A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

Merging Cellular Automata for Simulating Surface Effects

International audienceThis paper describes a model of three-dimensional cellular automata allowing to simulate different phenomena in the fields of com- puter graphics and image processing, and to combine them together in order to produce complex effects such as automatic multitexturing, sur- face imperfections, or biological retina multi-layer cellular behaviours. Our cellular automaton model is defined as a network of connected cells arranged in a natural and dynamic way, which affords multi-behavior ca- pabilities. Based on cheap and widespread computing systems, real-time performance can be reached for simulations involving up to a hundred thousand cells. Our approach efficiency is illustrated through a set of CA related to computer graphics –e.g. erosion, sedimentation, or vegetal growing processes– and image analysis –e.g. pipeline retina simulation

High-throughput sequence alignment using Graphics Processing Units

<p>Abstract</p> <p>Background</p> <p>The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and <it>de novo </it>genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.</p> <p>Results</p> <p>This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies.</p> <p>Conclusion</p> <p>MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU.</p

Interactive deformation and visualization of level set surfaces using graphics hardware

technical reportDeformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization for applications such as segmentation, surface process- ing, and surface reconstruction. Their usefulness has been limited, however, by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters that can be difficult to tune correctly for specific applications. The second problem is compounded by the first. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates. Our efficient GPU- based solution relies on packing the level-set isosurface data into a dynamic, sparse texture format. As the level set moves, this sparse data structure is updated via a novel GPU to CPU message passing scheme. When the level-set solver is integrated with a real-time volume renderer operating on the same p

Experiencing Belugas: Action selection for an interactive aquarium exhibit

This paper presents a case study of an action selection system designed with adaptive techniques to create a virtual beluga aquarium exhibit. The beluga interactive exhibit uses a realistic 3D simulation system that allows the virtual belugas, in a natural pod context, to learn and alter their behavior based on contextual visitor interaction. Ethogram information on beluga behavior was incorporated into the simulation, which uses physically based systems for natural whale locomotion and water, artificial intelligence systems including modified neural networks and a reactive hierarchical action selection mechanism to simulate real-time natural individual beluga and group behavior. The beluga’s behavioral system consists of two layers: a low-level navigation system and a high-level reaction hierarchical action selection system. The system is designed to be run on consumer level hardware while maintaining real-time speeds

Real-time 3d person tracking and dense stereo maps using GPU acceleration

Interfacing with a computer, especially when interacting with a virtual three di- mensional (3D) scene, found in video games for example, can be frustrating when using only a mouse and keyboard. Recent work has been focused on alternative modes of interactions, including 3D tracking of the human body. One of the essential steps in this process is acquiring depth information of the scene. Stereo vision is the process of using two separate images of the same scene, taken from slightly different positions, to get a three dimensional view of the scene. One of the largest issues with dense stereo map generation is the high processor usage, usually preventing this process from being done in real time. In order to solve this problem, this project attempts to move the bulk of the processing to the GPU. The depth map extraction is done by matching points between the images, and using the difference in their positions to determine the depth, using multiple passes in a series of openGL vertex and fragment shaders. Once a depth map has been created, the software uses it to track a person’s movement and pose in three dimensions, by tracking key points on the person across frames, and using the depth map to find the third dimension

An investigation of the efficient implementation of Cellular Automata on multi-core CPU and GPU hardware

Copyright © 2015 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Parallel and Distributed Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Parallel and Distributed Computing Vol. 77 (2015), DOI: 10.1016/j.jpdc.2014.10.011Cellular automata (CA) have proven to be excellent tools for the simulation of a wide variety of phenomena in the natural world. They are ideal candidates for acceleration with modern general purpose-graphical processing units (GPU/GPGPU) hardware that consists of large numbers of small, tightly-coupled processors. In this study the potential for speeding up CA execution using multi-core CPUs and GPUs is investigated and the scalability of doing so with respect to standard CA parameters such as lattice and neighbourhood sizes, number of states and generations is determined. Additionally the impact of ‘Activity’ (the number of ‘alive’ cells) within a given CA simulation is investigated in terms of both varying the random initial distribution levels of ‘alive’ cells, and via the use of novel state transition rules; where a change in the dynamics of these rules (i.e. the number of states) allows for the investigation of the variable complexity within.Engineering and Physical Sciences Research Council (EPSRC