DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).Comment: LDAV 2018, October 201

Exploiting replicated data for communication load balancing in image-space parallel direct volume rendering of unstructured grids

Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent Univ., 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 89-94.The focus of this work is on parallel volume rendering applications in which renderings with different parameters are successively repeated over the same dataset. The only reason for inter-task interaction is the existence of data primitives that are inputs to several tasks. Both computational structure and expected task execution times may change during successive rendering instances. Change in computational structure means change in the data primitive requirements of tasks. Since the individual processors of a parallel system have a limited storage capacity, we can reserve a limited amount of storage for holding replicas at each processor. For the parallelization of a particular rendering instance, the remapping model should utilize the replication pattern of the previous rendering instance(s) for reducing the communication overhead due to the data replication requirement of the current rendering instance. We propose a two-phase model for solving this problem. The hypergraphpartitioning-based model proposed for the first phase aims to minimize the total message volume that will be incurred due to the replication/migration of input data while maintaining balance on computational and receive-volume loads of processors. The network-flow-based model proposed for the second phase aims to minimize the maximum message volume handled by processors via utilizing the flexibility in assigning send-communication tasks to processors, which is introduced by data replication. The validity of our proposed model is verified on image-space parallelization of a direct volume rendering algorithm.Okuyan, ErkanM.S

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Smoke and Shadows: Rendering and Light Interaction of Smoke in Real-Time Rendered Virtual Environments

Realism in computer graphics depends upon digitally representing what we see in the world with careful attention to detail, which usually requires a high degree of complexity in modelling the scene. The inevitable trade-off between realism and performance means that new techniques that aim to improve the visual fidelity of a scene must do so without compromising the real-time rendering performance. We describe and discuss a simple method for realistically casting shadows from an opaque solid object through a GPU (graphics processing unit) based particle system representing natural phenomena, such as smoke

Sketchy rendering for information visualization

We present and evaluate a framework for constructing sketchy style information visualizations that mimic data graphics drawn by hand. We provide an alternative renderer for the Processing graphics environment that redefines core drawing primitives including line, polygon and ellipse rendering. These primitives allow higher-level graphical features such as bar charts, line charts, treemaps and node-link diagrams to be drawn in a sketchy style with a specified degree of sketchiness. The framework is designed to be easily integrated into existing visualization implementations with minimal programming modification or design effort. We show examples of use for statistical graphics, conveying spatial imprecision and for enhancing aesthetic and narrative qualities of visual- ization. We evaluate user perception of sketchiness of areal features through a series of stimulus-response tests in order to assess users’ ability to place sketchiness on a ratio scale, and to estimate area. Results suggest relative area judgment is compromised by sketchy rendering and that its influence is dependent on the shape being rendered. They show that degree of sketchiness may be judged on an ordinal scale but that its judgement varies strongly between individuals. We evaluate higher-level impacts of sketchiness through user testing of scenarios that encourage user engagement with data visualization and willingness to critique visualization de- sign. Results suggest that where a visualization is clearly sketchy, engagement may be increased and that attitudes to participating in visualization annotation are more positive. The results of our work have implications for effective information visualization design that go beyond the traditional role of sketching as a tool for prototyping or its use for an indication of general uncertainty

Master slave en-face OCT/SLO

Master Slave optical coherence tomography (MS-OCT) is an OCT method that does not require resampling of data and can be used to deliver en-face images from several depths simultaneously. As the MS-OCT method requires important computational resources, the number of multiple depth en-face images that can be produced in real-time is limited. Here, we demonstrate progress in taking advantage of the parallel processing feature of the MS-OCT technology. Harnessing the capabilities of graphics processing units (GPU)s, information from 384 depth positions is acquired in one raster with real time display of up to 40 en-face OCT images. These exhibit comparable resolution and sensitivity to the images produced using the conventional Fourier domain based method. The GPU facilitates versatile real time selection of parameters, such as the depth positions of the 40 images out of the set of 384 depth locations, as well as their axial resolution. In each updated displayed frame, in parallel with the 40 en-face OCT images, a scanning laser ophthalmoscopy (SLO) lookalike image is presented together with two B-scan OCT images oriented along rectangular directions. The thickness of the SLO lookalike image is dynamically determined by the choice of number of en-face OCT images displayed in the frame and the choice of differential axial distance between them

Procedural function-based modelling of volumetric microstructures

We propose a new approach to modelling heterogeneous objects containing internal volumetric structures with size of details orders of magnitude smaller than the overall size of the object. The proposed function-based procedural representation provides compact, precise, and arbitrarily parameterised models of coherent microstructures, which can undergo blending, deformations, and other geometric operations, and can be directly rendered and fabricated without generating any auxiliary representations (such as polygonal meshes and voxel arrays). In particular, modelling of regular lattices and cellular microstructures as well as irregular porous media is discussed and illustrated. We also present a method to estimate parameters of the given model by fitting it to microstructure data obtained with magnetic resonance imaging and other measurements of natural and artificial objects. Examples of rendering and digital fabrication of microstructure models are presented