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Chapter 46. Improved GPU Sorting
Peter Kipfer
Technische Universität München
Rüdiger Westermann
Technische Universität München
Sorting is one of the most important algorithmic building blocks in computer science. Being able to efficiently sort large amounts of data is a critical operation. Although implementing sorting algorithms on the CPU is relatively straightforward—mostly a matter of choosing a particular sorting algorithm to use—sorting on the GPU is less easily implemented because the GPU is effectively a highly parallel single-instruction, multiple-data (SIMD) architecture. Given that the GPU can outperform the CPU both for memory-bound and compute-bound algorithms, finding ways to sort efficiently on the GPU is important. Furthermore, because reading back data from the GPU to the CPU to perform operations such as sorting is inefficient, sorting the data on the GPU is preferable.
Buck and Purcell 2004 showed how the parallel bitonic merge sort algorithm could be used to sort data on the GPU. In this chapter, we show how to improve the efficiency of sorting on the GPU by making full use of the GPU's computational resources. We also demonstrate a sorting algorithm that does not destroy the ordering of nearly sorted arrays at intermediate steps of the algorithm. This can be useful for effects such as particle systems, where geometric objects need to be sorted according to viewer distance but small sorting errors can be tolerable temporarily in exchange for improved application response.
46.1 Sorting Algorithms
Sorting algorithms are among the most important building blocks of virtually every program. Computer graphics applications require visibility sorting for correctly rendering transparent objects and efficiently exploiting acceleration features such as the early-z test. In physics simulation, sorting is necessary for inserting the participating objects into spatial structures for collision detection.
Sorting algorithms can be divided into two categories: data-driven ones and data-independent ones. In practice, the fastest algorithms are data-driven, which means that the step the algorithm takes depends on the value of the key currently under consideration. This may result in unexpected bad performance if the sequence to sort is already in order. The well-known Quicksort algorithm is one example. When sorting n items, it has O(n log(n)) complexity on average, which is provably optimal. But in the worst case, Quicksort has O(n ^{2}) complexity, which is not acceptable. There are other sorting algorithms such as Heapsort that do not have this problem, but they exhibit more difficult data access patterns or require more comparison operations. Heapsort is most useful when you need only partially ordered lists (such as in a priority queue).
The second category of algorithms, the data-independent ones, does not exhibit this discrepancy. As the algorithms do not change their processing according to the current key value, they always take the same processing path. This makes their operation sequence completely rigid, a fact that we can exploit to implement them on multiple processors, because the points at which communication must occur are known in advance. This feature is key when considering an implementation of sorting on the GPU, because the sorting program will run on the fragment processors, which cannot change their output location in memory based on input data values.
46.2 A Simple First Approach
Data-independent sorting algorithms can be represented as a sorting network, as shown in Figure 46-1. To study the general approach, let's look at a simple example. For simplicity, we assume that we are sorting a 1D array of keys. In the sorting network, each column is a series of compare operations that execute in parallel. We call this one pass of the sorting network. The input keys get sorted as they travel from left to right through the network. In this simple example, called odd-even transition sort, if the compare operation is less-than, as indicated by the direction of the arrows, small keys will move one row upward with each iteration, while large keys move downward. In the worst case, if the smallest key starts in position n, it will take n iterations to bring it all the way up to position 0. This sorting algorithm therefore has a complexity of O(n ^{2}), which is not optimal.
Figure 46-1 The Odd-Even Transition Sorting Network for Keys
Implementation of this algorithm is easy, and it is easily parallelizable in nature. We render to two texture buffers in double-buffer mode. For each pass, we draw a full-size quad and execute a fragment program that fetches the key at its fragment's position, fetches the key at its neighbor's position from the source buffer, compares the two keys, and writes the result to the target buffer. Note that because we can write only one fragment at a time, comparing two keys involves processing two fragments; we perform a less-than comparison on the first and a greater-equal comparison on the second. The decision of which operation to perform can be computed with a modulo operation on the current row number. This also means that both fragments do two data (texture) fetches. This approach therefore needs twice the bandwidth of a CPU implementation that fetches the data once and then swap-writes the result according to the comparison result.
46.3 Fast Sorting
Although each pass of the simple network is very cheap, the number of passes to perform a complete sort will be too high to be acceptable in most (real-time) situations. Suppose we use the sorter within a particle engine to keep the particles in back-to-front order for correct transparent rendering with depth test turned on. We will run into serious performance problems with such an approach, because we will want to use a large number of particles (up to one million). Even with a faster sort, we might want to distribute the workload over several displayed frames, and we might even accept locally inexact ordering if it converges to the full sort within a short period of time.
For such an application, what we would like to have is a fast yet smooth transition of the input into an ordered sequence. This will allow us to use intermediate results of the algorithm that converge to the correct sequence while we do more passes incrementally (Kolb et al. 2004). Figure 46-2 shows a sorting network that has the desired property: the odd-even merge sort (Sedgewick 1998). The idea is to sort all odd and all even keys separately and then merge them. Each step like this is called a stage of the sorting algorithm. The stages are then scaled and repeated for all powers of two until the whole field is merged. To sort the m keys of a stage, log(m) passes are needed. For sorting all n keys, we therefore need log(n) stages, resulting in an overall O(n log^{2}(n) + log(n)) passes. This is more than the O(n log(n)) of Quicksort, but it is still optimal in the limit case, which is why odd-even merge sort is also called an optimal algorithm. Sorting one million keys therefore takes 210 passes, compared to one million passes for the simple first approach.
Figure 46-2 The Odd-Even Merge Sorting Network for Keys
46.3.1 Implementing Odd-Even Merge Sort
We take the same approach as in the simple odd-even transition sort example shown earlier. The sorting algorithm is implemented in a fragment program. It is driven by two nested loops on the CPU that just transport stage, pass number, and some derived values via uniform parameters to the shader before drawing the quad. If we want to sort many items, we have to store them in a 2D texture. To sort the entire 2D field, the fragment program must convert the 1D array index into 2D texture coordinates. Listing 46-1 is GLSL code for the odd-even merge sort fragment shader.
Example 46-1. The GLSL Fragment Program Implementing Odd-Even Merge Sort
uniform vec3 Param1; uniform vec3 Param2; uniform sampler2D Data; #define OwnPos gl_TexCoord[0] // contents of the uniform data fields #define TwoStage Param1.x #define Pass_mod_Stage Param1.y #define TwoStage_PmS_1 Param1.z #define Width Param2.x #define Height Param2.y #define Pass Param2.z void main(void) { // get self vec4 self = texture2D(Data, OwnPos.xy); float i = floor(OwnPos.x * Width) + floor(OwnPos.y * Height) * Width; // my position within the range to merge float j = floor(mod(i, TwoStage)); float compare; if ( (j < Pass_mod_Stage) || (j > TwoStage_PmS_1) ) // must copy -> compare with self compare = 0.0; else // must sort if ( mod((j + Pass_mod_Stage) / Pass, 2.0) < 1.0) // we are on the left side -> compare with partner on the right compare = 1.0; else // we are on the right side -> compare with partner on the left compare = -1.0; // get the partner float adr = i + compare * Pass; vec4 partner = texture2D(Data, vec2(floor(mod(adr, Width)) / Width, floor(adr / Width) / Height)); // on the left it's a < operation; on the right it's a >= operation gl_FragColor = (self.x * compare < partner.x * compare) ? self : partner; }
We now have an algorithmically efficient sorter. However, when taking a critical look at the code and how it uses the GPU resources, we notice some shortcomings that are typical of attempts to bring algorithms to the GPU. First, we still need twice the number of data fetches as the CPU. Second, the code seems to do many operations on uniform parameters. Can we pull them out of the fragment shader and save unnecessarily repeated computation? Third, the shader uses branching and thus may unnecessarily execute many operations even for values that only need to be copied. Also, what are the vertex unit and the rasterizer doing all the time? Almost nothing. Can we do better?
46.4 Using All GPU Resources
The GPU is optimized for very regular highly parallel access patterns. In the odd-even merge sort, some values get compared, while others just get copied. We would like to avoid this situation, because both cases take the same time to execute. We can do more comparisons per pass without penalty if we get rid of the copies. Overall, this will make the program even faster, because the determination of whether to copy or compare can be dropped. This is due to the network performing alternating odd-even accesses. In summary, we need a different sorting network.
Figure 46-3 shows the sorting network for the bitonic merge sort algorithm (Batcher 1968), which builds both an ascending and a descending subsequence of keys (that is, a bitonic sequence) and then merges the two subsequences. Starting with n/2 two-item sequences, this continues until one fully sorted ascending sequence is left. This means we have to build log(n) bitonic sequences and merge them. The sorting network reflects this pattern, showing log(n) sorting stages, where the ith stage has to perform i passes. This results in a total complexity of O(n log^{2}(n) + log(n)), which is the same as the odd-even merge sort. This is still not as fast as Quicksort, but remember that we do not have a bad worst-case complexity here.
Figure 46-3 The Bitonic Merge Sorting Network for Keys
The straightforward implementation would be to simply encode the stage and pass numbers in texture coordinates and have the fragment shader execute the "inner loop" of the sorting procedure (that is, one column of the network), as other work has shown (Purcell et al. 2003). However, from Figure 46-3, we note that this incurs only integer operations for computing the compare distance and change of compare operation. Moreover, these changes occur always at positions that are powers of two and do not straddle certain power-of-two boundaries. Unfortunately, integer operations that could efficiently detect these cases are not supported on current GPUs. They therefore have to be emulated by appropriate floating-point operations. Specifically, the operations that we need for the sorting procedure, such as modulo and bitwise operations, are possible only with floating-point arithmetic on the GPU. The straightforward implementation of this algorithm in a fragment shader will therefore not be as efficient as possible.
We need a smarter solution, one that reduces the amount of work done in the fragment units and makes use of the vertex processors and the rasterizer (Kipfer et al. 2004). By rotating Figure 46-3 90 degrees to the right, we obtain Figure 46-4, for sorting a 1D array of n keys. Observe that in each pass, there are always groups of items that are treated alike (yellow or green boxes). This means that their sorting parameters are the same. Moreover, there is a strong relationship between neighboring groups: they sometimes have parameters with opposite values. These changes depend on the stage and pass and can be expressed as a simple modulo relationship. In sorting passes where there is more than one group, instead of drawing one single, big quad and computing the appropriate parameters in the fragment shader, we now draw several quads per sorting pass that exactly fit pairs of groups (one yellow and one green box) and together cover the entire buffer. On the right side of each quad, we perform the operation opposite that of the left side. To compute this flip, we observe that texture parameters specified in the vertex program are linearly interpolated by the rasterizer over the fragments. If we simply specify a flag value of +1 on the left side of the quad and a flag value of -1 on the right side, the fragment program can determine the current fragment's position within the quad by simply looking at the sign of the flag.
Figure 46-4 Grouping Keys for the Bitonic Merge Sort
The remaining two actions that have to be computed are to decide which compare operation to use and to locate the partner item to compare. Both can also be expressed using the interpolation technique. We first fix the compare operation to be a less-than operation. Multiplying the two key values with the interpolated flag value from the vertex shader now makes the operation implicitly flip to greater-equal halfway across the quad, which is exactly what we want. The same is done for the compare distance. Its absolute value is constant for the quad. We extract the sign of another interpolated flag value and multiply it with the absolute value in order to not scale the distance. The numeric precision of the interpolator on all current GPUs is high enough to prevent subpixel rounding problems.
If we want to sort many items, we have to store them in a 2D texture. To sort an entire 2D field, we understand each row of the field to be one bitonic sequence of the whole field. We therefore perform the same sorting operations for every row. The quads we have introduced previously need simply to be extended vertically to cover all rows. Figure 46-5 shows the compare operation performed and the underlying quads that are rendered. If we now introduce another modulation to the compare operation according to the row number, we easily get rows completely sorted in alternating ascending/descending order. All that's left is to merge them in pairs, groups of 4, groups of 8, groups of 16, until the whole field is processed. Note that this uses the same sorting procedure along the columns that we used along the rows. We take the same approach for the columns as for the rows, using vertical compare distances (instead of horizontal) by simply transposing the quads that form each pass.
Figure 46-5 Bitonic Merge Sort of Rows of a 2D Field
The final optimization we make is to generalize the sorter to work on key/index pairs. Because the GPU processes four-vectors, we can pack two key/index pairs into one fragment. Because the last pass of each sorting stage only compares neighboring items (see Figure 46-3), this will happen inside the fragment, saving the second texture fetch. Packing two consecutive items also cuts the row width in half and consequently the overall number of fragments to process. We now do the same number of data fetches as a CPU implementation. Note that the concept of packing can also be used for the odd-even transition sort shader, but there it obviously helps only every other pass avoid the second data fetch.
46.4.1 Implementing Bitonic Merge Sort
For the sample application of sorting particles according to camera distance, we implement a small fragment program that computes the sorting keys (distance) and indices (particle numbers) and packs two of them into one RGBA texture value. Because we must touch every item to sort anyway, we take the opportunity and also perform the very first sorting pass while writing the packed representation. The first pass simply has to produce alternating ascending/descending pairs of keys—an operation that is internal to each fragment (see Figure 46-3).
All quads for one sorting pass are recorded in a display list. To sort the entire field, we just call the next list and swap the buffers until we're done. The display list records the vertices of the quads and two texture coordinates. The first texture coordinate holds all data that varies per quad (that is, the fragment position, the search direction, and the compare operation flag that is to be interpolated). The second texture coordinate holds data that is constant for the quad. Because we pack two consecutive items into one fragment, in the last pass of each stage, the fragment shader accesses the same items as the previous pass (compare Figure 46-4). We account for this by folding both passes into one, using a special shader for this case. Note that the fragment program does not need to perform a conversion from 1D indices to 2D texture coordinates, because this is done implicitly by the texture coordinates of the quads. Listing 46-2 shows the GLSL code for the special program.
The program for passes greater than 1 along the rows skips the last compare operation in Listing 46-2 and returns the result variable. The program for passes along the columns differs just in building the adr variable: here x is 0 and y is computed according to the search direction.
In summary, we need four programs to perform the sorting: The first one computes the key/index pairs and does the first sorting pass. The second performs row sorting for passes 0 and 1 of each stage simultaneously. The third does row sorting for all passes greater than 1 of each stage, and the fourth performs the column sort.
When the sort is done, we have a permutation of the key/index pairs. The application can now reorder any data buffer according to the sorting criterion by simple indexed lookup.
Example 46-2. GLSL Fragment Program Implementing the Combined Passes 1 and 0 for Row-wise Sorting of the Bitonic Merge Sort
uniform sampler2D PackedData; // contents of the texcoord data #define OwnPos gl_TexCoord[0].xy #define SearchDir gl_TexCoord[0].z #define CompOp gl_TexCoord[0].w #define Distance gl_TexCoord[1].x #define Stride gl_TexCoord[1].y #define Height gl_TexCoord[1].z #define HalfStrideMHalf gl_TexCoord[1].w void main(void) { // get self vec4 self = texture2D(PackedData, OwnPos); // restore sign of search direction and assemble vector to partner vec2 adr = vec2( (SearchDir < 0.0) ? -Distance : Distance, 0.0); // get the partner vec4 partner = texture2D(PackedData, OwnPos + adr); // switch ascending/descending sort for every other row // by modifying comparison flag float compare = CompOp * -(mod(floor(gl_TexCoord[0].y * Height), Stride) - HalfStrideMHalf); // x and y are the keys of the two items // --> multiply with comparison flag vec4 keys = compare * vec4( self.x, self.y, partner.x, partner.y); // compare the keys and store accordingly // z and w are the indices // --> just copy them accordingly vec4 result; result.xz = (keys.x < keys.z) ? self.xz : partner.xz; result.yw = (keys.y < keys.w) ? self.yw : partner.yw; // do pass 0 compare *= adr.x; gl_FragColor = (result.x * compare < result.y * compare) ? result : result.yxwz; }
46.5 Conclusion
We have implemented the bitonic merge sort very efficiently on the GPU. Table 46-1 lists the number of passes required to sort various field sizes and the sorting performance obtained on a 425 MHz NVIDIA GeForce 6800 Ultra GPU. The CPU and the GPU sorted 16-bit key/16-bit index pairs. ^{[1]} Additional performance information can be found in Kipfer et al. 2004. Note that the CPU timings do not include the time that would be necessary for upload. A limitation of the bitonic merge sort is that we cannot distribute sorting passes over multiple displayed frames in order to make the system more responsive, because the intermediate stages of the sort do not maintain a globally ordered sequence. Because they always produce a bitonic pair (one ascending and one descending partial sequence), at least half of the field gets completely reordered in each stage.
Table 46-1. Performance of the CPU and GPU Sorting Algorithms
std::sort: 16-Bit Data, Pentium 4 3.0 GHz | |||
N | Full Sorts/Sec | Sorted Keys/Sec | |
256^{2} | 82.5 | 5.4 M | |
512^{2} | 20.6 | 5.4 M | |
1024^{2} | 4.7 | 5.0 M | |
Odd-Even Merge Sort: 16-Bit Data, NVIDIA GeForce 6800 Ultra | |||
N | Passes | Full Sorts/Sec | Sorted Keys/Sec |
256^{2} | 136 | 36.0 | 2.4 M |
512^{2} | 171 | 7.8 | 2.0 M |
1024^{2} | 210 | 1.25 | 1.3 M |
Bitonic Merge Sort: 16-Bit Float Data, NVIDIA GeForce 6800 Ultra | |||
N | Passes | Full Sorts/Sec | Sorted Keys/Sec |
256^{2} | 120 | 90.07 | 6.1 M |
512^{2} | 153 | 18.3 | 4.8 M |
1024^{2} | 190 | 3.6 | 3.8 M |
The odd-even merge sort maintains the sort-while-drawing property, but the last pass of each stage performs a merge of the odd and the even items. This is not a good access pattern for the packing we have done. For this sorting algorithm, it thus does not make sense to combine the last two passes of each stage into a special program, as we did with the bitonic merge sort. The implementation therefore performs log(n) more passes than the bitonic merge sort. Although this results in inferior overall performance, the workload can be spread over several displayed frames.
You now have two optimal sorting algorithms, each with its own advantages and drawbacks. Both, however, execute very efficiently on the GPU. So if your shiny new GPU algorithm requires sorting, there is no longer a need to return to the CPU. On the book's CD, you will find a small demo program implemented in C++, OpenGL, and GLSL that performs the GPU sorting techniques discussed in this chapter.
46.6 References
Batcher, Kenneth E. 1968. "Sorting Networks and Their Applications." Proceedings of AFIPS Spring Joint Computer Conference 32, pp. 307–314.
Buck, Ian, and Tim Purcell. 2004. "A Toolkit for Computation on GPUs." In GPU Gems, edited by Randima Fernando, pp. 621–636. Addison-Wesley.
Kipfer, Peter, Mark Segal, and Rüdiger Westermann. 2004. "UberFlow: A GPU-Based Particle Engine." In Proceedings of the SIGGRAPH/Eurographics Workshop on Graphics Hardware 2004, pp. 115–122.
Kolb, A., L. Latta, and C. Rezk-Salama. 2004. "Hardware-Based Simulation and Collision Detection for Large Particle Systems." In Proceedings of the SIGGRAPH/Eurographics Workshop on Graphics Hardware 2004, pp. 123–131.
Purcell, Tim, Craig Donner, Mike Cammarano, Henrik Wann Jensen, and Pat Hanrahan. 2003. "Photon Mapping on Programmable Graphics Hardware." In Proceedings of the SIGGRAPH/Eurographics Workshop on Graphics Hardware 2003, pp. 41–50.
Sedgewick, Robert. 1998. Algorithms in C++, 3rd ed., Parts 1–4. Addison-Wesley.
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Library of Congress Cataloging-in-Publication Data
GPU gems 2 : programming techniques for high-performance graphics and general-purpose
computation / edited by Matt Pharr ; Randima Fernando, series editor.
p. cm.
Includes bibliographical references and index.
ISBN 0-321-33559-7 (hardcover : alk. paper)
1. Computer graphics. 2. Real-time programming. I. Pharr, Matt. II. Fernando, Randima.
T385.G688 2005
006.66—dc22
2004030181
GeForce™ and NVIDIA Quadro® are trademarks or registered trademarks of NVIDIA Corporation.
Nalu, Timbury, and Clear Sailing images © 2004 NVIDIA Corporation.
mental images and mental ray are trademarks or registered trademarks of mental images, GmbH.
Copyright © 2005 by NVIDIA Corporation.
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Text printed in the United States on recycled paper at Quebecor World Taunton in Taunton, Massachusetts.
Second printing, April 2005
Dedication
To everyone striving to make today's best computer graphics look primitive tomorrow
- Copyright
- Inside Back Cover
- Inside Front Cover
- Part I: Geometric Complexity
- Chapter 1. Toward Photorealism in Virtual Botany
- Chapter 2. Terrain Rendering Using GPU-Based Geometry Clipmaps
- Chapter 3. Inside Geometry Instancing
- Chapter 4. Segment Buffering
- Chapter 5. Optimizing Resource Management with Multistreaming
- Chapter 6. Hardware Occlusion Queries Made Useful
- Chapter 7. Adaptive Tessellation of Subdivision Surfaces with Displacement Mapping
- Chapter 8. Per-Pixel Displacement Mapping with Distance Functions
- Part II: Shading, Lighting, and Shadows
- Chapter 10. Real-Time Computation of Dynamic Irradiance Environment Maps
- Chapter 11. Approximate Bidirectional Texture Functions
- Chapter 12. Tile-Based Texture Mapping
- Chapter 13. Implementing the mental images Phenomena Renderer on the GPU
- Chapter 14. Dynamic Ambient Occlusion and Indirect Lighting
- Chapter 15. Blueprint Rendering and "Sketchy Drawings"
- Chapter 16. Accurate Atmospheric Scattering
- Chapter 17. Efficient Soft-Edged Shadows Using Pixel Shader Branching
- Chapter 18. Using Vertex Texture Displacement for Realistic Water Rendering
- Chapter 19. Generic Refraction Simulation
- Chapter 9. Deferred Shading in S.T.A.L.K.E.R.
- Part III: High-Quality Rendering
- Chapter 20. Fast Third-Order Texture Filtering
- Chapter 21. High-Quality Antialiased Rasterization
- Chapter 22. Fast Prefiltered Lines
- Chapter 23. Hair Animation and Rendering in the Nalu Demo
- Chapter 24. Using Lookup Tables to Accelerate Color Transformations
- Chapter 25. GPU Image Processing in Apple's Motion
- Chapter 26. Implementing Improved Perlin Noise
- Chapter 27. Advanced High-Quality Filtering
- Chapter 28. Mipmap-Level Measurement
- Part IV: General-Purpose Computation on GPUS: A Primer
- Chapter 29. Streaming Architectures and Technology Trends
- Chapter 30. The GeForce 6 Series GPU Architecture
- Chapter 31. Mapping Computational Concepts to GPUs
- Chapter 32. Taking the Plunge into GPU Computing
- Chapter 33. Implementing Efficient Parallel Data Structures on GPUs
- Chapter 34. GPU Flow-Control Idioms
- Chapter 35. GPU Program Optimization
- Chapter 36. Stream Reduction Operations for GPGPU Applications
- Part V: Image-Oriented Computing
- Chapter 37. Octree Textures on the GPU
- Chapter 38. High-Quality Global Illumination Rendering Using Rasterization
- Chapter 39. Global Illumination Using Progressive Refinement Radiosity
- Chapter 40. Computer Vision on the GPU
- Chapter 41. Deferred Filtering: Rendering from Difficult Data Formats
- Chapter 42. Conservative Rasterization
- Part VI: Simulation and Numerical Algorithms
- Chapter 43. GPU Computing for Protein Structure Prediction
- Chapter 44. A GPU Framework for Solving Systems of Linear Equations
- Chapter 45. Options Pricing on the GPU
- Chapter 46. Improved GPU Sorting
- Chapter 47. Flow Simulation with Complex Boundaries
- Chapter 48. Medical Image Reconstruction with the FFT