Fine‐Grained Memory Profiling of GPGPU Kernels

Memory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine‐grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state‐of‐the‐art memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications

Depth of Field Segmentation for Near-Lossless Image Compression and 3D Reconstruction

Over the years, photometric 3d reconstruction gained increasing importance in several disciplines, especially in cultural heritage preservation. While increasing sizes of images and datasets enhanced the overall reconstruction results, requirements in storage got immense. Additionally, unsharp areas in the background have a negative influence on 3d reconstructions algorithms. Handling the sharp foreground differently from the background simultaneously helps to reduce storage size requirements and improves 3d reconstruction results. In this paper, we examine regions outside the Depth of Field (DoF) and eliminate their inaccurate information to 3d reconstructions. We extract DoF maps from the images and use them to handle the foreground and background with different compression backends making sure that the actual object is compressed losslessly. Our algorithm achieves compression rates between 1:8 and 1:30 depending on the artifact and DoF size and improves the 3d reconstruction

Kompression und Visualisierung von großen und animierten Volumen-Daten

Volume visualization is the rendering of scalar fields in a way that it can be interpreted. The scalar field is given as a function of space. Visualizing these fields, three common problems are faced that will be discussed in this work. The first problem is to find a representation that allows for fast access and analysis of interesting parts within the volume. The second problem is to reduce the size of this representation since it can easily exceed the size of the main memory. The third problem is that for time dependent or animated volume data only very few or even a single time step can be held in main memory. Interactive visualization of large and animated volume data is, especially in the area of medical and physical applications, a very important problem. This problem has to be solved with special algorithms. While the data in medical applications is usually sampled on a regular grid, physical simulations use structured or unstructured grids to better adapt to the details within the volume. There have been a lot of publications on very good research for rendering volume data sampled on regular grids. The main focus of the research was to improve the image quality of the final renderings. Reducing the amount of data to be processed for each image still remains as a very important problem that has to be solved. There are two existing solutions to this problem. One is to use multi-resolution algorithms in order to process parts of the volume data at lower resolutions or not to process them at all. The other one is to use compression in order to reduce the size of the data set in the first place. Both approaches will be discussed and it will be investigated how to combine them efficiently. For rendering structured or unstructured grids an additional problem arises. In contrast to the fixed rendering order of regular grids, the visibility order of all cells has to be determined prior to rendering. This can also introduce new cells depending on whether the grid is convex or concave or if it contains visibility cycles. These additional cells have to be computed for each image since they depend on the location of the viewer. After the visibility order has been determined each cell is rendered individually. The rendering works similar to the rendering of regular grids in order to achieve the same high quality results. Finally, compression for structured and unstructured grids is considered since the amount of data can also be very high for this representation. A further application of rendering unstructured grids is displacement mapping. Here a simple height field over any kind of triangle mesh is rendered. To achieve a good image quality however, the previous rendering approaches need to be modified. These modifications include simplifications since the rendering is only interested in a single iso-surface but also new features since lighting and coloring are much more complex for displacement mapping.Die Volumenvisualisierung beschäftigt sich mit der Darstellung von skalaren Feldern, die als Funktion des Raumes gegeben sind, in einer Weise, dass sie vom Benutzer interpretiert werden können. Bei der Visualisierung dieser Felder gibt es drei zentrale Probleme, die alle in dieser Arbeit behandelt werden. Das erste Problem ist eine adäquate Darstellung des Volumens zu erhalten um alle interessanten Details so schnell wie möglich finden und analysieren zu können. Das zweite Problem ist die Repräsentation der Daten und damit auch die Datenmenge, die schnell die Größe des Arbeitsspeichers überschreiten kann. Das dritte Problem tritt bei zeitveränderlichen oder animierten Daten auf, da hier oftmals nur sehr wenige oder sogar nur ein einzelner Zeitschritt im Arbeitsspeicher gehalten warden kann. Die interaktive Visualisierung großer und animierter Volumen-Datensätze ist, vor allem im Bereich der medizinischen und physikalischen Anwendungen, ein sehr wichtiges Problem, das sich nur durch speziell angepasste Algorithmen lösen lässt. Während die Daten in der Medizin für gewöhnlich als reguläre Gitter gegeben sind, werden bei physikalischen Simulationen oft strukturierte oder unstrukturierte Gitter verwendet um sich besser an die Problemstellung anzupassen. Bei der Darstellung von Volumendaten auf regulären Gittern gab es in den vergangenen Jahren große Fortschritte, vor allem im Bereich der Bildqualität. Ein zentrales Problem bleibt allerdings weiterhin die große Datenmenge, die für jades einzelne Bild bearbeitet werden muss. Als Lösungsansätze bieten sich zum einen Mehrfachgitterverfahren an, die Teile des Datensatzes nicht oder nur in geringer Auflösung betrachten, und Kompressionsverfahren, die die Datenmenge an sich reduzieren. Auf beide Verfahren wird in dieser Arbeit eingegangen und es wird untersucht, wie sie sich effizient kombinieren lassen. Im Fall von strukturierten oder unstrukturierten Gittern kommt noch ein weiteres Problem bei der Darstellung hinzu. Im Gegensatz zu der festen Reihenfolge, in der die Daten zur Darstellung von regulären Gittern behandelt werden, muss hier vorab entschieden werden, in welcher Reihenfolge die Daten zu bearbeiten sind. Die kann, je nachdem, ob das Gitter konvex oder konkav ist, bzw. ob zyklische überdeckung zwischen Zellen besteht, zusätzliche Zellen zum Datensatz hinzu fügen, die für jedes Bild neu berechnet werden müssen, da sie von der Position des Betrachters abhängen. Nachdem die Sortierung der Zellen fest steht, muss nun jede Zelle einzeln dargestellt werden. Diese Darstellung kann dabei ähnlich wie bei regulären Gittern erfolgen, um eine gleich hohe Qualität zu gewährleisten. Ein weiterer Punkt bei strukturierten oder unstrukturierten Gittern ist die Kompression, da die Datenmengen hier auch sehr große Ausmaße annehmen können. Eine weitere Anwendung der Darstellung unstrukturierter Gitter ist das so genannte Displacement Mapping, bei dem ein Höhenfeld über einem beliebigen Dreiecksnetz dargestellt wird. Um eine gute Bildqualität zu erhalten, muss der bestehende Algorithmus allerdings an einigen Stellen modifiziert werden. Diese Modifikationen sind zum Teil Vereinfachungen, da man im allgemeinen nur an einer einzigen Iso-Fläche interessiert ist, aber auch neue Fähigkeiten, denn Beleuchtung und Farbgebung sind beim Displacement Mapping wesentlich komplexer

Interactive visualization of volumetric vector fields using texture based particles

This paper introduces a new approach to the visualization of volumetric vector fields with an adaptive distribution of animated particles that show properties of the underlying steady flow. The shape of the particles illustrates the direction of the vector field in a natural way. The particles are transported along streamlines and their velocity reflects the local magnitude of the vector field. Further physical quantities of the underlying flow can be mapped to the emissive color, the transparency and the length of the particles. A major effort has been made to achieve interactive frame rates for the animation of a large number of particles while minimizing the error of the computed streamlines. There are three main advantages of the new method. Firstly, the animation of the particles diminishes the inherent occlusion problem of volumetric vector field visualization, as the human eye can trace an animated particle even if it is highly occluded. The second advantage is the variable resolution of the visualization method. More particles are distributed in regions of interest. We present a method to automatically adjust the resolution to features of the vector field. Finally, our method is scalable to the computational and rasterization power of the visualization system by simply adjusting the number of visualized particles

Tetrahedral Mesh Compression with the Cut-Border Machine

In recent years, substantial progress has been achieved in the area of volume visualization on irregular grids, which is mainly based on tetrahedral meshes. Even moderately fine tetrahedral meshes consume several mega-bytes of storage. For archivation and transmission compression algorithms are essential. In scientific applications lossless compression schemes are of primary interest. This paper introduces a new lossless compression scheme for the connectivity of tetrahedral meshes. Our technique can handle all tetrahedral meshes in three dimensional euclidean space even with non manifold border. We present compression and decompression algorithms which consume for reasonable meshes linear time in the number of tetrahedra. The connectivity is compressed to less than 2.4 bits per tetrahedron for all measured meshes. Thus a tetrahedral mesh can almost be reduced to the vertex coordinates, which consume in a common representation about one quarter of the total storage space