A graphics processing system includes a graphics processor and a memory for storing data to be used by and generated by the graphics processor. In a first rendering pass, the graphics processor generates an array of graphics data and stores the generated array of graphics data in the memory. The array of graphics data generated in the first rendering pass is used in a subsequent rendering pass. In the first rendering pass, the graphics processor determines one or more regions of the array of graphics data that have a particular characteristic, and generates information indicative of the one or more regions. In the subsequent rendering pass, the graphics processor uses the information indicative of the one or more regions to control the reading of the array of graphics data when it is to be used in the subsequent rendering pass.

Graphics processing systems

Organized flight in birds

This poster presents efficient strategies for sorting large sequences of fixed-length keys (and values) using GPGPU stream processors. Compared to the state-of-the-art, our radix sorting methods exhibit speedup of at least 2x for all generations of NVIDIA GPGPUs, and up to 3.7x for current GT200-based models. Our implementations demonstrate sorting rates of 482 million key-value pairs per second, and 550 million keys per second (32-bit). For this domain of sorting problems, we believe our sorting primitive to be the fastest available for any fully-programmable microarchitecture. These results motivate a different breed of parallel primitives for GPGPU stream architectures that can better exploit the memory and computational resources while maintaining the flexibility of a reusable component. Our sorting performance is derived from a parallel scan stream primitive that has been generalized in two ways: (1) with local interfaces for producer/consumer operations (visiting logic), and (2) with interfaces for performing multiple related, concurrent prefix scans (multi-scan).

Revisiting sorting for GPGPU stream architectures

The need to rank and order data is pervasive, and many algorithms are fundamentally dependent upon sorting and partitioning operations. Prior to this work, GPU stream processors have been perceived as challenging targets for problems with dynamic and global data-dependences such as sorting. This paper presents: (1) a family of very efficient parallel algorithms for radix sorting; and (2) our allocation-oriented algorithmic design strategies that match the strengths of GPU processor architecture to this genre of dynamic parallelism. We demonstrate multiple factors of speedup (up to 3.8x) compared to state-of-the-art GPU sorting. We also reverse the performance differentials observed between GPU and multi/many-core CPU architectures by recent comparisons in the literature, including those with 32-core CPU-based accelerators. Our average sorting rates exceed 1B 32-bit keys/sec on a single GPU microprocessor. Our sorting passes are constructed from a very efficient parallel prefix scan "runtime" that incorporates three design features: (1) kernel fusion for locally generating and consuming prefix scan data; (2) multi-scan for performing multiple related, concurrent prefix scans (one for each partitioning bin); and (3) flexible algorithm serialization for avoiding unnecessary synchronization and communication within algorithmic phases, allowing us to construct a single implementation that scales well across all generations and configurations of programmable NVIDIA GPUs.

High performance and scalable radix sorting: a case study of implementing dynamic parallelism for gpu computing

We propose a new technique to simulate dynamic patterns of crowd behaviors using stress modeling. Our model accounts for permanent, stable disposition and the dynamic nature of human behaviors that change in response to the situation. The resulting approach accounts for changes in behavior in response to external stressors based on well-known theories in psychology. We combine this model with recent techniques on personality modeling for multi-agent simulations to capture a wide variety of behavioral changes and stressors. The overall formulation allows different stressors, expressed as functions of space and time, including time pressure, positional stressors, area stressors and inter-personal stressors. This model can be used to simulate dynamic crowd behaviors at interactive rates, including walking at variable speeds, breaking lane-formation over time, and cutting through a normal flow. We also perform qualitative and quantitative comparisons between our simulation results and real-world observations.

/pdf/interactive-simulation-of-dynamic-crowd-behaviors-using-5e0hz6vyyw.pdf

Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory

Artificial intelligence (AI) is generally considered to be one of the key components of a computer game. Sometimes when we play a game, we may wish that the computer opponents were written better. At those times while playing against the computer, we feel that the game is unbalanced. Perhaps the computer player has been given different set of rules, or uses the same rules, but has more resources (health, weapons, etc.). The complexity of underlying AI systems, along with game design, belies the resulting feeling we have when playing any game. As the CPU and GPU speed and power continues to grow, along with increasing memory amounts and bandwidth, game developers are constantly improving the graphics of their games. In the last five years the production quality of games has been increasing (along with the corresponding budgets). Recent games woo players with incredible breakthroughs in real- time 3D graphics, complexity of the worlds and characters, as well as various post-processing effects. And while there had been tremendous improvements for parallelizing rendering through the evolution of consumer GPU pipelines, artificial intelligence computations are treading behind. To date, there had been rather few attempts at parallelizing AI computations.

March of the Froblins: simulation and rendering massive crowds of intelligent and detailed creatures on GPU

A method and apparatus for sorting data into spatial bins or buckets using a graphics processing unit (GPU). The method takes unsorted point data as input and scatters the points, in sorted order, into a set of bins. This key operation enables construction of a spatial data structure that is useful for applications such as particle simulation or collision detection. The disclosed method achieves better performance scaling than previous methods by exploiting geometry shaders to progressively trim the size of a working set. The method also leverages predicated rendering functionality to allow early termination without CPU/GPU synchronization. Furthermore, unlike previous techniques, the method can guarantee sorted output without requiring sorted input. This allows the method to be used to implement a form of bucket sort using the GPU.

Method and apparatus for spatial binning on a GPU and global path planning to avoid spatially binned objects

A method carried out by graphics processing circuitry includes generating animated coarse mesh vertex information based on instanced coarse mesh data; and tessellating instanced coarse mesh data based on the animated coarse mesh vertex information to produce instances of a three dimensional object for display. A graphics processing circuitry includes programmable shader logic operative to execute programmable instructions that when executed cause the programmable shader logic to generate animated coarse mesh vertex information based on instanced coarse mesh data; and tessellate instanced coarse mesh data based on the animated coarse mesh vertex information to produce instances of a three dimensional object for display. Another method carried out by graphics processing circuitry includes determining density of a plurality of three dimensional objects in a current view on a display; setting a tessellation level based on said density; and tessellating said plurality of three dimensional objects.

Rendering Detailed Animated Three Dimensional Characters with Coarse Mesh Instancing and Determining Tesselation Levels for Varying Character Crowd Density

A method and apparatus for rendering instance geometry whereby all culling, level of detail (LOD) and scene management is performed directly on a GPU.

Method and apparatus for rendering instance geometry

We present a new technique for sorting data into spatial bins or buckets using a graphics processing unit (GPU). Our method takes unsorted point data as input and scatters the points, in sorted order, into a set of bins. This is a key operation in the construction of spatial data structures, which are essential for applications such as particle simulation or collision detection. Our technique achieves better performance scaling than previous methods by exploiting geometry shaders to progressively trim the size of the working set. We also leverage predicated rendering functionality to allow early termination without CPU/GPU synchronization. Furthermore, unlike previous techniques, our method can guarantee sorted output without requiring sorted input. This allows our method to be used to implement a form of bucket sort using the GPU. CR Categories: I.3.6 [Computer Graphics]: Methodology and Techniques—Graphics data structures and data types; K.3.5 [Computer Graphics]: Computational Geometry and Object Modeling— Physically based modeling; I.3.7 [Computer Graphics]: ThreeDimensional Graphics and Realism—Animation

http://sa08.idav.ucdavis.edu/oat-binning.pdf

Joshua D. Barczak

Papers

March of the Froblins: simulation and rendering massive crowds of intelligent and detailed creatures on GPU

Method and apparatus for spatial binning on a GPU and global path planning to avoid spatially binned objects

Rendering Detailed Animated Three Dimensional Characters with Coarse Mesh Instancing and Determining Tesselation Levels for Varying Character Crowd Density

Method and apparatus for rendering instance geometry

Efficient Spatial Binning on the GPU