Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and scales applications. An iDSL includes domain-specific language constructs, a compilation toolchain, and a runtime providing task scheduling, data placement, and workload balancing across and within heterogeneous nodes. In this work, we focus on the runtime framework. We introduce a novel design and extension of a runtime framework, the Parallel Runtime Environment for Multicore Applications. In response to the ever-increasing intra/inter-node concurrency, the runtime system supports efficient task scheduling and workload balancing at both levels while allowing the development of custom policies. Moreover, the new framework provides abstractions supporting the utilization of heterogeneous distributed nodes consisting of CPUs and GPUs and is extensible to other devices. We demonstrate that by utilizing this work, an application (or the iDSL) can scale its performance on heterogeneous exascale-era supercomputers with minimal effort. A future goal for this framework (out of the scope of this thesis) is to be integrated with machine learning to improve its decision-making and performance further. As a bridge to this goal, since the framework is under development, we experiment with data from Nuclear Physics Particle Accelerators and demonstrate the significant improvements achieved by utilizing machine learning in the hit-based track reconstruction process

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets

Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Real-Time Noise Removal in Foveated Path Tracing

Path tracing is a method for rendering photorealistic two-dimensional images of three-dimensional scenes based on computing intersection between the scene geometry and light rays traveling through the scene. The rise in parallel computation resources in devices such as graphics processing units (GPUs) have made it more and more viable to do path tracing in real time. To achieve real-time performance, path tracing can be further optimized by using foveated rendering, where the properties of the human visual system are exploited to reduce the number of rays outside the central point of vision (fovea), where the human eye cannot discern fine detail. The reduction in the number of rays can, however, lead to several issues. Noise appears in the image as a result of an inadequate number of path tracing samples allocated to each pixel. Furthermore, the variation in the noise from one animation frame to the next appears as flicker. Finally, artifacts can appear when the spatially subsampled image is upsampled to a uniform resolution for display. In this thesis, solutions to the aforementioned issues are explored by implementing three noise removal methods into a foveated path tracing rendering system. The computational performance and the visual quality of the implemented methods is evaluated. Of the implemented methods, cross-bilateral filter provides the best quality, but its runtime doesn't scale well to large filter sizes. Large filter sizes are enabled by the À-Trous approximation of the cross-bilateral filter, at the cost of generating more artifacts in the result. Overall, while the implemented methods are able to provide visually pleasing results in some scenarios, improvements in the algorithms (e.g., local filter parameter selection) are needed to reach the quality seen in offline methods

Radial Basis Functions: Biomedical Applications and Parallelization

Radial basis function (RBF) is a real-valued function whose values depend only on the distances between an interpolation point and a set of user-specified points called centers. RBF interpolation is one of the primary methods to reconstruct functions from multi-dimensional scattered data. Its abilities to generalize arbitrary space dimensions and to provide spectral accuracy have made it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc. The present thesis discusses three applications of RBF interpolation in biomedical engineering areas: (1) Calcium dynamics modeling, in which we numerically solve a set of PDEs by using meshless numerical methods and RBF-based interpolation techniques; (2) Image restoration and transformation, where an image is restored from its triangular mesh representation or transformed under translation, rotation, and scaling, etc. from its original form; (3) Porous structure design, in which the RBF interpolation used to reconstruct a 3D volume containing porous structures from a set of regularly or randomly placed points inside a user-provided surface shape. All these three applications have been investigated and their effectiveness has been supported with numerous experimental results. In particular, we innovatively utilize anisotropic distance metrics to define the distance in RBF interpolation and apply them to the aforementioned second and third applications, which show significant improvement in preserving image features or capturing connected porous structures over the isotropic distance-based RBF method. Beside the algorithm designs and their applications in biomedical areas, we also explore several common parallelization techniques (including OpenMP and CUDA-based GPU programming) to accelerate the performance of the present algorithms. In particular, we analyze how parallel programming can help RBF interpolation to speed up the meshless PDE solver as well as image processing. While RBF has been widely used in various science and engineering fields, the current thesis is expected to trigger some more interest from computational scientists or students into this fast-growing area and specifically apply these techniques to biomedical problems such as the ones investigated in the present work

Fortgeschrittene Entrauschungs-Verfahren und speicherlose Beschleunigungstechniken für realistische Bildsynthese

Stochastic ray tracing methods have become the industry's standard for today's realistic image synthesis thanks to their ability to achieve a supreme degree of realism by physically simulating various natural phenomena of light and cameras (e.g. global illumination, depth-of-field, or motion blur). Unfortunately, high computational cost for more complex scenes and image noise from insufficient simulations are major issues of these methods and, hence, acceleration and denoising are key components in stochastic ray tracing systems. In this thesis, we introduce two new filtering methods for advanced lighting and camera effects, as well as a novel approach for memoryless acceleration. In particular, we present an interactive filter for global illumination in the presence of depth-of-field, and a general and robust adaptive reconstruction framework for high-quality images with a wide range of rendering effects. To address complex scene geometry, we propose a novel concept which models the acceleration structure completely implicit, i.e. without any additional memory cost at all, while still allowing for interactive performance. Our contributions advance the state-of-the-art of denoising techniques for realistic image synthesis as well as the field of memoryless acceleration for ray tracing systems.Stochastische Ray-Tracing Methoden sind heutzutage der Industriestandard für realistische Bildsynthese, da sie einen hohen Grad an Realismus erzeugen können, indem sie verschiedene natürliche Phänomene (z.B. globale Beleuchtung, Tiefenunschärfe oder Bewegungsunschärfe) physikalisch korrekt simulieren. Offene Probleme dieser Verfahren sind hohe Rechenzeit für komplexere Szenen sowie Bildrauschen durch unzulängliche Simulationen. Demzufolge sind Beschleunigungstechniken und Entrauschungsverfahren essentielle Komponenten in stochastischen Ray-Tracing-Systemen. In dieser Arbeit stellen wir zwei neue Filter-Methoden für erweiterte Beleuchungs- und Kamera-Effekte sowie ein neuartiges Verfahren für eine speicherlose Beschleunigungsstruktur vor. Im Detail präsentieren wir einen interaktiven Filter für globale Beleuchtung in Kombination mit Tiefenunschärfe und einen generischen, robusten Ansatz für die adaptive Rekonstruktion von hoch-qualitativen Bildern mit einer großen Auswahl an Rendering-Effekten. Für das Problem hoher geometrischer Szenen-Komplexität demonstrieren wir ein neuartiges Konzept für die implizierte Modellierung der Beschleunigungsstruktur, welches keinen zusätzlichen Speicher verbraucht, aber weiterhin interaktive Laufzeiten ermöglicht. Unsere Beiträge verbessern sowohl den aktuellen Stand von Entrauschungs-Verfahren in der realistischen Bildsynthese als auch das Feld der speicherlosen Beschleunigungsstrukturen für Ray-Tracing-Systeme