OpenGL 4.0 - Mountains demo

OpenGL 3.0 capable GPUs introduced a level of processing power and programming flexibility that isn’t comparable with any earlier generations. After that, OpenGL 4.0 and the hardware supporting it even further pushed the limits of what previously seemed to be impossible. Thanks to these features nowadays more and more possibilities are available for the graphics developers to implement GPU based scene management and culling algorithms. The Mountains demo showcases some of these rendering techniques that, as far as I know, were never implemented so far using OpenGL. In this article I will present the key features of the demo that will be discussed in more detail in subsequent articles. Demo binaries with full source code are also published.

The demo itself is mainly inspired by the March of the Froblins demo released by AMD and the SIGGRAPH 2008 Course Notes by Jeremy Shopf, Joshua Barczak, Christopher Oat and Natalya Tatarchuk presenting the actual implementation in detail. That demo targeted the Radeon HD4800 series and presented several practical GPU based culling algorithms implemented using DirectX10. The Mountains demo implements these techniques in OpenGL and further improves the technique used in AMD’s demo by unleashing the new features introduced by Shader Model 5.0 hardware and OpenGL 4.0.

While this article briefly presents the demo and the used rendering techniques, the details of each individual technique will be presented in subsequent articles as the thorough examination of them needs a longer discussion that would render this article simply too long and overwhelming.

Introduction

The Mountains demo renders a tiled terrain block with thousands of high detail tree models (the full detail tree model is over five thousand triangles). Due to the view distance used in the demo is quite large, several tiles of the terrain block are potentially visible on the screen and this results in a huge explosion in the number of triangles the GPU has to render. Also, with traditional methods the rendering of the terrain blocks and the several thousand tree models would need loads of draw calls. In order to solve this problem, the demo renders the trees using geometry instancing to minimize the number of draw calls.

In a traditional rendering engine CPU based culling methods would be used. While that would even work in practice, it is more convenient to perform the culling on the GPU as every information needed to do it is available there. Nevertheless, culling is a typical algorithm that can easily take advantage of the highly parallel architecture of the GPU. Also, performing the culling on the CPU would make geometry instancing barely beneficial.

Another problem with a scene like this is that a simple per-object view frustum culling would not solve the problem completely as most of trees in the view frustum are not visible due that they are hidden by the terrain. In traditional OpenGL the way how to solve this problem would be the use of per-object occlusion queries and rendering of bounding volumes. While this may work in practice, it involves too much CPU intervention even if we take advantage of conditional rendering and nevertheless, this also breaks instancing.

These are the issues that motivated me in creating this demo and I established the following goals for the project:

View from above

• All the object-level information must stay on the GPU and the CPU should not make decisions on a per-object basis.
• The renderer should use as few draw calls as possible in order to solve the problem of visibility determination.
• Don’t draw anything that is not inside the view frustum or is occluded by terrain.

The result is a renderer that does little to no scene management on the CPU, instead uses the GPU for visibility determination that is, in most cases, able to reduce the scene’s geometric complexity from over 400 million triangles under one million triangles providing an interactive experience on a Radeon HD5770 with around 200 frames per second.

Implementation

The scene consists of a tiled terrain with over 130 thousands of triangles and more than 1400 tree instances each with almost 6 thousands of triangles. This sums up to 8 million triangles for a single tile block of terrain. As the view range is needed to be quite large we actually deal with a 7×7 tile of terrain that is dynamically placed in a way that the camera always resides in the middle block of the tile. What all this means that even though we dynamically generate the scenery around the camera, we still have to deal with a scene consisting of over 400 million triangles. This is simply too much for the GPU to deal with.

The first step done in order to reduce the geometric complexity of the scene is done on the CPU by performing a view frustum culling on a per-terrain-block basis. This will limit our 7×7 tile to a smaller subset that contains only those blocks that are lying within the view frustum. The result is a scene usually around 50 million triangles.

While this is already a reasonable amount of simplification, in order to further reduce the amount of geometry we have to render we have to do per-object culling. But as mentioned before, we would not like to do such fine grained scene management on the CPU so we need some sophisticated methods to do it on the GPU.

In order to accomplish this, we will take advantage of the geometry shader’s capability of discarding geometry. We will use it to do the per-object decisions in order to cull the tree instances that are not visible. The three techniques implemented in the culling geometry shader and the accompanying vertex shader are the following:

• Instance Cloud Reduction (ICR) – This method does view frustum culling on a per-instance basis based on the bounding box of the instanced geometry, in this case the tree. The technique was first presented in my previous article titled Instance culling using geometry shaders and then further improved according to the instructions presented in Instance Cloud Reduction reloaded. In this case, the technique allows us to do a more fine grained yet still high level view frustum culling of the tree instances than that allowed by the simple per-tile culling performed on the CPU.
• Hierarchical-Z Map based Occlusion Culling – This technique allows for conservative per-instance occlusion culling completely done and evaluated on the GPU using a similar algorithm that the hardware depth buffer uses to hierarchically reject fragments based on their depth values. Using this technique, a coarse occlusion culling can be performed on the instances without the need of occlusion queries and CPU intervention. Update! The technique is discussed in detail in the article Hierarchical-Z map based occlusion culling.
• Dynamic Level-of-Detail Determination – This method allows us to dynamically select a suitable geometry level-of-detail on a per-instance basis completely on the GPU based on the application provided LOD parameters and the distance of the instance from the camera. The Mountains demo uses three LOD levels for the tree object: one with 5811 triangles, another with 2893 triangles and the lowest detailed version contains 1492 triangles. Update! The technical details of the algorithm are presented in the article GPU based dynamic geometry LOD.

While in the Mountains demo all these techniques are used to determine the visibility and the LOD of static scenery (as trees are unlikely to move) the truth is that these methods apply with no modification also to dynamic scenery. This is a very important thing to note as usually dynamic objects are those that makes many of the CPU based scene management and visibility determination algorithms difficult to use or simply inefficient.

The key improvement compared to how these techniques are used in AMD’s demo is that my implementation applies all the algorithms to the instance set in a single rendering pass compared to the several passes needed by the original implementation. This is because the Mountains demo takes advantage of the latest technologies introduced by OpenGL 4.0 and the supporting hardware (in this case the functionality provided by the extension GL_ARB_transform_feedback3).

By using these techniques the GPU is able to reduce the geometric complexity of the scene from 50 million triangles down to around a few millions, sometimes even under a million. Of course, the actually reduction efficiency is heavily influenced by the view position and direction.

Besides the scene management and visibility determination techniques, the demo also showcases a few simple visual effects:

View horizon and sky

• A simple infinitely far skybox generated using a geometry shader.
• Simple diffuse lighting applied to the tree instances.
• Global illumination-like effect that simulates the terrain to cast shadows over the trees even though no shadow rendering technique is applied.
• Fog effect to smooth out the disappearance of the terrain at the far clip plane.
• Simplistic fake depth-of-field effect that makes far away objects look blurry.

Maybe I will present also some of these techniques in detail in another article if there is interest for it.

As I mentioned, I used a geometry shader to render the skybox and so I did when rendering full screen quads to apply image space algorithms. I’ve done this because I always feel kind of stupid when I have to put such a simple geometry like a skybox or a full screen quad into a vertex buffer. In these situations I feel like I would simply use immediate mode to draw that damn little piece of geometry but I want to stick to core OpenGL so I quickly change my mind. As a simple alternative, I rather used geometry shaders to emit these simple geometric objects that are used so often that I even wonder how OpenGL does not have e.g. a glDrawScreenQuad-like command. Of course, the geometry shaders don’t start by themselves so I used dummy draw commands to make the geometry shader do its job.

Performance

Now let’s see how our GPU based optimizations perform in practice. I’ve collected results from typical view positions from where a moderate number of trees are visible. The tests were done on a Radeon HD 5770. Other configuration parameters are not really relevant as the demo is clearly GPU bound as only a few state changes and render commands are executed on the CPU. Of course, this is kind of a synthetic demo as you would usually want to balance the workload between the CPU and the GPU but usually you have AI, physics and other things for the CPU so transferring as much work to the GPU as possible usually gives a great benefit.

Performance comparison of the demo in frames per second on a Radeon HD5770 (higher is better): no culling (bottom), instance cloud reduction (middle), ICR + Hi-Z map based occlusion culling (top), no geometry LOD (blue), dynamic geometry LOD (red).

As you can see on the figure above, using all the optimizations clearly shows its benefits on the frame rate of the demo, even though the Hi-Z map based occlusion query requires several additional draw passes due to the construction of the Hi-Z map. It is also clearly visible that in a scene like this where there are a lot of occluders, ICR is simply not sufficient on its own. One final note that the application of dynamic LOD has a more significant effect without Hi-Z as occlusion culling removes the largest ratio of the instances.

Amount of visible geometry after culling in millions of triangles: no culling (bottom), instance cloud reduction (middle), ICR + Hi-Z map based occlusion culling (top), no geometry LOD (blue), dynamic geometry LOD (red).

Our next chart shows the amount of geometry that is finally drawn after culling in millions of triangles. On this figure we see exactly the inverse of the previous chart and it is not surprising as obviously we have a geometry throughput bottleneck. It also clearly shows how important dynamic LOD is even if we don’t perform more sophisticated visibility determination algorithms.

Finally, in the table above we’ve listed the number of draw calls needed by each technique from the reference point of view. The techniques applied do not have a significant effect on the amount of draw calls: we have a fixed number of draw calls and additionally two draw calls if we use LOD. The only exception is when we use Hi-Z map based occlusion culling as the Hi-Z map is a full mipmap chain and we need ten additional draw calls to generate all the mip-levels.

Conclusion

The techniques presented are rather simple to implement and can provide huge performance increases. Nevertheless, they allow the renderer to offload even some of the object-level algorithms from the CPU to the GPU and obviously this is the direction to go in the future.

We’ve also met mostly our goals set at the beginning. Of course not fully as the occlusion culling performed is rather a coarse culling method and does not eliminate completely all the instances that will not contribute to the final image.

Future work

While the implementation almost completely eliminates all need of CPU intervention during the rendering phase, I still had to use a few asynchronous queries to get the amount of visible instances for each geometry LOD, although the latency incurred by the use of query objects is hidden in the demo by rendering the skybox between the initiation of the queries and the retrieving of the results.

Deep in the forest

As soon as we get atomic counters into core OpenGL and consequently when we’ll have drivers supporting it, I will further improve the technique using indirect rendering and atomic counters so even the need for these queries will be eliminated.

Additionally, as mentioned several times, I plan to write detailed articles about the individual techniques I used in the demo. I decided to go in this direction as a thorough description of all the details of the demo would be simply too long in one piece.

Running the demo

The demo uses OpenGL 4.0 so a Shader Model 5.0 capable graphics card is a must. Even though most of the used techniques makes it possible to create an implementation running on OpenGL 3.x, this time I wanted to stick to GL 4.0 as I took advantage of the new features of it to even further improve the implementation.

First, don’t be afraid if after startup the demo will run on very low frame rates. This is because by default all GPU based optimizations are disabled.

You can use the SPACE button to switch between the various culling methods:

• No culling at all
• Instance cloud reduction
• ICR with Hi-Z map based occlusion culling

Finally, you can turn dynamic LOD on and off using the F3 key.

There are a few other controls present in the demo that you may figure out if you read the code, but I don’t want to go into the details of them as they will be presented in the upcoming articles where I will present Hi-Z map based occlusion culling and dynamic LOD in detail. So stay tuned: follow me on twitter or subscribe to the RSS feed.

The demo can be downloaded with full source code in the downloads section.

Gaussian blur

Gaussian blur is an image space effect that is used to create a softly blurred version of the original image. This image then can be used by more sophisticated algorithms to produce effects like bloom, depth-of-field, heat haze or fuzzy glass. In this article I will present how to take advantage of the various properties of the Gaussian filter to create an efficient implementation as well as a technique that can greatly improve the performance of a naive Gaussian blur filter implementation by taking advantage of bilinear texture filtering to reduce the number of necessary texture lookups. While the article focuses on the Gaussian blur filter, most of the principles presented are valid for most convolution filters used in real-time graphics.

Gaussian blur is a widely used technique in the domain of computer graphics and many rendering techniques rely on it in order to produce convincing photorealistic effects, no matter if we talk about an offline renderer or a game engine. Since the advent of configurable fragment processing through texture combiners and then using fragment shaders the use of Gaussian blur or some other blur filter is almost a must for every rendering engine. While the basic convolution filter algorithm is a rather expensive one, there are a lot of neat techniques that can drastically reduce the computational cost of it, making it available for real-time rendering even on pretty outdated hardware. This article will be most like a tutorial article that tries to present most of the available optimization techniques. Some of them may be familiar to all of you but maybe the linear sampling will bring you some surprise, but let’s not go that far but start with the basics.

Terminology

In order to precede any possibility of confusion, I’ll start the article with the introduction of some terms and concepts that I will use in the post.

Convolution filter – An algorithm that combines the color value of a group of pixels.

NxN-tap filter – A filter that uses a square shaped footprint of pixels with the square’s side length being N pixels.

N-tap filter – A filter that uses an N-pixel footprint. Note that an N-tap filter does *not* necessarily mean that the filter has to sample N texels as we will see that an N-tap filter can be implemented using less than N texel fetches.

Filter kernel – A collection of relative coordinates and weights that are used to combine the pixel footprint of the filter.

Discrete sampling – Texture sampling method when we fetch the data of exactly one texel (aka GL_NEAREST filtering).

Linear sampling – Texture sampling method when we fetch a footprint of 2×2 texels and we apply a bilinear filter to aquire the final color information (aka GL_LINEAR filtering).

Gaussian filter

The image space Gaussian filter is an NxN-tap convolution filter that weights the pixels inside of its footprint based on the Gaussian function:

The pixels of the filter footprint are weighted using the values got from the Gaussian function thus providing a blur effect. The spacial representation of the Gaussian filter, sometimes referred to as the “bell surface”, demonstrates how much the individual pixels of the footprint contribute to the final pixel color.

The graphical representation of the 2-dimensional Gaussian function

Based on this some of you may already say “aha, so we simply need to do NxN texture fetches and weight them together and voilà”. While this is true, it is not that efficient as it looks like. In case of a 1024×1024 image, using a fragment shader that implements a 33×33-tap Gaussian filter based on this approach would need an enormous number of 1024*1024*33*33 ≈ 1.14 billion texture fetches in order to apply the blur filter for the whole image.

In order to get to a more efficient algorithm we have to analyze a bit some of the nice properties of the Gaussian function:

• The 2-dimensional Gaussian function can be calculated by multiplying two 1-dimensional Gaussian function:

• A Gaussian function with a distribution of 2σ is equivalent with the product of two Gaussian functions with a distribution of σ.

Both of these properties of the Gaussian function give us room for heavy optimization.

Based on the first property, we can separate our 2-dimensional Gaussian function into two 1-dimensional one. In case of the fragment shader implementation this means that we can separate our Gaussian filter into a horizontal blur filter and the vertical blur filter, still getting the accurate results after the rendering. This results in two N-tap filters and an additional rendering pass needed for the second filter. Getting back to our example, applying the two filters to a 1024×1024 image using two 33-tap Gaussian filters will get us to 1024*1024*33*2 ≈ 69 million texture fetches. That is already more than an order of magnitude less than the original approach made possible.

Using the second property of the Gaussian function, we can separate our 33×33-tap filter into three 9×9-tap filter (9+8=17, 17+16=33). Back to our example, for the 1024×1024 sized image this results in 1024*1024*9*9*3 ≈ 255 million texture fetches. As we can see, we also spared a large amount of the necessary texture fetches using this approach as well.

Of course, the combination of the two techniques is also possible. That means we both separate our filter to a vertical and horizontal filter as well as decompose our 33-tap filter into three 9-tap filter. This will get us to the almost optimal number of 1024*1024*9*3*2 ≈ 56 million texture fetches.

Gaussian kernel weights

We’ve seen how to implement an efficient Gaussian blur filter for our application, at least in theory, but we haven’t talked about how we should calculate the weights for each pixel we combine using the filter in order to get the proper results. The most straightforward way to determine the kernel weights is by simply calculating the value of the Gaussian function for various distribution and coordinate values. While this is the most generic solution, there is a simpler way to get some weights by using the binomial coefficients. Why we can do that? Because the Gaussian function is actually the distribution function of the normal distribution and the normal distribution’s discrete equivalent is the binomial distribution which uses the binomial coefficients for weighting its samples.

The Pascal triangle showcasing the binomial coefficients that can be used to calculate the kernel weights (each element in the succeeding rows is the sum of its "parents").

For implementing our 9-tap horizontal and vertical Gaussian filter we will use the last row of the Pascal triangle illustrated above in order to calculate our weights. One may ask why we don’t use the row with index 8 as it has 9 coefficients. This is a justifiable question, but it is rather easy to answer it. This is because with a typical 32 bit color buffer the outermost coefficients don’t have any effect on the final image while the second outermost ones have little to no effect. We would like to minimize the number of texture fetches but provide the highest quality blur as possible with our 9-tap filter. Obviously, in case very high precision results are a must and a higher precision color buffer is available, preferably a floating point one, using the row with index 8 is better. But let’s stick to our original idea and use the last row…

By having the necessary coefficients, it is very easy to calculate the weights that will be used to linearly interpolate our pixels. We just have to divide the coefficient by the sum of the coefficients that is 4096 in this case. Of course, for correcting the elimination of the four outermost coefficients, we shall reduce the sum to 4070, otherwise if we apply the filter several times the image may get darker.

Now, as we have our weights it is very straightforward to implement our fragment shaders. Let’s see how the vertical file shader will look like in GLSL:

uniform sampler2D image;

out vec4 FragmentColor;

uniform float offset[5] = float[]( 0.0, 1.0, 2.0, 3.0, 4.0 );
uniform float weight[5] = float[]( 0.2270270270, 0.1945945946, 0.1216216216,
                                   0.0540540541, 0.0162162162 );

void main(void)
{
    FragmentColor = texture2D( image, vec2(gl_FragCoord)/1024.0 ) * weight[0];
    for (int i=1; i<5; i++) {
        FragmentColor +=
            texture2D( image, ( vec2(gl_FragCoord)+vec2(0.0, offset[i]) )/1024.0 )
                * weight[i];
        FragmentColor +=
            texture2D( image, ( vec2(gl_FragCoord)-vec2(0.0, offset[i]) )/1024.0 )
                * weight[i];
    }
}

Obviously the horizontal filter is no different just the offset value is applied to the X component rather than to the Y component of the fragment coordinate. Note that we hardcoded here the size of the image as we divide the resulting window space coordinate by 1024. In a real life scenario one may replace that with a uniform or simply use texture rectangles that don’t use normalized texture coordinates.

If you have to apply the filter several times in order to get a more strong blur effect, the only thing you have to do is ping-pong between two framebuffers and apply the shaders to the result of the previous step.

9-tap Gaussian blur filter applied to an image of size 1024x1024: no filter applied (left), applied once (middle), applied nine times (right). Click to view the full-sized image in order to better see the difference.

Linear sampling

So far, we were able to see how to implement a separable Gaussian filter using two rendering pass in order to get a 9-tap Gaussian blur. We’ve also seen that we can run this filter three times over a 1024×1024 sized image in order to get a 33-tap Gaussian blur by using only 56 million texture fetches. While this is already quite efficient it does not really expose any possibilities of the GPUs as this form of the algorithm would work perfectly almost unmodified on a CPU as well.

Now, we will see that we can take advantage of the fixed function hardware available on the GPU that can even further reduce the number of required texture fetches. In order to get to this optimization let’s discuss one of the assumptions that we made from the beginning of the article:

So far, we assumed that in order to get information about a single pixel we have to make a texture fetch, that means for 9 pixels we need 9 texture fetches. While this is true in case of a CPU implementation, it is not necessarily true in case of a GPU implementation. This is because in the GPU case we have bilinear texture filtering at our disposal that comes with practically no cost. That means if we don’t fetch at texel center positions our texture then we can get information about multiple pixels. As we already use the separability property of the Gaussian function we actually working in 1D so for us bilinear filter will provide information about two pixels. The amount of how much each texel contribute to the final color value is based on the coordinate that we use.

By properly adjusting the texture coordinate offsets we can get the accurate information of two texels or pixels using a single texture fetch. That means for implementing a 9-tap horizontal/vertical Gaussian filter we need only 5 texture fetches. In general, for an N-tap filter we need [N/2] texture fetches.

What this will mean for our weight values previously used for the discrete sampled Gaussian filter? It means that each case we use a single texture fetch to get information about two texels we have to weight the color value retrieved by the sum of the weights corresponding to the two texels. Now that we know what are our weights, we just have to calculate the texture coordinate offsets properly.

For texture coordinates, we can simply use the middle coordinate between the two texel centers. While this is a good approximation, we won’t accept it as we can calculate much better coordinates that will result us exactly the same values as when we used discrete sampling.

In case of such a merge of two texels we have to adjust the coordinates that the distance of the determined coordinate from the texel #1 center should be equal to the weight of texel #2 divided by the sum of the two weights. In the same style, the distance of the determined coordinate from the texel #2 center should be equal to the weight of texel #1 divided by the sum of the two weights.

As a result, we get the following formulas to determine the weights and offsets for our linear sampled Gaussian blur filter:

By using this information we just have to replace our uniform constants and decrease the number of iterations in our vertical filter shader and we get the following:

uniform sampler2D image;

out vec4 FragmentColor;

uniform float offset[3] = float[]( 0.0, 1.3846153846, 3.2307692308 );
uniform float weight[3] = float[]( 0.2270270270, 0.3162162162, 0.0702702703 );

void main(void)
{
    FragmentColor = texture2D( image, vec2(gl_FragCoord)/1024.0 ) * weight[0];
    for (int i=1; i<3; i++) {
        FragmentColor +=
            texture2D( image, ( vec2(gl_FragCoord)+vec2(0.0, offset[i]) )/1024.0 )
                * weight[i];
        FragmentColor +=
            texture2D( image, ( vec2(gl_FragCoord)-vec2(0.0, offset[i]) )/1024.0 )
                * weight[i];
    }
}

This simplification of the algorithm is mathematically correct and if we don’t consider possible rounding errors resulting from the hardware implementation of the bilinear filter we should get the exact same result with our linear sampling shader like in case of the discrete sampling one.

9-tap Gaussian blur applied nine times with discrete sampling (left) and linear sampling (right). Click for the full resolution of the image. Note that there is no visible difference between the two techniques even after several passes.

While the implementation of the linear sampling is pretty straightforward, it has a quite visible effect on the performance of the Gaussian blur filter. Taking into consideration that we managed to implement a 9-tap filter using just five texture fetches instead of nine, back to our example, blurring a 1024×1024 image with a 33-tap filter takes only 1024*1024*5*3*2 ≈ 31 million texture fetches instead of the 56 million required by discrete sampling. This is a quite reasonable difference and in order to better present how much that matters I’ve done some experiment to measure the difference between the two techniques. The result speaks for itself:

Performance comparison of the 9-tap Gaussian blur filter with discrete and linear sampling on a Radeon HD5770. The vertical axis is the frames per second (higher is better) and the horizontal axis represents results with various number of blur steps (higher is blurrier).

As we can see, the performance of the Gaussian filter implemented with linear sampling is about 60% faster than the one implemented with discrete sampling indifferent from the number of blur steps applied to the image. This roughly proportional to the number of texture fetches spared by using linear filtering.

Conclusion

We’ve seen that implementing an efficient Gaussian blur filter is quite straightforward and the result is a very fast real-time algorithm, especially using the linear sampling, that can be used as the basis of more advanced rendering techniques.

Even though we concentrated on Gaussian blur in this article, many of the discussed principles apply to most convolution filter types. Also, most of the theory applies in case we need a blurred image of reduced size like it is usually needed by the bloom effect, even the linear sampling. The only thing that is really different in case of a reduced size blurred image is that our center pixel is also a “double-pixel”. This means that we have to use a row from our Pascal triangle that has even number of coefficients as we would like to linear sample the middle texels as well.

We’ve also had a brief insight into the computational complexity of the various techniques and how the filter can be efficiently implemented on the GPU.

The demo application used for the measurements performed to compare the discrete and linear sampling method can be downloaded here:

Binary release

Platform: Windows
Dependency: OpenGL 3.3 capable graphics driver
Download link: gaussian_win32.zip (2.96MB)

Source code

Language: C++
Platform: cross-platform
Dependency: GLEW, SFML, GLM
Download link: gaussian_src.zip (5.37KB)

P.S.: Sorry for the high minimum requirements of the application just I would really like to stick to strict OpenGL 3+ demos.

OpenGL 3.3 - Nature

A few months ago I’ve presented an object culling mechanism that I’ve named Instance Cloud Reduction (ICR) in the article Instance culling using geometry shaders. The technique targets the first generation of OpenGL 3 capable cards and takes advantage of geometry shaders’ capability to reduce the emitted geometry amount in order to get to a fully GPU accelerated algorithm that performs view frustum culling on instanced geometry without the need of OpenCL or any other GPU compute API. After the culling step the reduced set of instance data is fed to the drawing pass in the form of a texture buffers. In this article I will present an improved version of the algorithm that exploits the use of instanced arrays introduced lately in OpenGL 3.3 to further optimize it.

Lets recap the basics of the algorithm before I present the improved technique. The geometry shaders have a very nice feature that they cannot just emit a modified version of the input geometry but can also alter the number of emitted primitives compared to the number of received ones. This is a both-way ability what means that we cannot just increase but also decrease the number of primitives. That is what the technique takes advantage.

In the first pass we feed a simple vertex shader – geometry shader pair with the instance data of the geometries as they’ve been the data of point primitives. The vertex shader then checks whether the actual instance is inside the view frustum or not and sends the result to the geometry shader. If the result is yes then the geometry shader outputs the instance data otherwise discards it. The primitives emitted by the geometry shaders are captured then using transform feedback into a buffer object. Also a query object is needed in order to be able to get the amount of instances that passed the view frustum culling. In the drawing pass we use the result of the query to decide how many instances we have to draw and the captured feedback buffer is used as instance data.

Instance Cloud Reduction - Combined view of Pass 1 + Pass 2

This is a very brief description of the culling mechanism so for a complete specification please read the original article.

Motivation

While Instance Cloud Reduction is a quite robust technique that can severely simplify and speed up the rendering of high amount of instanced geometry its performance is also limited due to some hardware and API restrictions. The most important ones are the following:

• Needs an extra rendering pass to perform the culling.
• Requires the usage of asynchronous queries to determine the number of visible instances.
• Uses texture fetching in the vertex shader of the actual drawing pass.

The first mentioned drawback means that more draw commands are required that use the output of the first pass as input. This and the second disadvantage may cause stalls due to the fact that the CPU has to wait for the data to be ready before issuing the second pass thus the GPU is not used effectively.

What this improvement tries to solve is the third problem. Texture fetching itself is quite fast in the latest generation of hardware, however it causes some slowdowns anyway due to the latency introduced by texture fetches even though GPUs use some latency hiding techniques.

Instanced arrays provide us a way to replace texture fetching with vertex fetching that is usually done by different hardware element that works synchronously with the execution of vertex shaders. I’ve expected quite a reasonable speedup by taking advantage of instanced arrays, however we will see that actual results were far from my initial expectations.

Implementation

Traditional vertex fetching happens in a way that one element is fetched from each enabled input attribute buffer and the vertex shader is issued with these values. One element in a vertex attribute buffer can mean up to four floating point or integer values and for each execution of the vertex shader one set of these elements is used. There is an internal counter that is increased after each fetch and the next vertex attribute fetch will use this counter as an index into the buffer object.

While this mechanism is satisfactory for the most attributes of a vertex, it is not practical for instance data as such data belongs to an instance rather than a vertex. In order to source instance data from vertex attributes in case of traditional vertex fetching, high amount of redundant storage is required in order to get the same information for all the vertices belonging to a particular instance. This is not just waste of memory but also waste of bandwidth and it also defeats the goal of Instance Cloud Reduction.

Compared to traditional vertex fetching, instanced arrays provide a way to increase the internal counter used as the index into the vertex attribute buffer in a different way, in particular one can set the frequency of increase using a vertex attribute divisor that specifies after how many instances the counter shall be increased. This is a per-attribute property and by setting it to one we end up with exactly what we need: one vertex fetch per instance.

This means that actually we need just a very minor change compared to the original technique, more precisely we replace our texture buffer with a vertex attribute buffer that has a divisor of one and use it as the source of instance data in the vertex shader of the drawing pass.

Execution results

As we are not talking about a new technique but just an optimized implementation of the same method, the best way to evaluate it is by comparing the performance of the new version with the original one.

As I’ve mentioned earlier, I expected a reasonable performance increase by replacing texture fetches with vertex fetches, in practice the difference was not so significant. However, the performance difference between the two implementation can heavily depend on the underlying hardware implementation so various cards from various vendors and GPU generations can show more diverging behavior. In fact even driver versions may have an effect on the results.

Performance comparison of the old implementation and the presented one on an AMD Radeon HD5770. Scale is in frames per second (higher value is better).

Due to lack of hardware to use for testing, I’ve checked only with one card, namely a Radeon HD5770 with Catalyst 10.6 drivers. I noticed roughly a 10% speedup as the the new version of the Nature demo showed 100 FPS compared to the 90 FPS observed with the old implementation.

Even though this was not exactly the outcome I’ve expected from the new implementation, maybe the assumption is still valid for older generation of GPUs or for NVIDIA cards. I suspect so because for Shader Model 4.0 cards the hardware implementation of the texture fetching unit and the vertex fetching unit was most probably more differentiated than that of the latest GPUs. Also my guess is that on NVIDIA cards the difference is maybe higher as the vertex fetching hardware in SM 4.0 GeForce cards is less flexible than that of AMD’s taking in consideration that the first HD series Radeons already had some form of tessellation functionality that requires more freedom from the vertex pushing hardware.

In order to get a better picture about how effective the presented optimization is, I would like to ask all the visitors of this post to try the two releases and send me feedback about it.

Conclusion

We’ve seen that how easy it was to take advantage of instanced arrays in an existing implementation of the ICR technique and how does it perform on the latest generation of GPUs compared to the previous version. While this small addition provides some benefits, it also comes at a cost and we have to talk about that as well.

Advantages:

• Eliminates the need for texture fetching in the vertex shader thus improving performance.
• Does not compromise the goal and the implementation architecture of the original method.
• Frees up one texture unit that was previously reserved for the texture buffer containing the instance data.

Disadvantages:

• Requires OpenGL 3.3 or the GL_ARB_instanced_arrays extension in addition to the OpenGL 3.2 features.
• We have to possibly sacrifice multiple vertex input attributes to feed the instance data to the shaders.

Most of the mentioned benefits and drawbacks are self-explanatory, however I would like to say a few words about the last mentioned one…

For the purpose of showcase I used a simple translation factor as instance data that means a single vector of floats. In real life situation one may need more complex transformation data that can only be stored in the matrix. While in the demo the feeding of instance data consumed only one vertex attribute slot, in case of a full transformation matrix it would require four of them (not to mention other possible instance attributes). As the maximum number of input attributes is severely limited, usually to 16, the application of the optimization is restricted to situations when all the vertex and instance attributes fit into this limit.

In case of the original implementation, where a texture buffer was used as input, this did not cause any problem as the vertex shader is free to fetch any number of texels from that (still, performance can be a concern in this case). In order to help situations when input attribute slots are at a premium, in real life scenarios it is recommended to use quaternions instead of transformation matrices as they consume two times less attribute resources. Actually this can be a general recommendation as using quaternions decreases the bandwidth requirements of the instance data fetch thus increasing performance even in situations when there are enough input attribute slots available.

In order to ease the performance comparison for you, you can find download links for both versions of the Nature demo.

Old version binary release

Platform: Windows
Dependency: OpenGL 3.2 capable graphics driver
Download link: nature12_win32.zip (3.58MB)
Comments: This version does NOT include the optimization presented in this article.

Old version source code

Language: C++
Platform: cross-platform
Dependency: GLEW, SFML, GLM
Download link: nature12_src.zip (12.6KB)
Comments: This version does NOT include the optimization presented in this article.

New version binary release

Platform: Windows
Dependency: OpenGL 3.3 capable graphics driver
Download link: nature20_win32.zip (3.58MB)
Comments: This version includes the optimization presented in this article.

New version source code

Language: C++
Platform: cross-platform
Dependency: GLEW, SFML, GLM
Download link: nature20_src.zip (12.8KB)
Comments: This version includes the optimization presented in this article.

The importance of static code analysis is already a well known thing in the domain of software development. There are plenty of useful and less useful tools for the purpose, especially in the case of C++. However, even if in general the quality of these softwares is adequate they usually suffer from the inability for extending or customizing behavior. Also, a usual problem arises from the fact that the C++ language syntax is overwhelmingly complex and it makes the code parser of any static analysis tool a nightmare. In this article I would like to present a tool called CppDepend that gracefully solves the aforementioned problems primarily focusing on providing an interface that enables 100% adaptability and extensibility for creating customized metrics that are relevant or applicable in a particular domain.

Why static code analysis?

Analysis of computer software, in particular verification and validation, is a very important factor in professional software development. The process behind itself can come in different forms. Generally all kind of verification and validation techniques can be categorized in two major groups: static analysis and dynamic analysis. The key difference between the two is while dynamic analysis verifies the execution of the code, static analysis strictly works on the code base itself.

Well, there are thousands of reasons why using a static code analysis tool makes any benefits to a particular software development process. If you ask various people they will all have their own reasons and rationale behind that. Just to mention my favorites here is a brief excerpt from the long list:

• Find coding errors before executing a single line of code. This is important as it does not require the project to be built or executed as in many cases these two additional phases can be quite expensive from both time and budget point of view.
• Identifies parts of the code that seem to be difficult to maintain or do not conform to various policies of a particular company or organization. This provides us the benefit to move towards a sustainable development by heavily reducing maintenance costs.
• Provides us miscellaneous metrics about our code that can have key importance in measuring the quality of the code base.

If you can’t measure it, you can’t improve it – Lord Kelvin

Many people still think that code metrics are overrated. Even if at first sight it seems to be true for micro-projects its importance becomes very obvious when one mets a large code bases specifically talking about situations when legacy code is inherited from earlier software developer generations. When the magnitude of the software goes out of the limits a programmer is capable to keep in mind (this means the 99% of software products) code metrics provide great value to identify “hot spots” in the code base, no matter what actual situation we are talking about.

Also, making decision about whether the evolution of the software goes in the right direction is very difficult if not impossible without ways of measuring the quality of the code. The most naive solution for this problem is to measure the amount of bug reports reported over time, however, code metrics provide a much more sophisticated way of measuring the quality by different aspects and on different levels.

During my career, as a software developer, I also faced many situations when the inspection of the legacy code was necessary in order to introduce new functionalities. Unfortunately, in most of the cases, due to the lack of an adequate static code analyst, this required developers to read and manually inspect the code in order to solve the particular problem. I can tell you that it’s not a joyful duty. Just to mention some of the most critical situations that current developers meet regarding to the topic:

Removing dependencies on deprecated features. This is a thing that each software development faces from time to time. This time interval is usually relatively low, as we talk about few years which can be called quite often compared to other industries. Just think about situations when one migrates to a new version of a third party library that the whole software depends on. As a recent event, we can talk about the release of version 3 of the OpenGL specification. CAD software developer companies faced a huge challenge by being forced to adopt the new features as the old ones became deprecated and obsolete. Actually they were quite lucky that vendors denied to drop features from their implementations. Using a code analyst one can easily identify the modules that needs to be modified in order to adopt to the latest changes.

Introducing multiprocessing. This is also a very imminent problem that every software development company will face sooner or later. Code bases inherited from the previous decades were not prepared to handle concurrent execution of the code thus making big headaches to software architects to redesign the code in order to be SMP compliant, especially when dealing with multi-core processors. I’ve also faced this situation during my career and it was a painful lesson that code analyzing possibilities have a great importance. Before inspecting carefully the whole code base it is very difficult to identify the possible problems that may arise by the introduction of multiprocessing. Automatic inspection of the code can be a very handy tool for minimizing the required efforts.

What makes up a good static code analysis tool?

There are many different aspects that affect how good a particular static code analysis tool is. In many situations having competing alternatives for this purpose is at a premium. Fortunately, this is not the case regarding to C++ as being a well supported programming language from the community. However, in order to choose a suitable alternative we have to collect our requirements:

• Correctness – It must correctly analyze the code. This is a very basic requirement against any software development tool. While this seems to be a completely obvious requirement and one expects that tools behave as expected from this point of view, most of such tools for C++ do not conform to this principle. Those who know the C++ language standard know well that writing a good parser for it is almost impossible.
• Usefulness – There is no sense in using a static code analyst if we don’t get any benefits from it. The reports generated by the analyst should provide useful information that are directly applicable in a particular use case. One typical example that I also faced quite often is that when one analyses legacy code and gets a report about thousands of problematic code parts. These reports are almost impossible to be handled and it makes headaches to the developers even to answer the very simple question: where to start?
• Customizability – This requirement directly relates to the previous one. By examining the previous example if there would be some customization possibility to get reports only about the 10 most problematic module it would be much easier to handle it. However, this requirement goes far beyond this. As an example, beside the build-in metrics of the analysis tool, it should provide means to add or modify metrics in order to have more relevant measures about the code fitting a particular domain or use case.

We’ve just mentioned three requirements explicitly and we already heavily reduced the number of alternatives…

CppDepend as a flawless alternative

Recently I’ve got a request to review a C++ static code analyst tool called CppDepend. After having a brief eye shot on the product I realized that it deserves a thorough inspection as it features a revolutionary technology called CQL that I will talk about a bit later in the article.

CppDepend was developed in partnership with NDepend, it was released six months ago having a two years development history by a very small team of experts. Actually it is accompanied with it’s brothers NDepend and XDepend that accomplish the same job for .NET and Java projects respectively.

We are talking about a Windows application that has tight integration with Visual Studio projects but also provides ways to be applicable in case of projects built with other development tool-set. Beside it is a command-line static code analysis tool for the C++ language, it provides a powerful GUI tool for visual inspection of different aspects of the code base thus enabling increased productivity and ease of use.

Lets have our first sight on the tool by using the visual interface to analyse a sample code base that will be in our case the source code of SFML.

Setting up the basic configuration for an analysis project is very straightforward. Beside that, the code analysis itself is surprisingly fast. While testing, the longest time it took was in case when I parsed the code of the Bullet Physics Library but even that didn’t required a minute on my system.

CppDepend graphical user interface

The visual controls themselves sometimes lack of good responsiveness due to the complex structures and relationships presented by them but we soon forgive CppDepend this minor issue when we take a closer look at the navigation possibilities offered by the tool.

At first sight, the user interface seems to be a bit overcomplicated but we soon realize that each and every element of it is made by purpose in order to provide as much freedom in navigation as possible. Just to mention the most interesting ones here’s the explanation of the purpose of the graphical figures at the top right part of the GUI:

• At top left we see a graphical representation of the currently selected code metric. It shows the magnitude of the result of the metric according to the selected level of granularity. We can easily visualize here as an example how the size of different classes of our project compare to each other.
• At middle left is the dependency matrix of our solution. We can easily find “hot spots” in our code regarding to coupling, by default, on project level. The granularity of the table can be easily changed in a non-proportional way from project level down to method level. I used the word “non-proportional” by intension as we can examine dependency even between a method and a foreign project thus providing additional flexibility over how fine grained we would like to have our numbers.
• My favorite is in the middle, called dependency graph. It can present the dependencies between different software elements from project level down to method level, as usual, by means of a graph that is very convenient for human inspection.

The whole user interface is designed in a way that each time we point on a particular element it shows convenient information about that particular element and its environment, no matter if we talk about the metrics view, the dependency graph or matrix.

Beside the tools for navigation and easy visualization, the GUI provides a collection of built-in reports about different aspects of the code. One of the first thing everybody would try out from these is the query called “Quick summary of methods to refactor”. This is exactly the answer what the developer would like to have for the question “where to start?” that I mentioned earlier.

To emphasize even more the fact that how convenient is the user interface, when one selects a particular query it will immediately show the results by means of a list of classes, methods or whatever, but beside this, the code elements in question are immediately highlighted in the relevant graphical views as well.

Maybe I already convinced most of you that CppDepend is a tool that deserves attention as being a valuable tool in good hands but I haven’t even talked about the most interesting feature that really makes it a uniquely powerful software.

The power of extensibility

I have often brought to relief the importance of extensibility and customizability of a static code analyst. This, in fact, is not just my craze but it is an important factor in the decision of most software developers out there. Being able to get some common metrics about the code is one thing, having the possibility to define own metrics and analysis criterias is another…

The power of CppDepend is behind a revolutionary technology that provides us an interface to retrieve information about the code that is relevant for us as easy as querying a relational database. The apparatus in our hand to achieve this is the Code Query Language (CQL). CppDepend actually builds some internal database structure from the source code and provides us an SQL-like language to make queries that fetches reports from this internal database. Those who are already familiar with SQL will adore this feature. Just to illustrate how easy it is to use CQL in order to build custom queries, let’s query the classes that have more than 20 methods is as simple as the following line of CQL code:

SELECT TYPES WHERE NbMethods > 20

Simple, isn’t it? For further details, please refer to the specification of the Code Query Language: http://www.cppdepend.com/CQL.htm

This means that the software developers have complete freedom over how they define the metrics that indicate whether the code quality reaches the levels required by company policies or individual needs. It is also useful to solve the problems arising from the sample situations I’ve mentioned earlier, namely the problem with dependency on deprecated features and the introduction of multiprocessing, by easily and clearly identifying the modules that need to be changed even in situations when the code base is extremely huge and traditional ways for identifying affected modules are not applicable or simply not feasible.

Endurance test

Well, I’ve already talked enough about the abilities of CppDepend regarding to usefulness and customizability, however, I’ve barely touched the topic of correctness. As I’ve already mentioned, parsing C++ code correctly is not as easy as it may look like. For this purpose I’ve prepared a bunch of template heavy libraries like GLM and GoogleMock to check how well CppDepend handles code bases when it comes to awkward features of the C++ language.

Even though generally static analyst tools does not provide too much useful information about such project, due to their special nature, it still looked convenient to try to make parsed these libraries by CppDepend in order to have a picture about how it would handle huge projects that also take advantage of the templating mechanisms of C++. I have to say that the results are very promising as it had problems only with GoogleMock but the developers were already informed about the problem I’ve encountered.

The dark side of the story

While CppDepend is an excellent tool for software developers working under Windows, especially if they use Visual Studio, I would like to see a cross-platform version of CppDepend in the future, at least for Linux and MacOSX.

Also, CppDepend does not come for free but at a reasonable price. Even though most probably individuals and hobbyists would not consider buying it, for enterprises, even for small ones, the price of the tool will most probably pay back soon by heavily decreasing short- and long-run maintenance costs of the development.

Conclusion

A clever static code analyst tool is nowadays a must for every software development company that deals with code whose size have already ran over a certain threshold but it is also good to use one from the very beginning of a new project. Selecting a particular tool for this purpose is the choice of the enterprise, still, the requirements against such a software are usually the same.

CppDepend proved to me of being a valuable software in the tool-chain of every C++ programmer using Windows as primary development platform. If you are still not convinced then check out the full feature list on the official site.

Even if you are not interested in using CppDepend or in static analysis tools at all, you should still take a look at CQL and the great idea behind it as it is a perfect example how a solution for a well discussed problem can ascend to new levels by adopting good practices from other domains, in this case from relational databases and related technologies.

OpenGL 3.2 - Nature

Since the appearance of Shader Model 4.0 people wonder how to take advantage of the newly introduced programmable pipeline stage. The most important feature enabled by geometry shaders is that one can change the amount of emitted primitives inside the pipeline. The first thing that a naive developer would try to do with it is geometry tesselation. However, the new shader performs very bad when used for tesselation in a real life scenario even though there are demos show casting this possibility. If we take a closer look at the new feature we observe that the most revolutionary in it is not that it can raise the number of emitted primitives but that it can discard them. This article would like to present a rendering technique that takes advantage of this aspect of geometry shaders to enable the GPU accelerated culling of higher order primitives.

Geometry shaders can be used for many different advanced rendering techniques that were impossible before the introduction of this flexible programmable shader stage. In this article I would like to present one use case that for me seemed to be one of the most practical application of primitive manipulation possibilities introduced by geometry shaders. As I haven’t seen any whitepaper talking specifically about this particular technique, even if some of them inherently used it, I would dare name the technique myself as Instance Cloud Reduction. I will also present a demo program that shows how to take advantage of the technique in a heavy workload situation.

The idea itself was inspired by AMD’s  tech demo for the Radeon 4800 series cards called March of the Froblins. An almost identical technique presented in this article is used in the mentioned demo for the culling of large amount of animated creatures against the view frustum. Also a somewhat similar technique is used in NVIDIA’s Skinned Instancing demo for determining LOD instance sets. Unfortunately, both demos are for DirectX only and, as far as I can tell, there is no OpenGL demo showing any of the aforementioned rendering techniques.

Motivation

Nowadays, as the computational capabilities of GPUs is growing in a much faster pace than that of CPUs, graphics developers meet more and more optimization problems related to CPU bound applications. More and more focus is on minimizing the number of driver invocations, actually that’s what motivated the restructuring of the two most commonly used graphics APIs. As a result we have now DirectX 10+ and OpenGL 3+. However, even if the introduction of geometry instancing, texture arrays and local memory buffer storage for the most important inputs of the rendering, there is still need for wise decisions from graphics programmers to take full advantage of the horsepower coming with the latest GPUs.

Earlier graphics applications strongly relied on CPU based culling techniques, whether it be the usage of the quite outdated BSPs or the more generic and still heavily applied hierarchical culling techniques. We’ve already reached the point that sometimes even the most efficient CPU based culling techniques seem to be too expensive and usually introduce the small batch problem. Instanced rendering is not an exception.

The applicability of geometry instancing is strongly limited by several factors. One of the most important ones is the culling of instanced geometries. One may choose to cull these objects in the same fashion as others, using the CPU, but that usually breaks the batch and maybe we loose the benefits of geometry instancing. It is more and more imminent to have a GPU based alternative. Without CPU based culling, by sending the whole bunch of instances down the graphics pipeline may choke our vertex processor in case we have high poly geometries and quite large amount of instances of it.

The rendering technique presented in this article will try to achieve this goal. We will use a multi-pass technique that in the first pass culls the object instances against the view frustum using the GPU and in the second pass renders only those instances that are likely to be visible in the final scene. This way we can severely reduce the amount of vertex data sent through the graphics pipeline.

Implementation

For some people it might seem that the promise for such a technique is simply too naive and is most probably relying on very exotic OpenGL features, heavy misuse of some basic features or need of data conversions during the frame rendering. Wondrously, this is not the case as we have all we need in OpenGL 3.2 to implement the object culling method sketched above. All we need are the followings:

• instanced rendering (core since OpenGL 3.1)
• geometry shaders (core since OpenGL 3.2)
• transform feedback (core since OpenGL 3.0)
• uniform or texture buffers (core since OpenGL 3.1)

The method itself is a multi-pass rendering technique, however, unlike other multi-pass rendering techniques it does not produce any fragments in the first pass, instead the first pass does the view frustum culling and processes data entirely only inside buffer objects.

Culling pass

In the first pass we will feed the graphics pipeline with information about the instances that are needed to perform the view frustum culling. For this we need two inputs for the executed shaders in order to be able to perform the required calculations:

1. Instance transformation data (whether it be a simple transformation matrix or quaternions or whatever) -- This preferably comes from one or more buffer objects that are bound as vertex buffers to the context.
2. Object extents information -- Beside the instance positions we have to know the extents of an instance in order to perform correct culling. This can be either a single float representing the object radius if we choose to use bounding spheres for the culling or a three-dimensional extent vector if we would like to use bounding boxes.

Using these as input we can feed in the instance transformation data as attributes of point primitives to our culling shader. The culling shader is composed of a vertex and a geometry shader. In a typical setup the role of each is the following: the vertex shader determines whether the actual object instance’s bounding volume is inside the view frustum and sends a flag about the culling to the geometry shader, that will emit the instance data to the destination buffer if the flag says that the instance is likely to be visible or does not emit anything if it is determined that the object instance is out of view.

Next, transform feedback is used to capture the primitives emitted by the geometry shader into another buffer object that will be used in the actual rendering pass to source instance transformation data. Beside this, we also need to have an asynchronous query to determine the number of primitives generated to know how many instances of the object do we actually need to render. The following figure shows the workflow of the first pass:

Instance Cloud Reduction - Pass 1: Culling

The actual geometry shader implementation needed to perform the actual culling based on the view frustum check performed by the vertex shader should look like the following chunk:

#version 150 core

layout(points) in;
layout(points, max_vertices = 1) out;

in vec4 OrigPosition[1];
flat in int objectVisible[1];

out vec4 CulledPosition;

void main() {

	/* only emit primitive if the object is visible */
	if ( objectVisible[0] == 1 )
	{
		CulledPosition = OrigPosition[0];
		EmitVertex();
		EndPrimitive();
	}
}

In this example we used only simply a four-component position vector for the instance transformation data but the technique works well for transformation matrices and quaternions as well.

One more thing is that beside that we set up transform feedback in a way that we feed our buffer object dedicated for the culled instance data and we also started an asynchronous query to be able to determine the number of primitives written into the buffer object, it is also useful to turn of rasterization as we wouldn’t like to produce any fragments as a result of the first pass.

Rendering pass

In the second pass there is nothing special to do. Simply use whatever rendering setup you would like to use. The only things that need to be changed in this step compared to your already existing rendering path is that the instance data for the rendering must be sourced from the generated culled instance data buffer and, as a result, the number of instances passed for the instanced drawing functions shall be changed in order to render only the visible instances. This number can be read from the asynchronous query’s result that we started in the first pass.

The instance data in the rendering pass can be, of course, sourced from either a uniform or a texture buffer object. This depends on the actual use case and is more clearly explained in the article Uniform Buffers VS Texture Buffers.

Important note is that when one has to deal with several instanced geometries it is recommended to do the culling phase prior to rendering any instanced primitives because of the following reasons:

• The result of the first instance cloud’s culling is more likely to be finished on the GPU so no sync issues arise from reading the asynchronous query result to determine the number of visible instances.
• Probably less state changes are needed as very different setup is required by the two passes.
• Results in tidier renderer design as culling is clearly separated from actual rendering.

Putting everything together, the application of the presented technique would result in the following workflow on the GPU:

Instance Cloud Reduction - Combined view of Pass 1 + Pass 2

Conclusion

We’ve seen that the presented advanced rendering technique is able to help in situations when we have to deal with large number of instanced geometries and how to take advantage of the latest features of graphics cards and OpenGL to perform view frustum culling calculations on the GPU. This prevents us from having to deal with complicated and expensive CPU based object culling methods that break the drawing batches, especially when dealing with dynamic objects. For ease the decision whether to incorporate this technique in your rendering engine I would like to present the advantages and disadvantages of it.

Advantages:

• Heavily reduces the amount of processed data in a naive implementation.
• No need for any space partitioning methods in the host application to handle the culling of dynamic objects.
• Can handle huge amount of instanced objects due to the enormous horsepower of today’s GPUs.
• Scales well with increased number of instances as the per-instance calculation is relatively low.
• Relies strictly on OpenGL 3.2 core features.
• No need for OpenCL capable hardware.

Disadvantages:

• Needs an extra rendering pass to perform the culling.
• Requires the usage of asynchronous queries to determine the number of visible instances.

I hope you agree with me and think about this technique as one more step towards fully GPU based scene management. If you have any remarks or improvement ideas regarding to the rendering technique itself feel free to tell me.

The Demo

As I promised, the technique presented above comes with a live demo that actually took most of my time dedicated to writing this blog in the last two weeks. The demo itself is more like a technical show cast rather than a presentation of a real-life use case scenario.

First of all, I used high polygon count models for the rendering to emphasize the amount of time the culling phase spares from the very valuable time of our GPU. In a real world application one would never do something like this. As a result, the demo is more like a benchmark than an interactive application. However, maybe on high-end graphics cards it can perform pretty well.

The demo scene consists of two object types: trees and grass blocks. The tree model is further divided into two parts as they need different textures: the tree trunk and the tree foliage. Obviously, this additional burden can be prevented by using texture arrays to avoid the need of separate draw calls to render the trunk and the foliage.

The tree trunk consists of 33138 triangles, the tree foliage has 16069 triangles and the faking-free grass block consists of 8961 triangles which I had to model myself as didn’t found any suitable model. Actually this modeling step consumed quite a reasonable amount of my time spent with the demo as I’m not an expert in this domain.As you can see, these models are not the ones that one might use in an interactive real-time application like games. However, they seemed to be very suitable for the purpose of the demonstration.

What really kicks off the boundaries of GPUs is that the demo renders 10,000 trees and 250,000 grass blocks using instancing. This ends up in more than 2.7 billion triangles in the scene. This is far more that a GPU can handle without the aid of some scene management and culling. However, we will use no scene management at all and the only culling method that we will use is the one presented in this article.

The actual results are quite promising. The view frustum culling step usually spares more than 99.9% of the GPU horsepower as the amount of actually rendered triangles after the culling step is far below 2 million triangles. This is still quite much but as we use high polygon count models and we don’t use any LOD techniques this seems reasonable.

Even if the demo scene statistics doesn’t seem like a typical use case scenario, the ease of the implementation and the compelling visual results made me pleased anyway:

www.youtube.com/watch?v=srbOFTLTe8k

On my Radeon HD2600XT I have achieved 6-7 frames per second which is acceptable taking in consideration the huge amount of geometry data still passed to the graphics card. On more recent cards I suppose it should run with good frame rates, however, due to the lack of hardware to test on, these are my only results. If anybody manages to take a better screen capture than mine above then please let me know.

Implementation details

Just to tell a few words about what techniques and tricks I’ve used during the creation of the demo here is a listing of the most important ones:

• Three models are used as mentioned previously with high instance counts with over 2.7 billion of total triangles in the scene as mentioned already.
• Three 512x512 RGBA textures are used for the models that are partially handmade, and again, I’m not a texture artist so sorry if they don’t look flawless.
• The wavefront model and TGA image loader that accompany the demo are very roughly implemented only for the demo so I would strongly encourage you not to use it to any purpose as it handles only a subset of the possibilities of the file formats.
• The vertex data from the wavefront model files is transferred in a very naive way so vertex reuse isn’t taken into account.
• The instance data consists of simple four-component vectors representing the world-space position of the instance. This seemed to be the most simple for the demonstration purposes.
• In the second pass, the instance data is sourced from a texture buffer but not really because the visible instance count exceeded the amount that would fit in a uniform buffer. I used texture buffers because for this simple demonstration they seemed to be a little bit more easy to be integrated.
• The morphing effect that simulated wind blow is done using hard-coded geometry deformation in the vertex shader. It is not physically correct but visually compelling.
• The lighting is a simple directional light using Phong’s shading and reflection model.
• Simple fog is simulated with some awkward formula that I’ve chosen after a few test runs.
• Alpha testing is achieved by using the discard operation in the fragment shader.

Driver issues

During the development of the demonstration program I’ve met several driver related problems as I’ve never used so heavily the latest OpenGL features previously. I’ve worked with Catalyst 9.12 and 10.1 but both seemed to lack of a proper GLSL compiler. Here are some of the issues I’ve met:

• When I’ve forgot to declare the varyings in the geometry shader as arrays like the standard requires then still the driver hasn’t complained about any syntax error but when tried to execute the code the program crashed.
• Except the texture sampler uniform, all other uniforms failed to work when used in the fragment shader only so I’ve put them all in the vertex shader.
• For loops seemed not to work when used inside the geometry shader, that’s why the culling itself is done in the vertex shader in the demo.

All these problems resulted in nasty tricks to make things working and ended up in awful shader code. Sorry for that. At least now it works on my configuration but pretty unsure whether it will work on other graphics card and driver combos. Please report me any success or failure when trying out the demo. Anyway, be sure to have the latest graphics drivers installed as, at least in case of AMD, OpenGL 3.2 drivers came out only at the fall of 2009.

Edit:

Thanks to the information got from Pierre Boudier from AMD I’ve updated both the source and binary releases to support the latest drivers properly. The problem was that I didn’t use attribute location binding as specified in the standard.

Also have to mention that with my new Radeon HD5770 I managed to achieve over 90 frames per second that actually show that this technique can be in fact used for games and other interactive applications.

One more thing in the end. As you know this version of the Nature demo uses a texture buffer to source instance positions. I plan to create another version that will take advantage of the instanced arrays introduced in core with OpenGL 3.4. I expect quite a reasonable speedup as that would eliminate the need for texture fetches in the vertex array by rather dedicating a vertex fetcher for the purpose thus increasing the overall performance of the technique.

Binary release

Platform: Windows
Dependency: OpenGL 3.2 capable graphics driver
Download link: nature12_win32.zip (3.58MB)
Comments: Includes the update that makes it work even with the latest drivers.

Full source code

Language: C++
Platform: cross-platform
Dependency: GLEW, SFML, GLM
Download link: nature12_src.zip (12.6KB)
Comments: Sorry for the many dependencies, however, I would recommend the mentioned libraries for everybody who is doing OpenGL development.