NUMA-aware image compositing on multi-GPU platform

Sort-last parallel rendering is widely used. Recent GPU developments mean that a PC equipped with multiple GPUs is a viable alternative to a high-cost supercomputer: the Fermi architecture of a single GPU supports uniform virtual addressing, providing a foundation for non-uniform memory access (NUMA) on multi-GPU platforms. Such hardware changes require the user to reconsider the parallel rendering algorithms. In this paper, we thoroughly investigate the NUMA-aware image compositing problem, which is the key final stage in sort-last parallel rendering. Based on a proven radix-k strategy, we find one optimal compositing algorithm, which takes advantage of NUMA architecture on the multi-GPU platform. We quantitatively analyze different image compositing modes for practical image compositing, taking into account peer-to-peer communication costs between GPUs. Our experiments on various datasets show that our image compositing method is very fast, an image of a few megapixels can be composited in about 10 ms by eight GPUs.     

Parallel implementation of an adaptive and parameter-free N-body integrator

Previously, Pruett et al. (2003) [3] described an N-body integrator of arbitrarily high order M with an asymptotic operation count of O(M²N²). The algorithm's structure lends itself readily to data parallelization, which we document and demonstrate here in the integration of point-mass systems subject to Newtonian gravitation. High order is shown to benefit parallel efficiency. The resulting N-body integrator is robust, parameter-free, highly accurate, and adaptive in both time-step and order. Moreover, it exhibits linear speedup on distributed parallel processors, provided that each processor is assigned at least a handful of bodies.

Program summary

Program title: PNB.f90Catalogue identifier: AEIK_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIK_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 3052No. of bytes in distributed program, including test data, etc.: 68 600Distribution format: tar.gzProgramming language: Fortran 90 and OpenMPIComputer: All shared or distributed memory parallel processorsOperating system: Unix/LinuxHas the code been vectorized or parallelized?: The code has been parallelized but has not been explicitly vectorized.RAM: Dependent upon NClassification: 4.3, 4.12, 6.5Nature of problem: High accuracy numerical evaluation of trajectories of N point masses each subject to Newtonian gravitation.Solution method: Parallel and adaptive extrapolation in time via power series of arbitrary degree.Running time: 5.1 s for the demo program supplied with the package.     

A simple distributed mechanism for accounting system self-configuration in next-generation charging and billing

Modern communication systems are becoming increasingly dynamic and complex. In this article a novel mechanism for next generation charging and billing is presented that enables self-configurability for accounting systems consisting of heterogeneous components. The mechanism is required to be simple, effective, efficient, scalable and fault-tolerant. Based on simulation results it is shown that the proposed simple distributed mechanism is competitive with usual cost-based or random mechanisms under realistic assumptions and up to non-extreme workload situations as well as fulfilling the posed requirements.     

3D flow features visualization via fuzzy clustering

A key approach to visualizing a flow field is to emphasize regions with significant behavior. However, it is difficult to give concrete criteria for classifying feature regions. In this paper, we use a novel framework in which fuzzy sets are used to determine flow features: Fuzzy relationships assess structural properties of features. A fuzzy c-means-like clustering algorithm is used to evaluate the importance of each voxel. Our approach can be readily modified with new fuzzy relationships describing other features of interest to users. We use a multi-resolution approach which displays structural features in greater detail, and represents the background by coarse-grained information. Experiments on synthetic and real datasets show that our framework can highlight significant aspects of the whole flow while avoiding occlusion and clutter. Interactive performance is achieved via a GPU implementation.     

Sketch theory as a framework for knowledge management

In this paper, we give an overview of sketch theory as a knowledge management framework and discuss its strengths relative to logic, the semantic web and relational algebra. Sketch theory, for example, supports modularity among meta-data, instance data and uncertainty. It also provides a notion of constraint-preserving map. We explore Q-trees as a technique for inference with sketches and compare it to logical deduction. Ideas can be formulated in distinct ways even within a fixed formalism. We illustrate solution of this alignment problem using sketches and the notion of Morita equivalence of logical theories. Sketch theory provides rich notions of contextual view with which we compute illustrative examples. Finally, we outline a program for advancing sketch theory as a complement to other knowledge management technologies and discuss transformations between sketches and other models.     

Robust Statistical Pixel Estimation

Robust statistical methods are employed to reduce the noise in Monte Carlo ray tracing. Through the use of resampling, the sample mean distribution is determined for each pixel. Because this distribution is uni‐modal and normal for a large sample size, robust estimates converge to the true mean of the pixel values. Compared to existing methods, less additional storage is required at each pixel because the sample mean distribution can be distilled down to a compact size, and fewer computations are necessary because the robust estimation process is sampling independent and needs a small input size to compute pixel values. The robust statistical pixel estimators are not only resistant to impulse noise, but they also remove general noise from fat‐tailed distributions. A substantial speedup in rendering can therefore be achieved by reducing the number of samples required for a desired image quality. The effectiveness of the proposed approach is demonstrated for path tracing simulations.     

Intelligent Robot Vision Sensors in VLSI

Traditional approaches for solving real-world problems using computer vision have depended heavily on CCD cameras and workstations. As the computation power of workstations doubles every 1.5 years, they are now better able to handle the large amount of data presented by the cameras; yet real-time solutions for physical interaction with the real-world continues to be very hard, and relegated to large and expensive systems. Our approach attempts to solve this problem by using computational sensors and small/inexpensive embedded processors. The computational sensors are custom designed to reduce the amount of data collected, to extract only relevant information and to present this information to the simple processor, microcontrollers (Cs) or DSPs, in a format which reduces post-processing latency. Consequently, the post-processors are required to perform only high level computation on features rather than data. These systems are applied to problems such as target acquisition and tracking for image stabilization and autonomous data driven autonavigation for mobile robots. We present an example of a system that uses a pair of computational sensors and a C to solve a toy autonavigation problem.The computational sensors, however, have wide applications in many problems that require image preprocessing such as edge detection, motion detection, centroid localization and other spatiotemporal processing. This paper also presents a general-purpose computational sensor capable of extracting many visual information components at the focal plane.     

Grain-boundary structure in an austenitic stainless steel

This paper considers to what extent the grain-boundary structure in a commercial material may be understood in terms of modern structural theories of crystal boundaries. It is shown that in one particular state (partially recrystallized) 78% of the boundaries, examined by transmission electron microscopy, could be said to contain the type of structure predicted by the theoretical approach (i.e. intrinsic dislocation arrays).This paper goes on to examine not only the equilibrium component of the boundary structure (intrinsic dislocation arrays) but also looks at the perturbations created in this component by the presence of other, non-equilibrium components (such as topographical discontinuities, precipitates and extrinsic dislocations produced by the dissociation of run-in matrix dislocations).     

Are Fischer–Tropsch waxes good feedstocks for fluid catalytic cracking units?

The potential of a highly paraffinic Fischer–Tropsch synthesis (FTS) wax as a feedstock for Fluid Catalytic Cracking (FCC) has been evaluated. A once-through microriser reactor was used to mimic realistic FCC conditions. FTS waxes are indeed attractive feedstocks for FCC. As a result of its high paraffinicity an interesting spectrum of products can be obtained by tuning process conditions and catalyst formulations. A high gasoline fraction (70 wt%) with an apparent high-quality motor octane number can be obtained. Also a relatively good diesel is expected. Due to the absence of sulfur and nitrogen in the feed extremely clean transportation fuels can be obtained that are additionally low on aromatics. The combination of an equilibrium catalyst with a steam-deactivated ZSM-5 additive yields high amounts of propene (16 wt%), isobutene (7 wt%) and n-C₄-olefins (8 wt%).