Archive for the ‘3D-Graphics’ Category

A GPU Bilateral Filter Implementation

This post reports on a bilateral filter implementation that improves processing time from 32ms to 0.25ms.


The Kinect (for Windows) depth data are subject to some uncertainty that comes with its resolution. Depth estimates are defined in millimeters, and typically, subsequent depth measurements by the Kinect vary by a fixed amount.

Consider the graphs below. The x-axis counts the number of measurements, the y-axis represents distance measurements of a single point. The top graph shows connected dots, the lower graph shows

just the dots.

De graphs show two tendencies. One is that variance is one unit above, or one unit below the average practically all of the time, the second tendency is that the average changes a bit before it stabilizes. Here we see it change from about 3.76m via 3.8m to about 3.84m.

If the Kinect depth data is projected onto an image this variation translates into a nervous jitter. Since I do not particularly care for a nervous jitter, I would like to stabilize the depth data a bit.

Stabilizing Kinect Depth Data – Temporal Approach

The Kinect for Windows SDK (1.6) contains a whitepaper on skeletal joint smoothing. The paper deals with the reduction of noise in the Kinect skeletal tracking system. This tracking system employs the same depth data, and therefore suffers from the same problem.

The proposed solution is to filter the data over time. The depth measurement z(x,y)(t) of a location (x, y) at time t can be averaged over a number of measurements in the past at the same location: z(x,y)(t-i) where i is in [1, n]. The suggestion is to take n not too large, say 5.

Averaging can also be over measurements in the future. This implies that one or two frames are included in averaging before an image based on the depth image is rendered, hence there is a latency in rendering equal to the number of ‘future’ frames included in averaging. The advantage of considering the ‘future’ is that if the measured scene changes (or a player changes position – in skeletal tracking), another type of averaging can be applied, one that is better suited for changes and e.g. puts a heavier weight on recent measurements.

I’ve done an experiment with temporal filtering, but it was not satisfactory. The fast and nervous jitter just turns into a slower one that is even more disturbing because short periods of stability make changes seem more abrupt.

Stabilizing Kinect Depth Data – Spatial Approach

Another approach is not to average over measurements at the same location through time, but to average within one frame, over several proximate measurements. A standard solution for this kind of filtering is the Bilateral filter. The Bilateral Filter is generally attributed to Carlo Tomasi and Roberto Manduchi. But see this site where it is explained that there were several independent discoveries.

The idea behind the Bilateral Filter is that the weight of a measurement in the average is a Gaussian function of both the distance and the similarity (in color, intensity, or as in our case: depth value). The similarity term prevents edges to be ‘averaged out’.

The Bilateral Filter works well, the only drawback it has is its computational complexity: O(N^2) where N is the (large!) number of pixels in the image. So, several people have been working on fast algorithms to alleviate the computational burden. To me it seems that Ben Weiss provided a good solution, but it is not generally available. The solution by Frédo Durand and Julie Dorsey (2002), and the elaboration of this work by Sylvain Paris and Frédo Durand (2006), all from MIT, seems to be the leading solution, and is general available – both the theory and example software. Their method has a project site that is here.

In a nutshell, the method by Sylvain Paris and Frédo Durand reduces processing time by first down sampling the image, then applying a convolution to compute the averages, and finally scaling up the image again while clamping over out-of-bounds values. So in essence, it operates on a (cleverly) reduced version of the image.

I’ve downloaded and compiled the software – the really fast version with the truncated kernel – and it requires about 0.032s to process a ppm image of 640×480 pixels (grayscale values), where the spatial neighborhood is set to 16 (pixels) and the ‘similarity’ neighborhood is set to 0.1, so grayscale colors that differ more than 0.1 after transformation to normalized double representation, are not considered in the average. See the image below for a screen shot.

The processing time is, of course, computer dependent, but my pc is not really slow. Although 32ms is a fine performance, it is too slow for real-time image processing. The Kinect produces a frame 30 times per second, i.e. every 33ms, and we do not want to create a latency of about one frame just because of the Bilateral Filter.

GPU implementation: C++ AMP

In order to improve on the processing time of this fast algorithm I’ve written a C++ AMP program inspired by the CPU implementation, this program runs on the GPU, instead of on the CPU. For information on C++ AMP, see here and here. What I think is great about AMP is that it provides a completely general access to General Purpose GPU computing. Having said that, I must also warn the reader that I do not master it to the degree that I could guarantee that my implementation of the Bilateral Filter in C++ AMP is representative of what could be achieved with C++ AMP.

The result of my efforts is that the ppm image above can now be processed in little over 1 ms. Consider

the picture below, made with my ATI Radeon HD 5700 Graphics card.

What you see here is a variety of timings of the computational phases. The top cycle takes 1.1ms, the middle one takes 1.19, and the bottom cycle takes 1.07ms. So, what is in the cycle?

1. The image is loaded into the GPU, and data structures are initialized. If you want to know more on ‘warming up’ the data and the code, see here. Since it takes 0.5 to 0.6 ms it is obviously the bottle neck.

2. Down sampling the image to a smaller version takes around 0.1 ms.

3. Computing the convolution takes 0.35 ms. This is the real work.

4. Up scaling and clamping takes again 0.1 ms.

A processing time of about 1 ms is satisfactory as a real-time processing time. Moreover, since we may assume the data is already in GPU memory (we need it there to render it to the screen), GPU upload time is not an attribute of an application of the Bilateral Filter in this context. So we may think of the processing time as being about 0.55 ms. which is absolutely fabulous.

New Graphics Card

At about this time, I bought a new graphics card, an Asus NVidia GTX690 (which for the purposes of this application yields the same results as a GTX 680, I know). This card was installed in my pc. Ok, I didn’t buy a new motherboard, so data is still being uploaded through PCI-e 2.0 and not through PCI-e 3.0 16x (but in time…). So, will this make a difference? Yes, it does. Look at the screen shot below.

I rearranged the timings a bit, to gain better oversight. We see that:

1. Data uploading and the warming up process now takes about 0.45 ms.

2. Filtering now takes about 0.25 ms.

From 32ms to 0.25ms. Most satisfying!

Vector –Matrix Inner Product with Computer Shader and C++ AMP

Large vector-matrix inner products by the GPU are 250 times faster than straight forward CPU implementations on my PC. Using C++ AMP or a Compute Shader the GPU realized a performance of over 30 gFLOPS. That is a huge increase, but my GPU has a “computational power” (whatever that may be) of 1 teraFLOP, and 30 gFLOPS is still a long way from 1000 gFLOPS.

This article presents a general architectural view of the GPU and some details of a particular exemplar: the Ati Radeon HD5750. Then code examples follow that show various approaches to large vector-matrix products. Of course the algorithms at the end of the article are the fastest. It is also the simplest.

Unified View of the GPU Architecture

Programming the GPU is based on an architectural view of the GPU. The purpose of this architectural view is to provide a unified perspective on GPUs from various vendors, hence with different hardware setup. It is this unified architecture that’s being programmed against using DirectX11. A good source of information on Direct Compute and Compute Shaders is the Microsoft Direct Compute BLog. The architecture described below is based on information from Chas Boyd’s talk at PDC09, as published on Channel9. Of course, this blog post only presents some fragments of the information found there.

A GPU is considered to be build from a number of SIMD cores. SIMD means: Single Instruction Multiple Data. By the way, the pictures below are hyperlinks to their source.

The idea is that a single instruction is executed on a lot of data, in parallel. The SIMD processing unit is particularly fit for “data parallel” algorithms. A GPU may consist of 32 SIMD cores (yes, the image shows 40 cores) that access memory with 32 floats at a time (128 bit bus width). Typically the processor runs at 1Ghz, and has a (theoretical) computational power of about 1 TeraFLOP.

A SIMD core uses several kinds of memory:

  • 16 Kbyte of (32-bit) registers. Used for local variables
  • 8 Kbyte SIMD shared memory, L1 cache.
  • L2 cache

The GPU as a whole has typically 1Gb of general RAM. Memory access bandwidth is typically of order 100GBit/s.

Programming Model

A GPU is programmed using a Compute Shader or C++ AMP. Developers can write compute shaders in HLSL (Looks like C) to be executed on the GPU. AMD is a C++ library. The GPU can run up to 1024 threads per SIMD. A thread is a line of execution through code. The SIMD shared memory is shared among the threads of a SIMD. It is programmable in the sense that you can declare variables (arrays) as “groupshared” and they will be stored in the Local Data Share. Note however, that over-allocation will spill the variables to general RAM, thus reducing performance. Local variables in shader code will be stored in registers.


The GPU architecture suggests programming tactics that will optimize performance.

  1. Do your program logic on the CPU, send the data to the GPU for operations that apply to (about) all of the data and contain a minimal number of alternative processing paths.
  2. Load as much data as possible into the GPU general RAM, so as to prevent the GPU waiting for data from CPU memory.
  3. Declare registers to store isolated local variables
  4. Cache data that you reuse in “groupshared” Memory. Don’t cache data you don’t reuse. Keep in mind that you can share cached data among the threads of a single group only.
  5. Use as much threads as possible. This requires you use only small amounts of cache memory per thread.
  6. Utilize the GPU as efficiently as possible by offering much more threads to it than it can process in a small amount of time.
  7. Plan the use of threads and memory ahead, then experiment to optimize.

Loading data from CPU memory into GPU memory passes the PCIe bridge which has a bandwidth, typically of order 1GBit/s; that is, it is a bottleneck.

So, you really like to load as much data onto GPU memory before executing your code.

The trick in planning your parallelism is to chop up (schedule, that is J ) the work in SIMD size chunks. You can declare groups of threads; the size of the groups and the number of groups. A group is typically executed by a single SIMD. To optimize performance, use Group Shared Memory, and set up the memory consumption of your thread group so it will fit into the available Group Shared Memory. That is: restrict the number of threads per group, and make sure you have a sufficient number of groups. Thread groups are three dimensional. My hypothesis at this time is that it is best to fit the dimensionality of the thread groups to match the structure of the end result. More about this below. Synchronization of the threads within a thread group flushes the GroupShared Memory of the SIMD.

A register typically has a lifetime that is bound to a thread. Individual threads are member of several groups – depending on how you program stuff. So, intermediate results aggregated by thread groups can be stored in registers.

Does My ATI Radeon HD5750 GPU Look Like This Architecture… A Bit?

The picture below (from here) is of the HD5770, which has 10 SIMD cores, one more than the HD5750.

What do we see here?

  • SIMD engines. We see 10 cores for the HD5770, but there are 9 in the HD5750. Each core consists of 16 red blocks (streaming cores) and 4 yellow blocks (texture units).
  • Registers (light red lines between the red blocks).
  • L1 Textures caches, 18Kbyte per SIMD.
  • Local Data Share, 32 Kbyte per SIMD.
  • L2 caches, 8 Kbyte each.

Not visible is the 1Gb general RAM.

The processing unit runs at 700Mhz, memory runs at 1,150Mhz. Over clocking is possible however. The computational power is 1,008 TeraFLOP. Memory bandwidth is 73.6 GBit/s.

So, my GPU is quite a lot less powerful than the reference model. At first, a bit disappointing but on the other hand: much software I write for this GPU cannot run on the PCs of most people I know – their PCs are too old.

Various Approaches to Vector-Matrix Multiplication

Below we will see a number of approaches to vector-matrix multiplication discussed. The will include measurements of time and capacity. So, how do we execute the code and what do we measure?

Times measured include a number of iterations that each multiply the vector by the matrix. Usually this is 100 iterations, but fast alternatives get 1000 iterations. The faster the alternative, the more we are interested in variance and overhead.


  • Do not include data upload and download times.
  • Concern an equal data load, 12,288 input elements if the alternative can handle it.
  • Correctness check; computation is also performed by CPU code, reference code.
  • Run a release build from Visual Studio, without debugging.
  • Allow AMP programs get a warming up run.

Vector-Matrix Product by CPU: Reference Measurement

In order to determine the performance gain, we measure the time it takes the CPU to perform the product. The algorithm, hence the code is straightforward:

In this particular case rows = cols = 12,288. The average over 100 runs is 2,452 ms, or 2.45 seconds. This amounts to a time performance of 0.12 gFLOPS (giga FLOPS: FLoating point Operations Per Second). We restrict floating point operations to addition and multiplication (yes, that includes subtraction and division). We calculate gFLOPS as:

2 / ms x Rows / 1000 x Cols / 1000, where ms is the average time in milliseconds.

The result of the test is correct.

Parallel Patterns Library

Although this blog post is about GPU performance, I took a quick look at PPL performance. We then see a performance gain of a factor 2, but the result is incorrect, that is, the above code leads to indeterminacy in a parallel_for loop. I left it at that, for now.

Matrix-Matrix Product

We can of course, view a vector as a matrix with a single column. The C++ AMP documentation has a running code example of a matrix multiplication. There is also an accompanying compute shader analog.


To the standard AMP example I’ve added some optimizing changes, and measured the performance. The AMP code look like this:

Here: amp is an alias for the Concurrency namespace. The tile size TS has been set to 32, which is the maximum; the product of the dimensional extents of a compute domain should not exceed 1024. The extent of the compute domain has been changed to depend on B, the matrix, instead of the output vector. The loop that sums element products has been unrolled in order to further improve performance.

As mentioned above, we start with a warming up. As is clear from the code we do not measure data transport to and from the GPU. Time measurements are over 100 iterations. The average run time obtained is 9,266.6 ms, hence 0.01 gFLOPS. The result after the test run was correct.

The data load is limited to 7*1024 = 7,168; that is 8*1024 is unstable.

Compute Shader

The above code was adapted to also run as a compute shader. The code looks like this:

The variables Group_SIZE_X and Group_SIZE_Y are passed into the shader at compile time, and are set to 32 each.

Time measurements are over 100 iterations. The average run time obtained is 11,468.3 ms, hence 0.01 gFLOPS. The result after the test run was correct. The data load is limited to 7*1024 = 7,168; that is 8*1024 is unstable.


The performance of the compute shader is slightly worse that the AMP variant. Analysis with the Visual Studio 11 Concurrency Visualizer shows that work by the GPU in case of the compute shader program is executes in small spurts, separated by small periods of idleness, whereas in the AMP program the work is executed by the GPU in one contiguous period of time.

Nevertheless, performance is bad, worse than the CPU alternative. Why? Take a look at the picture below:

For any value of[0] – which is based on the extent of the matrix- that is unequal to zero, vector A does not have a value. So, in fact, if N is the number of elements in the vector, we do O( N3)retrievals but only O(N2) computations. So, we need an algorithm that is based on the extent of a vector, say the output vector.

Vector-Matrix Product

Somehow, it proved easier to develop the vector-matrix product as a compute shader. This is in spite of the fact that unlike AMP, it is not possible (yet?) to trace a running compute shader in Visual Studio. The idea of the algorithm is that we tile the vector in one dimension, and the matrix in two, thus obtaining the effect that the vector tile can be reused in multiplications with the matrix tile.

Compute Shader

A new compute shader was developed. This compute shader caches vector and matrix data in Group Shared memory. The HLSL code looks like this:

This program can handle much larger amounts of data. Indeed, this program runs problem free for a vector of 12,288 elements and a total data size of 576 Mbyte. Using an input vector of 12,288 elements, with total data size of 576 Mbyte. The time performance is 10.3 ms per run, averaged over 1,000 runs, which amounts to 29.3 gFLOPS. The result of the final run was reported to be correct.


In analogy to the compute shader above I wrote (and borrowed 🙂 ) a C++ AMP program. The main method looks like this:

The matrix is a vector with size * size elements. He tile size was chosen to be 128, because that setting yields optimal performance. The program was run on an input vector of 12,288 elements again, with total data size of 576 Mbyte. The time performance is 10.1 ms per run, averaged over 1000 runs, which amounts to 30.0 gFLOPS. The result of the final run was reported to be correct.


We see here that the performance has much improved. When compared to the reference case, we can now do it (in milliseconds) 2,452 : 10.1 = 243 : 1, hence 243 times faster.


Then, I read an MSDN Magazine article on AMP tiling by Daniel Moth, and it reminded me that caching is useless if you do not reuse the data. Well, the above algorithm does not reuse the cached matrix data. So I adapted the Compute Shader program to retrieve matrix data from central GPU memory directly. The HLSL code looks like this:

Note the tileSize of 512(!). This program was run for a vector of 12,288 elements and a total data size of 576 Mbyte. The time performance is again 10.3 ms for a multiplication which amounts to 29,3 gFLOPS (averaged over 1000 runs). The result of the final run was reported to be correct. So, indeed, caching the matrix data does not add any performance improvement.


For completeness, the AMP version:

Time performance is optimal for a tile size of 128, in case the number of vector elements is 12,288. We obtain an average run time of 9.7 ms (averaged over 1,000 runs), and a corresponding 31.1 gFLOPS. The result of the final run was correct. This program is 2452 / 9.7 = 252.8 times as fast as the reference implementation.


Developing an algorithm for vector-matrix inner product has demonstrated comparable performance for Compute Shaders and AMP, but much better tooling support for AMP: we can step through AMP code while debugging, and the Concurrency Visualizer has an AMP line. This better tool support helped very well in analyzing performance of a first shot at the algorithm. The final algorithm proved over 250 times faster than a straight forward CPU program for the same functionality.

Detailed knowledge of the GPU architecture, or the hardware model, proved of limited value. When trying to run the program with either the maximum nr of threads per group, or the maximum amount of data per Group Shared Memory, I ran into parameter value limits, instabilities, performance loss, and incorrect results. I guess, you will have to leave the detailed optimization to the GPU driver and to the AMP compiler.

One question keeps bothering me though: Where is my TeraFLOP?

I mean, Direct Compute was introduced with the slogan “A teraFLOP for every one of us”, AMP is built on top of Direct Compute, and my GPU has a computational power of 1.08 TeraFLOP. Am I not ‘one of us’?

C++ AMP Performance and Compute Shader Performance

Edit (April 23rd 2012):

The AMP team has updated the N-Body Simulation code to turn it into a clean port that relates to the Compute Shader original in a comprehensible way. Now it has comparable performance to the original (optimized) version (both versions do >330 gFLOPS at >30 fps for 23,040 particles on my pc).

I’m impressed. For one, by the attitude of the AMP people that energetically reacted to issues which other people / teams might well have dismissed as unimportant. Then there is the point that you get maximum performance from a set of very powerfull processors with code that is very short compared to the direct compute code you had to write otherwise, and this code, by AMP design, is very elegant as well.

Of course, there is a risk in short and elegant code: subtle differences in code can make substantial differences in performance, hence developing AMP code is rather knowledge intensive. But I kind of like that.

Edit (April 16th 2012):

The results below were brought to the C++ AMP forum for discussion. Daniel Moth advised to update the driver of the graphics card. This update made a tremendous difference for two of the three programs mentioned below for which now C++ AMP performance is equal to or better than Compute Shader performance.

The discussion on the N-Body Simulation program, which is heavily optimized in the Compute Shader version is still open, mainly because the required information is not available yet. I expect that also in this case C++ AMP will prove to be equipotent to Compute Shader programs.

Now, what have we learned from this exercise? For one, a lot about Compute Shader optimization and the mechanisms of GPU computing performance. This is an interesting and instructive subject. I also have learned that C++ AMP performance is comparable to Compute Shader performance. However, I do not (yet) understand if and how this will always and necessarily be the case, and that still itches a bit.

Results as they are standing now:









Average time (ms, 10 it.)








Max. Data Load (Kb)





Vector Addition



Average time (ms, 10 it.)








Max. Data Load (Kb)





N-Body Simulation



Number of Particles




Frame rate








Up to date, I find that Compute Shader based programs outperform C++ APM programs both in time and space. Results of example programs I explored, which have been created by the respective product teams tend to show substantially better performance by the Compute Shader programs. These programs are the N-Body Simulation Sample; Basic Summation; and the matrix multiplication programs from the “C++ AMP for the DirectCompute Programmer” guide. Hyperlinks are provided in the sections below.

So, the question is: can there be an AMP program that performs substantially better in time and space on, let’s say, large matrix multiplication (or large matrix-vector multiplication) than a Compute Shader program? C++ AMP has been built upon Direct Compute, so the answer is: not likely.

Should we, alternatively, draw the conclusion that a direct compute program categorically has better performance?

N-Body Simulation

The first pair of programs compared, consisted of:

Performance is expressed in gFLOPS. The code for the gFLOPS was copied from the C++ AMP version to the Compute Shader version. I also changed the Compute Shader version to make it write gFLOPS and the number of particles to the screen.

First, I tweaked the particle count parameter to get the best gFLOP count from either program; they both peak at 16,128 particles on my PC. Then the following results (gFLOPS) were obtained for release builds, running without debugging (this was also the configuration in the comparisons below).

C++ AMP Compute Shader More (%) Less(%)
Number of particles 16,128 16,128
Frames per second 43.46 57.38 32.03 24.26
gFLOPS 226.07 298.51 32.04 24.27

A note on the More and Less columns: The Compute Shader version delivers 32.03% more frames per second, and the C++ AMP version 24.26% less. So crudely: the Compute Shader version is about 30% faster.

Vector Addition

The second pair of programs compared consisted of:

The C++ AMP code was adapted as follows:

  • It was made to work with the same structs as the BasicCompute11 sample. This struct consists of an int and a float.
  • The arrays were made global variables.
  • A loop was added to fill the input arrays.
  • The verification code from the BasicCompute11 sample was added.

For timing, timing code was added to both programs. This timing code is from this post in the Parallel Programming in Native Code blog.

For timing measurements the code was adapted as follows: In the Compute Shader program timing covers code from the Dispatch call to the Map call. In the AMP program timing covers the lambda expression, and an added array_view::Synchronize() call on the “sum” array_view.

In experiments I first pushed the size until, in the case of the Compute Shader version, the output of the result verifying code became “failure”,

and in the case of the C++ AMP program, it either didn’t compile or produced a runtime error.

Then I measured time and gFLOPS. The experiments yielded the following result.

C++ AMP Compute Shader More (%) Less(%)
Number array elements 76*10^6 87*10^6 14.47 12.64
Total data size (Kb) 1,781,250 2,039,062.5
Time (ms) 6,868 8,182
gFLOPS 0.022 0.021

gFLOPS were measured as: 2*n / (10^6 * ms), where n is the number of elements in an array.

It seems to me that the time results are too similar to call them different. The Compute Shader version has a slight space advantage.

Note that since the total data size in both cases is larger than the RAM the graphics card has on board, there is some automatic sectioning going on.

Matrix Multiplication

Both programs in this comparison come from the C++ AMP for the DirectCompute Programmer guide. This guide can be obtained from a post on the official MSDN Parallel Programming in Native Code blog. The C++ AMP program is a transformation of the Compute Shader program.

The code for the starting point of the transformation is not entirely complete, so I added standard code from the BasicCompute11 Sample that loads and compiles the compute shader.

The following results were obtained.

C++ AMP Compute Shader More (%) Less(%)
Number array elements 4,608 7,616 65.28 39.50
Total data size (Kb) 248,832 679,728 173.17 63.39
Av Processing time (ms, 10 runs) 11,742 12,804
gFLOPS 8.3 34.5 315.66 75.94


  • Both programs measure the time spent in the “mm” function, using the timing code referred to above. This includes uploading and offloading the data onto and from the GPU.
  • For both programs we have that any higher multiple of 64 in the number of array elements crashes the display driver.

  • gFLOPS are measured as: n^3 / (10^6 * ms) where:
  • n is the size of a matrix dimension (the matrices are square).
  • Ms is the averaged (over 10 iterations) measured processing time in milliseconds.


Three program pairs have been compared, informally and semi-systematically, for their performance in time and space.

In the case of the N-Body simulation, the data load was selected that is optimal for time performance. That resulted in an about 30% better time performance of the Compute Shader Program.

In the case of vector addition – about the simplest program imaginable in this context – the time performance was measured for maximum data load. This resulted in practically equal time performance for both programs. The Compute Shader version can load some more data.

Finally, the programs from the AMP guide for Compute Shader programmers were implemented, and the time performance was again measured for maximum data load. This resulted in a time performance of the Compute Shader that is three times as good as the time performance of the AMP program.

So, conclusion, it seems that if you want to get the max from your GPU, a Compute Shader is still the way to go.

PInvoking DirectX from Silverlight

Before moving on to Windows 8 development, I decided to write some legacy software. Well actually, this legacy software is perfectly up-to-date Windows 7 level software; tricks presented here will be useful for years to come. It’s just that Windows 8 (Consumer Preview) provides standard solutions to the problems solved here. This blog post discusses the use of a DirectX application, packaged as a DLL, by a Silverlight application, via PInvoke.

The problems tackled here stem from the desire to have Rich Internet Applications (RIAs) for Windows, that use computational resources on the client computer. In particular DirectX for 3D-graphics, X3dAudio, for 3D-audio, and also the GPU (Graphics Processing Unit – a powerful, highly parallel processor). Silverlight provides the facilities to write RIAs, but has a somewhat outdated 3D-graphics library: a subset of XNA – a managed wrapper for DirectX9 (but we want DirectX11, at least!). This Silverlight 3D-graphics library is not very extensive, it lacks e.g. 3D-audio.

On the other hand, Silverlight does provide facilities for interoperability with native code, e.g. by means of PInvoke: the invocation of native code in Dynamic Link Libraries (DLLs). PInvoke is here the bridge between Silverlight and DirectX code.

This blog post presents:

  • A sample DirectX11 application, and its transformation into a DLL to be used from Silverlight.
  • A Silverlight application that calls methods in the dll.
  • How to install and uninstall the DLL, and how to manage its lifetime explicitly, so the DLL may be uninstalled by the Silverlight application itself.
  • Performance aspects of the Silverlight-DirectX application, and a comparison with a Silverlight application that uses the Silverlight 3D-graphics library for the same task.
  • Concluding remarks, for one thing that this application should have had 3D-audio to decisively mark the advantage of the approach presented here (but at some point, you just have to round up).

The DirectX 11 Sample Application

The DirectX 11 Tutorial05 sample application will serve as the application a user wants to run on his or hers PC, and that uses resources already present on that PC. This DirectX application is the most simple application that contains some animation, and it has also a part – the small cube – that we can multiply in order to generate data for various performance loads.

To that end we transform it into a DLL with as much unnecessary functionality stripped, and an adequate interface added, including the code to transfer the data we need in the Silverlight application. Let’s take a look at the main changes.

Minimizing Window Management Code

For starters, We do not need a window, we use the DirectX application only to compute the 3D-graphics we present in the Silverlight application. The wWinMain (application entry point) function now looks like this:

Sample code like above is entered into the text as pictures. If you would like to have the code, just leave a comment on this blog with an e-mail address and I will ship it to you.

The function has no “Windows” parameters any more, nor has it a main message loop. The InitWindow function has been reduced to:

We do need to create a window in order to create a swap chain, and only for that reason, so we keep it as simple and small as possible. Note that the wcex.lpfnWndProc is assigned the DefWindowProc. That is: the application has no WindowProc of its own.

Create Texture to be Used in Export

In order to export the 3D-graphics data, an additional texture (a texture is a pixel array) called g_pOutputImage is created in the InitDevice function:

This texture has usage “Staging”, so the CPU can access it, and we specified CPU access as “Read”. With these settings we can’t bind the texture to the DeviceContext anymore, so no BindFlags. Note that we cannot have a texture that the GPU writes to, and the CPU reads from. If that would have been possible we could have had a data structure that both DirectX and Silverlight could have used directly. Since this is impossible we will have to perform expensive copy operations. Alas.

A final change in this same function is that we do not release the pointer to the back buffer, but keep it alive in order to export the graphics data in the Render function.

Rendering 3D-Graphics

The Render function has a loop added so we can have multiple small cubes. The idea is to compute a World matrix for each additional small cube. That is, we have only one cube, but draw it multiple times at different locations. Like this:


Converting and Exporting 3D-Graphics Data

Finally, we want to copy the 3D-graphics data into an array the Silverlight client has provided, so that the client can show it to the user. This is done like so:

The above is standard code, I obtained it around here (the direct link seems broken). The ConvertToARGB function, however is a custom addition, replacing the memcpy call (more about that in the section on performance). This ConvertToARGB converts the RGBA format of DirectX to the premultiplied (PM) ARGB format used in Silverlight. This PM ARGB format is considered legacy now. The conversion step is a real performance hit as anyone can imagine. The function looks like this:

Essentially this OR-s 4 integers, the first one is constructed by byte-shifting the A (transparency) byte all to the left, then 3 integers are created by pushing the RGB bytes in place. This is a fast algorithm since shifting is a quick operation. I found it here. After the conversion, the pixels are in the correct format in an array that is owned by the Silverlight client application.

The DLL Interface

The interface has the following methods:

And for performance measurements:

The above functions return an average time over the Render function, and an average time over the conversion and export respectively. Details will be discussed below. The

decoration results in a clean export of the function names. Without the decoration, the C++ compiler will add a number of tokens (among which at least a few like @#$%^&*) to the function name in order to make it unique. The problem with this is that you’ll have a hard time retrieving the actual function name for use in the Silverlight client.

The Silverlight Client

General Architecture

The application has the following structure:

The App class is the application entry point (as usual). The Application_Startup event handler, depicted below,

first checks if the application is running out-of-browser (OOB). Running OOB is the intended normal use of this application. If so, a MainPage control is instantiated which will run the DirectX code. If the application is running in-browser, it still needs to be installed. Only after installation, the application has access to the file system – required to save and load the dll – and to the GPU. The application requires Windows 7 or higher and bails out if a lower level Windows or Apple OS is found.

The install page offers to install the application on the user’s PC, as depicted below,

or tells the user that the application is already installed, and hints at ways to uninstall the application if so desired.

If the user installs the application, it starts running out of browser and shows the MainPage with the DirectX animation.

Installing, Uninstalling, and Managing DLL Lifetimes

Installing includes saving the DirectX application in the DLL to a file on the user’s PC. The DLL is packaged with the Silverlight application as a resource. For execution, the DLL has to be loaded in memory, or be present on the PC as a file. Saving the DLL to file is done with code after an example from the NESL application. We store the application at “<SystemDrive>ProgramDataRealManMonths PInvokeDirectXTutorial05”.

Once the DLL is saved to file we load it into memory using the LoadLibrary function from the kernel32.dll. The reason we manage the dll’s lifetime explicitly instead of implicitly by importing the dll, and calling its functions, is that we need to be able to explicitly remove the dll from memory when exiting the application, see below. Loading into memory requires a dll import declaration:

And a call of this function, in the MainPage_Loaded event handler:

Where DllPath is just the path specified above. Is that all? Yes, that’s all.

When the application is exited, we use the handleToDll to release the library with repeated calls to FreeLibrary. Declaration:

Then we call it in the Application_Exit event handler as follows:

The point is that each method we import from the dll increases the reference count. As long as the reference count is larger than zero we cannot unload the DLL, nor delete its file. Not being able to delete the file means we cannot properly uninstall the application – we would leave a mess. Once the ref count is zero, FreeLibrary unloads the library from memory.

The final question in this section is why we delete the dll file every time we exit the application, and create the file every time we start it up. The reason is that if we do not do that, and the user uninstalls the application from the InstallPage (running in-browser), the application does not have the permissions to access the file system, hence the DLL file will not be deleted. So, all these file manipulations are bound to the runtime of the application in order to have a clean install and uninstall experience for the user.

PInvoking the DirectX Functions

Now that the application can be installed, functions from the DirectX application interface can be declared and executed.

[DllImport(DLL_NAME, SetLastError = true, CallingConvention = CallingConvention.Cdecl)]

public extern static int Init(int width, int height,

[MarshalAs(UnmanagedType.LPWStr)] String effectFilePath);

[DllImport(DLL_NAME, SetLastError = true, CallingConvention = CallingConvention.Cdecl)]

public extern static void Render([In, Out] int[] array);

[DllImport(DLL_NAME, SetLastError = true,CallingConvention = CallingConvention.Cdecl)]

public extern static int Cleanup();

[DllImport(DLL_NAME, SetLastError = true, CallingConvention = CallingConvention.Cdecl)]

public extern static void GetRenderTimerAv(ref double pArOut);

[DllImport(DLL_NAME, SetLastError = true, CallingConvention = CallingConvention.Cdecl)]

public extern static void GetTransferTimerAv(ref double pArOut);

We make a call to the Init function in the MainPage_Loaded event handler, calls to the dll Render function, in the local Render method, and a call to CleanUp in the Application_Exit event handler.

Calls to the timer functions are made when the user clicks the “Get Timing Av” button on the MainPage.

Debugging PInvoke DLLs

At times you may want to trace the flow of control from the Silverlight client application into the native code of the DLL. This, however is not possible in Silverlight. Silverlight projects have no option to enable debugging native code. Manually editing the project file doesn’t help at this point. Now what?

A work around is to create a Windows Presentation Foundation (WPF) client. I did this for the current application. This WPF application does not show the graphics data the DirectX library returns, it just gets an array of integers.

To trace the flow of control into the DLL you need to uncheck ”Enable Just My Code (Managed only)” at (in the menu bar) Tools | Options| Debugging, and to check (in the project properties) “Enable native code debugging” at Properties | Debug | Enable Debuggers.

If you now set a breakpoint in the native code and start debugging from the WPF application, program execution stops at your breakpoint.

Reactive Extensions

In order to have a stable program execution, the calls to the dll’s Render method are made on a worker thread. We use two WriteableBitmaps, one is returned to the UI thread upon entering the Silverlight method that calls the dll’s Render method, the other WriteableBitmap is then rendered to by the DLL. After rendering, the worker thread pauses to fill up a time slot of 16.67ms (60 fps).

Thread management and processing the indices that point into the WriteableBitmap array (implementation detail J ) is done using Reactive Extensions (RX). The idea is that the stream of indices the method returns is interpreted by RX as an Observable collection and ‘observed’ such that it takes the last index upon arrival, and uses the index to render the corresponding WriteableBitmap to screen. This results in elegant and clean code, as presented below.

The first statement create an observable collection from a method that returns an IEnumerable. Note that ‘observing’ is on the UI thread (referred to by the ‘DispatcherScheduler’)

The SubscribeOn(ScheduleNewThread)-clause creates a new thread for the render process. The lambda expression defines the action if a new int (index) is observed.

Rendering on the worker thread proceeds as follows:

To stop rendering we just put IsRunning to “false”. And that’s it.


DirectX applications – by definition – have higher performance than .Net applications. However, if you pull out the data from a DirectX application and send it elsewhere, there is a performance penalty. You will be doing something like this:

CPU -> GPU -> CPU -> GPU -> Screen instead of CPU -> GPU -> Screen

The extra actions: copying data from the GPU to CPU accessible memory and converting to Premultiplied ARGB will take time. So the questions are:

  1. How much time is involved in these actions?
  2. Will the extra required time pose a problem?
  3. How does performance compare to the Silverlight 3D-graphics library?
  4. Are there space (footprint) consequences as well?

Before we dive into answering the questions, note that:

– The use of DirectX will be primarily motivated by the need to use features that are not present in the Silverlight 3D-Graphics / -Audio library at all. In such cases comparative performance is not at all relevant. Performance is relevant if the use of DirectX becomes prohibitively slow.

For the measurements I let the system run without fixed frequency; usually you would let the system run at a frequency of 60Hz, since this is fast enough to make animations fluent. At top speed, the frequency is typically around 110Hz. I found no significant performance differences between debug builds and release builds.

Visual Studio 11CP Performance analysis: Sampling

If we run a sampling performance analysis – this involves the CPU only, the bottleneck in the process becomes clear immediately: The conversion from RGBA to premultiplied ARGB (and I’m not even pre-multiplying) takes 96.5% of CPU time.

It is, of course, disturbing that the bulk of the time is spent in some stupid conversion. On the other hand, work done by the GPU is not considered here.

To investigate the contribution of the conversion further, I replaced the conversion by a memcpy call. Then we get a different color palette J, like this:

But look, the frequency jumps up to 185 fps (80% more). The analysis then yields:

That is: much improved results, but shoving data around is still the main time consumer. Note that the change of color palette by the crude reinterpretation of the pixel array is a problem we could solve at compile time, by pro-actively re-coloring the assets.

Compare to a Silverlight 5 3D-library application

Would the performance of our application hold up to the performance of a Silverlight application using the regular 3D-graphics library? To find out I transformed the standard Silverlight 3D-graphics starter application to a functional equivalent of our Silverlight-DirectX application, as depicted below – one large cube and 5 small cubes orbiting around it (yes, one small cube is hidden behind the large one).

If we click the “Get Timing Av button”, we typically get a “Client Time Average” (average time per Draw event handler call) of 16.6.. ms, corresponding to the 60 fps. The time it takes to actually render the scene averages to 3.3 ms. This latter time is 0.8ms without conversion, and 2.8ms with conversion for the Silverlight – DirectX application (if we let it run at max frequency). So, the Silverlight-DirectX alternative can be regarded as quicker.

If we look at the footprint, we see that the Silverlight-DirectX application uses 1,880K of video memory, and has an image of 50,048K in the Task Manager. The regular Silverlight application uses 5,883K of video memory, and has a 37,708K image. Both in SLlauncher. So, the regular Silverlight application is smaller.

Concluding Remarks

For one, it is feasible to use DirectX from Silverlight. PInvoke is a useful way to bridge the gap. This opens up the road to use of more, if not all, parts of the DirectX libraries. In the example studied here, the Silverlight-DirectX application is faster, but has a larger footprint.

We can provide the user with a clean install and uninstall experience that covers handling and lifetime management of the native dll.

Threading can be well covered with Reactive extensions.

There is a demo application here. This application requires the installation of the DirectX 11 and the Visual C++ 2010SP1 runtime packages (links are provided at the demo application site). I’ve kept these prerequisites separate, instead of integrating their deployment in the demo application installation the NESL way, mainly because the DirectX runtime package has no uninstaller.

If you would like to have the source code for the example program, just create a comment on this blog to request for the source code, I’ll send it to you if you provide an e-mail address.

A Windows Port of the 3D Scan 2.0 Framework

The 3D Scan 2.0 Framework by the Chair for Virtual Reality and Multimedia of the Computer Science Institute, TU Freiberg. Is a software framework that uses the Microsoft Kinect to create 3D scans. The software is based on a number of well known open source frameworks:

Open Kinect, OpenSceneGraph, the ARToolkit, and VCGLib. Oddly enough, the 3D Scan 2.0 Framework is written in C++ and available for Linux only. Oddly, since the kinect is a Microsoft product, and all above supporting frameworks have a Windows version as well. Reason to try and port the framework to Windows.

Downloading and Building the Supporting Frameworks

The Windows ports of the supporting libraries are of high quality and present no problems.

For OpenKinect you just follow the instructions on the Getting Started page. These instructions will let you download and use tools like Cmake-Gui, that translates files from the Linux build system into Visual Studio solutions and projects; Tortoise, a Subversion client; 7Zip and Notepad++. This latter tool knows how to handle the Linux / Unix vs. Windows LF/CR issues better than the standard Windows tools, which provides for much more comfortable consumption of readme-s.

Part of OpenKinect for Windows is libusb10emu. The 3D Scan Framework also needs it. So, the sources and headers files will have to be added to the SciVi solution, see below.

The instructions let you download and install libusb-win32, pthreads-win32, and Glut. All provide binaries and include files. Other supporting frameworks use these as well. After working my way through the instructions, OpenKinect works like a charm.

OpenSceneGraph provide the source code, but you can download nightly release and debug builds for Windows from AlphaPixel, also for Visual Studio 2010; both 32 and 64 bits versions. The test programs than usually run fine (there are many, and some just don’t run at once).

VCGLib just needs to be downloaded, no building required.

The ARToolkit does require building. In order to build it with Visual Studio 2010, just convert the solution and build it (a few times; the order of projects is not such that a single build will do). After building a number of test applications will run nicely.

Building the 3D Scan 2.0 Framework

To build the 3D Scan 2.0 Framework you have to create your own Visual Studio 2010 solution. This solution has three projects: the Poisson lib, the Poisson surface construction algorithm; libusb10emu, emulates the libusb1.0 library – usb aspects available for Linux but not for windows; and Scivi, the scanner, the generator and test & benchmark.

Building the solution brought few strange errors like the use of “and” instead of && in the code, but no big issues.

Building the 3D Scan 2.0 Framework

– Revolves around libusb10emu

The Test Applications

– The framework contains a number of test applications: testOsg; testKinect; test; testColor; and testPoisson .

The testOsg Application

Worked immediately, without a problem, see image below.

Shrinking/ expanding a cube is by rolling the scroll button after clicking the cube involved.

The testKinect Application

This is a relatively simple application that reads the serial number from the kinect, and queries and displays its video modes, both color and depth. At this point we ran into some hard problems with the libusb10emu lib. The application presumes an initialized usb device here, that has a ‘context’ and a mutex. However, there is no initialized libusbcontext, and there is not going to be one either. This results in the application using a non-existing mutex, and Bingo!

On the other hand, if we skip the code that digs up the serial number and just provide a mock-up (I chose ‘1’), the application works fine.

The testKinect Application

The central test application. Also digs up the serial number of the kinect. It requires the serial number in order to load and store calibration data. So, I just made up a mock file name in the code: “test”, and skipped querying the serial number. Then the application works fine. It even reeds calibration data, and does some calibrating, after renaming a designated file to “test.yml.

If you look at the picture of me (yes, that’s me) made using the Test application, and compare it to the images in the calibration section of the documentation, you would conclude that the calibration is sufficient, if compared to the image with the uncalibrated kinect.

The code also contains undocumented “I” and “k” control options that rise and lower an AR threshold. I don’t know what that does. There is also an “s” control button. It provides statistics overlays. Push it three times for increasingly more information, and a fourth time to remove all overlays again.

A quirk of this application is that it randomly portrays it subject mirrored or not. One time you’ll see the image presented correctly, another run the scene is presented mirrored.

The Poisson Benchmark and testPoisson Applications

After adjustment of the hard coded files paths, the program gets to work as seems to be its design. It loads the vertex file (phone), counts the vertices (80870), and starts to remove duplicates. Then after a short while the application throws an exception.

The same holds for the testPoisson application. The exception is generated in code for a not very accessible algorithm that removes duplicate vertices. I don’t know why the application throws an exception, but it does both decrements an iterator (also changes its values otherwise) and erases elements from the vector. Most probably the algorithm needs some specific parameters to operate correctly.

The testColor Application

Couldn’t be run because the required data isn’t available for download at the project’s web site.

The Scan and Generate Applications

The scan applications generates a point cloud with data from the kinect, the Generate application creates a mesh from the point cloud.


The scan application required an adaptation in the OSG lib. The Kinect delivers RGB color data, not RGBA color data, so the assumptions in the code had to be adapted accordingly.

I made a scan of our teapot. With some imagination you can discern the teapot from the point cloud, but it is not very convincing. Obviously, some extra parameter tuning needs to be done to obtain result that match the examples from the project site in quality, although I don’t know how.


The generate application loaded the point cloud file successfully

and ran into the above mentioned error when removing duplicates.


I think the idea of using the Kinect for 3D scanning is fabulous. However, it turns out that it is not straightforward to use the software. A number of questions and problems arise, mainly concerning removing duplicate vertices and tuning the scan mechanism (calibration?). Perhaps the members of the 3D scan 2.0 Framework team can help out. If so , more result will follow on this blog.