU memory because when you submitted the frame, it wasn’t mapped, and time travel is still quite hard.

The driver has only two choices. The na├»ve one is to simply drain the GPU and wait for it to finish. Performance will be horrible if this happens for every map call, but it will be correct. The right choice is to simply create a new buffer, put the data in there, upload it to the GPU and track the original buffer. Once the frame where the original buffer is used finishes, the original buffer can be recycled and everything is fine. Except the driver now needs to manage a new buffer per map call — tricky, but possible.

If you think that’s just an example — no, it isn’t. This buffer replacement is called buffer renaming and is a standard technique used by D3D11 drivers. Depending on how large the rename buffer is, and how often buffers are discarded, it can work quite well but it means there has to logic in the driver to manage and track this.

Going explicit

With D3D12, these things go away, and the developer is now directly exposed to memory management and synchronization. What does this mean exactly? Well, for starters, tracking of resources has to be done by the developer. If you look into my sample, you’ll notice I create “frame fences” which allow me to check if a frame has finished. For the constant buffers, I have one constant buffer per queued frame in a cheap-man’s ring buffer. Using the frame fence, I can synchronize with the GPU while still allowing the GPU queue to fill up. This removes the need for rename buffers from the driver.

Memory management is now also explicit, for instance, uploading does no longer happen “under the hood”. You’ll notice that I use two kinds of resources: Static data like the vertex and index buffer as well as the texture, and dynamic data like the constant buffer. For the dynamic data, which is read only once, it doesn’t make too much sense to push it to the GPU at all. In my sample, I hence place the constant buffer in CPU memory and let the GPU read that directly. In D3D11, the driver has to guess how often a buffer will be read and where to place it, but in D3D12, I can use the knowledge I have about my access patterns to optimize this.

The other data needs to be uploaded, and unlike D3D11 where this happens automatically, I have to do this on my own. Which means I need to reserve space on the CPU from where to stage the update, allocate some GPU memory, issue a copy and wait for it to finish before I use the resource. In the small sample, you can see that I wait for it to finish manually and hence keep everything deterministic but in a larger application I could take advantage of the copy queue and copy data independent of the rendering. This makes it easy to implement advanced streaming which was very hard to do before, as the driver can’t predict when a resource has to be resident on the GPU.

Resource state tracking

Another completely new responsibility for developers is state tracking. In D3D11, resources transition between states automatically which can lead to bad performance. Imagine the following scenario: Four shadow maps are rendered and applied onto the scene. The application renders into a shadow map, changes the target, renders into the next and so on and then finally loops over the four shadow maps and reads them. What you may not know is that GPUs compress depth data to improve bandwidth and eventually performance, but the texture units may not be able to read that compressed data directly and hence require a decompression. This decompression can potentially require a flush and wait-for-idle to make sure that the compressed data is written completely and no longer used before it gets decompressed.

Now, if the driver is not careful, this could result in a decompress, flush, read cycle, four times. The reason for this is that the driver only notices that the decompression is needed when it sees that the resource is bound for reading. With D3D12, these transitions are now explicit and the developer can schedule them. In the example above, he can choose to decompress all four shadow maps at once in a single transition, pay the cost for the flush once and improve performance.

Draw state & shaders

Another big area where the D3D11 driver spends time is setting and validating state. For instance, let’s assume you set a vertex and a pixel shader. The driver must check that the signatures of both match and this can only happen at draw time because the driver cannot precompute all permutations of vertex and pixel shaders to look this up. Often, the driver will even delay the compilation of a shader until it is used for the first time to improve startup time and easily skip unused shader. Games often have to to “pre-warm” the driver shader cache by touching all combinations once during loading to ensure that the gameplay doesn’t get interrupted when the driver starts to compile a shader.

In D3D12, this changes completely with the introduction of pipeline state objects which group all shaders and quite a bit of rendering state together. Grouping this data allows the driver to validate everything once and at runtime just swap the state without any further checks. It also means the driver can check if the pixel shader output is used at all and optimize the shader is some data is going to be discarded anyway. This is a huge change from previous APIs, and is also a major pain point when transitioning legacy engines which tend to identify the required combinations at run-time. In the D3D12 world, the shaders need to become part of the asset pipeline. In the sample, you can see how much state actually goes into the pipeline state object, even for a rather simple shader setup.

Resource binding

Finally, resource binding in the D3D12 world is totally different from D3D11. Legacy APIs tend to model the GPU as something I call the “slot machine”. You have lots of different slots where you plug in textures, samplers, etc. This used to be the case how hardware worked but it’s not true since several years. If you look for instance at the GCN ISA documentation, specifically for “image resources”, you’ll notice that there is no “sampler slot” or “texture slot” being used there. Instead, the texture and sampler descriptor is loaded into a bunch of registers and that’s it. This new model is what is used by D3D12 through the root signature and descriptor tables.

The root signature serves as the first indirection level for resource bindings. It can contain some data in-line if it is small enough — for instance, a pointer to memory (also known as a constant buffer) or a few floats, or pointers to descriptor tables that can contain larger descriptors (for instance, texture descriptors.)

It is interesting that the root signature is still tracked with renaming, but as it is generally very small, this is not a huge problem (for best performance, it should be small and some other rules should be followed as well — check out this GDC 2015 presentation on D3D12 for details.) In the sample, you can see how the texture descriptor is placed in such a table and then referenced from the root signature. Again, the goal here is to allow to change large amounts of bindings very quickly. Unlike in D3D11, where the developer changes slots and the driver needs to map them to descriptors and build the table on demand, the developer can now swap for instance all textures and samplers required by a material by updating the descriptor table pointers in the root signature — a very cheap and fast operation.

Things we didn’t look at

D3D12 also comes with explicit command buffers which allow multiple CPU threads to record commands. I’m not covering this here as the sample doesn’t take advantage of multiple threads — maybe some other time :) I’m also not covering the different queues exposed by D3D12 today. In D3D12, it is possible to execute a compute shader concurrently with draw calls and data transfers happening by taking advantage of the graphics, compute and copy queue. This is again and advanced feature which is no good fit for an introductory post.

Shadow mapping basics

Welcome to a different kind of blog post! This time, I’ll be writing an educational piece about shadow mapping for all of you who are just getting started with real-time graphics and shader programming. If you want to see the implementation, please follow the OctoAwesome live-coding project where this will be included over time.


Let’s start right away by trying to define what problem we want to solve. As of today, OctoAwesome is an outdoor game without any kind of shadows but with some kind of fixed-function illumination. For every pixel that is shaded, the lighting is evaluated even if the point is occluded by some geometry. What we want to add is the ability for a point to query whether it is occluded or not.

Some of you might shout ray-tracing. And you’re absolutely right, we will use this as our mental model to derive shadow mapping! So how would the lighting code work? If we had something like a shadow() API call in the shader, we could use that to trace a ray from the point being shaded to the light source. shadow() returns a boolean, which indicates whether the path is clear or not.

Three shadow rays cast by the sun. Two hit the green occluder before hitting the blue object which is currently shaded.

The problem here is how to implement the shadow() call. For efficient ray-tracing, we’re going to need some kind of acceleration structure and then a rather involved kernel to do the real tracing. In the case of OctoAwesome, we would probably want to trace a binary volume for maximum efficiency. We’d also need some special way to handle transparent blocks like the trees and animated objects. Not impossible but a lot of work.

We’ll notice pretty quickly that for each frame, we have to trace a lot of rays and that gets expensive due to the traversal. It’s even worse as we trace the same rays over and over, at least as long the light source and the camera is not moving. This is surely not the most efficient way; it feels as if we should be able to store and reuse the results of the shadow() calls somehow.


And indeed, there is a way to reuse shadow() calls. The key insight is that we can store one value per ray to resolve the shadow() query for all points along the ray. The value we store is the distance to the closest hit, and our new shadow() call now just checks the distance of the query point against the closest point. If further away, it is in shadow.

Ray-tracing with a cache. The blue/green cell stores the values for all rays passing through it. The two top points on the blue object are tested against the blue cell, and one of them is classified as lit even though it should be in shadow. Nitpick: If done with utmost precision, actually all points would be classified as occluded as all three have a larger depth value. Generally, a small epsilon (bias) is introduced to avoid self-shadowing; in the example above, it’s large enough to fix the upper point on the blue object from getting shadowed by itself.

Now the only problem is how to store the “per-ray” data, which are now cast from arbitrary points. For a distant light source, imagine we place a grid orthogonal to the light direction and quantize rays into small “cells”. That is, all rays which are emitted “nearby” will go into the same cell. This introduces a bit of error, depending on how big our cells are and other factors, but in general it’s quite acceptable.


What we need to do now is to produce a grid of distance values from the light source, store this somehow, and during shading, project the points into that grid and compare the values. Turns out the GPU is perfectly suited for this. Producing distance values is exactly what we do when writing into the depth buffer. Storing is equally easy, we can re-use a depth buffer as a texture. The only remaining problem is to project the points into the shadow map and compare the values.

Let’s tackle the problems one-by-one. First of all, we need a new camera, which captures the scene as seen from the light into a depth map. This means we need to create a new render target, which has only a depth buffer bound. We also need to set up the camera correctly: It should cover the complete view frustum and nothing else, to maximize the effective resolution. Finally, when rendering, we should turn off all pixel shaders to improve performance. Of course, for alpha-tested geometry, we need the shaders, but if possible we should use a simplified version which only calls discard() while generating the shadow map.

The next step is the normal render pass in which we need to implement the shadow() call. For this to work, we have to project the point being shaded into the shadow map, that is, it has to go from world-space into the light-space using the same projection as we used to generate the shadow map. This means we need to pass-through the world-space position somehow through the vertex shader, the easiest way is to simply forward it and then multiply with the light projection in the pixel shader. One division, one adjustment for the -1..1 to 0..1 coordinate system difference, and a comparison later and we know if the point is in shadow!


The solution above will work in practice but result in quite ugly, blocky shadows. We can achieve higher quality by using a comparison sampler and linear filtering, which will enable hardware-accelerated percentage closer filtering.

I’ve also omitted lots of other problems we’re going to run into. For instance, the shadow map resolution should be improved by using cascaded shadow maps, we should use some kind of contact hardening shadows to make the shadows softer further away, and so on. Good shadow mapping is actually really hard to implement, due to the fixed precision and resolution of shadow maps, but right now, it’s the best we can do until the hardware becomes fast enough to trace soft-shadows in real-time. This will be hopefully the topic of a future blog post :)

Scratching your itch: Side projects

Everyone has them, small side projects which somehow never want to get finished. That small tool you wrote to convert some ancient file format into a newer one. The tiny hack to an image library to add support for scanline offset caching to improve TGA loading performance. Small things which make a library much more usable for a particular use case, or tiny tools which help with corner cases that only few people run into.

What do these side projects have in common? First of all, I guess the majority of them never gets released, which means we’re all going to have our small hack to some image library, our own small converter for ancient file formats and other tiny pieces of code on which we hack occasionally. Well, not really occasionally, but every time we run into a new bug or when we find a new use-case. Second, these side projects take away increasing amounts of time and mental capacity as you have this lingering feeling at the back of your head that you should really “finish” this at some point, but you simply don’t have the time to do it “properly”.

I know this feeling very well. Doing it “properly” means for us to have all know issues fixed, have good documentation, port it to all platforms under the sun and ideally have 100% test coverage. After all, it’s a side project, so at least here we can do everything right, right? Here, we can be the programmer we want everyone to believe we are, writing perfect code.

So how can we solve this dilemma? The first step is to understand that even non-perfect code can solve problems, especially if the problem domain is very small. If all you need is to decompress DXT images, a bunch of C-functions with inline comments might not be the packaged library that you would like to see, but it does solve the problem for people. If anyone has the urgent need to decompress DXT, he will use that library, and chances are high he’ll contribute support for that one more format he cares about. This assumes that the code is out there in the first place!

The five-minute guide to release your side project is quite easy, but what you need to do is to prepare yourself to spend some time on the release itself. Not polishing the code, not fixing crazy corner cases, but doing the stuff that really matters:

  • Signing up at some web code repository: Bitbucket, Github, everything else doesn’t matter.
  • Get familiar with Mercurial or git. If your code uses a different revision control system, export and reimport now. Currently, only those two revision control systems matter, with a strong bias on git.
  • Decide on the license to use. BSD or MIT is the license of choice if you want people to use your code. GPL may be acceptable for Python or other script languages where you have to release the whole source anyway, but BSD or MIT is better still.
  • Write a readme: What problems does this code solve, on what system does it run, how do I compile it. The readme is crucial for search engines to find your code. Use something like Markdown or ReStructuredText for it so the plain text can be parsed easily.
  • Decide on a name: You don’t want to rename your project and loose your search engine rank. Check first if the name is already in use, calling your SQL database my-SQL-DB might not work as expected.
  • Write docs if needed: Don’t waste time on docs unless they are really needed. If needed, use Sphinx or something else which is readable in plain text if people don’t build the docs. Sphinx is great as there are web services like ReadTheDocs which you can point people to.
  • If you wrote a sufficiently self-contained library and there’s a distribution system for your language, do the extra leg work to publish your library on the packaging system. For Python, that would be PyPI, for C#, you probably want NuGet to work and for JavaScript npm is your friend. For very small projects, you can skip this.
  • Mark the current version as 1.0. If you don’t feel like 1.0, fix the most urgent bugs and push. If your stuff works, and doesn’t crash on every corner, go ahead with 1.0. I know this might sound a bit crazy (hey, 1.0 means stable, right?) but the sad truth about your pet project is that the current state is probably as stable as it will get (remember? You only fix critical bugs anyway), and there won’t be a “future” proper release. So you can go ahead and call it 1.0 just as well, and other people are more likely to use it. If you see a project hanging around at 0.1 for 3 years, you assume it’s dead, wasn’t used for anything and someone simply forgot to delete it.
  • Most importantly: Ship it! Your code is ready, don’t waste time. If it’s not good but useful people will tell you, and then you can improve stuff that really matters.

The steps above will likely take you something on the order of a few hours for your first project, and less than one hour later on. If you are spending a significant amount on writing docs, packaging or fixing bugs, then your side-project is probably quite big and not really solving one problem any more. Then you’re in framework or application development which is an area where people are much more unlikely to use your code snippet, and you really need to nail a lot of things before you can release stuff. In this case, this post is not for you!

One great example of such a small, reusable library is the stb lib. It’s a bunch of solutions to common problems which you can easily integrate into your own application. However, I’m sure you all have similar code lying around just waiting to get pushed to the web to the benefit of others! So go ahead, give it the small “release polishing” and share it with all of us — thank you!