Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho

Interactive Visualization on High-Resolution Tiled Display Walls with Network Accessible Compute- and Display-Resources

Papers number 2-7 and appendix B and C of this thesis are not available in Munin: 2. Hagen, T-M.S., Johnsen, E.S., Stødle, D., Bjorndalen, J.M. and Anshus, O.: 'Liberating the Desktop', First International Conference on Advances in Computer-Human Interaction (2008), pp 89-94. Available at http://dx.doi.org/10.1109/ACHI.2008.20 3. Tor-Magne Stien Hagen, Oleg Jakobsen, Phuong Hoai Ha, and Otto J. Anshus: 'Comparing the Performance of Multiple Single-Cores versus a Single Multi-Core' (manuscript)4. Tor-Magne Stien Hagen, Phuong Hoai Ha, and Otto J. Anshus: 'Experimental Fault-Tolerant Synchronization for Reliable Computation on Graphics Processors' (manuscript) 5. Tor-Magne Stien Hagen, Daniel Stødle and Otto J. Anshus: 'On-Demand High-Performance Visualization of Spatial Data on High-Resolution Tiled Display Walls', Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications (2010), pages 112-119. Available at http://dx.doi.org/10.5220/0002849601120119 6. Bård Fjukstad, Tor-Magne Stien Hagen, Daniel Stødle, Phuong Hoai Ha, John Markus Bjørndalen and Otto Anshus: 'Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes', Para 2010 – State of the Art in Scientific and Parallel Computing. Available at http://vefir.hi.is/para10/extab/para10-paper-60 7. Tor-Magne Stien Hagen, Daniel Stødle, John Markus Bjørndalen, and Otto Anshus: 'A Step towards Making Local and Remote Desktop Applications Interoperable with High-Resolution Tiled Display Walls', Lecture Notes in Computer Science (2011), Volume 6723/2011, 194-207. Available at http://dx.doi.org/10.1007/978-3-642-21387-8_15The vast volume of scientific data produced today requires tools that can enable scientists to explore large amounts of data to extract meaningful information. One such tool is interactive visualization. The amount of data that can be simultaneously visualized on a computer display is proportional to the display’s resolution. While computer systems in general have seen a remarkable increase in performance the last decades, display resolution has not evolved at the same rate. Increased resolution can be provided by tiling several displays in a grid. A system comprised of multiple displays tiled in such a grid is referred to as a display wall. Display walls provide orders of magnitude more resolution than typical desktop displays, and can provide insight into problems not possible to visualize on desktop displays. However, their distributed and parallel architecture creates several challenges for designing systems that can support interactive visualization. One challenge is compatibility issues with existing software designed for personal desktop computers. Another set of challenges include identifying characteristics of visualization systems that can: (i) Maintain synchronous state and display-output when executed over multiple display nodes; (ii) scale to multiple display nodes without being limited by shared interconnect bottlenecks; (iii) utilize additional computational resources such as desktop computers, clusters and supercomputers for workload distribution; and (iv) use data from local and remote compute- and data-resources with interactive performance. This dissertation presents Network Accessible Compute (NAC) resources and Network Accessible Display (NAD) resources for interactive visualization of data on displays ranging from laptops to high-resolution tiled display walls. A NAD is a display having functionality that enables usage over a network connection. A NAC is a computational resource that can produce content for network accessible displays. A system consisting of NACs and NADs is either push-based (NACs provide NADs with content) or pull-based (NADs request content from NACs). To attack the compatibility challenge, a push-based system was developed. The system enables several simultaneous users to mirror multiple regions from the desktop of their computers (NACs) onto nearby NADs (among others a 22 megapixel display wall) without requiring usage of separate DVI/VGA cables, permanent installation of third party software or opening firewall ports. The system has lower performance than that of a DVI/VGA cable approach, but increases flexibility such as the possibility to share network accessible displays from multiple computers. At a resolution of 800 by 600 pixels, the system can mirror dynamic content between a NAC and a NAD at 38.6 frames per second (FPS). At 1600x1200 pixels, the refresh rate is 12.85 FPS. The bottleneck of the system is frame buffer capturing and encoding/decoding of pixels. These two functional parts are executed in sequence, limiting the usage of additional CPU cores. By pipelining and executing these parts on separate CPU cores, higher frame rates can be expected and by a factor of two in the best case. To attack all presented challenges, a pull-based system, WallScope, was developed. WallScope enables interactive visualization of local and remote data sets on high-resolution tiled display walls. The WallScope architecture comprises a compute-side and a display-side. The compute-side comprises a set of static and dynamic NACs. Static NACs are considered permanent to the system once added. This type of NAC typically has strict underlying security and access policies. Examples of such NACs are clusters, grids and supercomputers. Dynamic NACs are compute resources that can register on-the-fly to become compute nodes in the system. Examples of this type of NAC are laptops and desktop computers. The display-side comprises of a set of NADs and a data set containing data customized for the particular application domain of the NADs. NADs are based on a sort-first rendering approach where a visualization client is executed on each display-node. The state of these visualization clients is provided by a separate state server, enabling central control of load and refresh-rate. Based on the state received from the state server, the visualization clients request content from the data set. The data set is live in that it translates these requests into compute messages and forwards them to available NACs. Results of the computations are returned to the NADs for the final rendering. The live data set is close to the NADs, both in terms of bandwidth and latency, to enable interactive visualization. WallScope can visualize the Earth, gigapixel images, and other data available through the live data set. When visualizing the Earth on a 28-node display wall by combining the Blue Marble data set with the Landsat data set using a set of static NACs, the bottleneck of WallScope is the computation involved in combining the data sets. However, the time used to combine data sets on the NACs decreases by a factor of 23 when going from 1 to 26 compute nodes. The display-side can decode 414.2 megapixels of images per second (19 frames per second) when visualizing the Earth. The decoding process is multi-threaded and higher frame rates are expected using multi-core CPUs. WallScope can rasterize a 350-page PDF document into 550 megapixels of image-tiles and display these image-tiles on a 28-node display wall in 74.66 seconds (PNG) and 20.66 seconds (JPG) using a single quad-core desktop computer as a dynamic NAC. This time is reduced to 4.20 seconds (PNG) and 2.40 seconds (JPG) using 28 quad-core NACs. This shows that the application output from personal desktop computers can be decoupled from the resolution of the local desktop and display for usage on high-resolution tiled display walls. It also shows that the performance can be increased by adding computational resources giving a resulting speedup of 17.77 (PNG) and 8.59 (JPG) using 28 compute nodes. Three principles are formulated based on the concepts and systems researched and developed: (i) Establishing the end-to-end principle through customization, is a principle stating that the setup and interaction between a display-side and a compute-side in a visualization context can be performed by customizing one or both sides; (ii) Personal Computer (PC) – Personal Compute Resource (PCR) duality states that a user’s computer is both a PC and a PCR, implying that desktop applications can be utilized locally using attached interaction devices and display(s), or remotely by other visualization systems for domain specific production of data based on a user’s personal desktop install; and (iii) domain specific best-effort synchronization stating that for distributed visualization systems running on tiled display walls, state handling can be performed using a best-effort synchronization approach, where visualization clients eventually will get the correct state after a given period of time. Compared to state-of-the-art systems presented in the literature, the contributions of this dissertation enable utilization of a broader range of compute resources from a display wall, while at the same time providing better control over where to provide functionality and where to distribute workload between compute-nodes and display-nodes in a visualization context

Scalable Real-Time Rendering for Extremely Complex 3D Environments Using Multiple GPUs

In 3D visualization, real-time rendering of high-quality meshes in complex 3D environments is still one of the major challenges in computer graphics. New data acquisition techniques like 3D modeling and scanning have drastically increased the requirement for more complex models and the demand for higher display resolutions in recent years. Most of the existing acceleration techniques using a single GPU for rendering suffer from the limited GPU memory budget, the time-consuming sequential executions, and the finite display resolution. Recently, people have started building commodity workstations with multiple GPUs and multiple displays. As a result, more GPU memory is available across a distributed cluster of GPUs, more computational power is provided throughout the combination of multiple GPUs, and a higher display resolution can be achieved by connecting each GPU to a display monitor (resulting in a tiled large display configuration). However, using a multi-GPU workstation may not always give the desired rendering performance due to the imbalanced rendering workloads among GPUs and overheads caused by inter-GPU communication. In this dissertation, I contribute a multi-GPU multi-display parallel rendering approach for complex 3D environments. The approach has the capability to support a high-performance and high-quality rendering of static and dynamic 3D environments. A novel parallel load balancing algorithm is developed based on a screen partitioning strategy to dynamically balance the number of vertices and triangles rendered by each GPU. The overhead of inter-GPU communication is minimized by transferring only a small amount of image pixels rather than chunks of 3D primitives with a novel frame exchanging algorithm. The state-of-the-art parallel mesh simplification and GPU out-of-core techniques are integrated into the multi-GPU multi-display system to accelerate the rendering process

Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi