TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes

We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware

The svgl toolkit: enabling fast rendering of rich 2D graphics

As more and more powerful graphical processors be- come available on mainstream computers, it becomes possible to investigate the design of visually rich and fast interactive applications. In this article, we present S VGL , a graphical toolkit that enables programmers and design- ers of interactive applications to benefit from this power. The toolkit is based on a scene graph which is translated into an optimized display graph. After describing the algorithms used to display the scene, we show that the toolkit is two to fifty times faster than similar toolkits

Ambient occlusion and shadows for molecular graphics

Computer based visualisations of molecules have been produced as early as the 1950s to aid researchers in their understanding of biomolecular structures. An important consideration for Molecular Graphics software is the ability to visualise the 3D structure of the molecule in a clear manner. Recent advancements in computer graphics have led to improved rendering capabilities of the visualisation tools. The capabilities of current shading languages allow the inclusion of advanced graphic effects such as ambient occlusion and shadows that greatly improve the comprehension of the 3D shapes of the molecules. This thesis focuses on finding improved solutions to the real time rendering of Molecular Graphics on modern day computers. The methods of calculating ambient occlusion and both hard and soft shadows are examined and implemented to give the user a more complete experience when navigating large molecular structures

Real Time 3-D Graphics Processing Hardware Design using Field-Programmable Gate Arrays.

Three dimensional graphics processing requires many complex algebraic and matrix based operations to be performed in real-time. In early stages of graphics processing, such tasks were delegated to a Central Processing Unit (CPU). Over time as more complex graphics rendering was demanded, CPU solutions became inadequate. To meet this demand, custom hardware solutions that take advantage of pipelining and massive parallelism become more preferable to CPU software based solutions. This fact has lead to the many custom hardware solutions that are available today. Since real time graphics processing requires extreme high performance, hardware solutions using Application Specific Integrated Circuits (ASICs) are the standard within the industry. While ASICs are a more than adequate solution for implementing high performance custom hardware, the design, implementation and testing of ASIC based designs are becoming cost prohibitive due to the massive up front verification effort needed as well as the cost of fixing design defects.Field Programmable Gate Arrays (FPGAs) provide an alternative to the ASIC design flow. More importantly, in recent years FPGA technology have begun to improve in performance to the point where ASIC and FPGA performance has become comparable. In addition, FPGAs address many of the issues of the ASIC design flow. The ability to reconfigure FPGAs reduces the upfront verification effort and allows design defects to be fixed easily. This thesis demonstrates that a 3-D graphics processor implementation on and FPGA is feasible by implementing both a two dimensional and three dimensional graphics processor prototype. By using a Xilinx Virtex 5 ML506 FPGA development kit a fully functional wireframe graphics rendering engine is implemented using VHDL and Xilinx's development tools. A VHDL testbench was designed to verify that the graphics engine works functionally. This is followed by synthesizing the design and real hardware and developing test applications to verify functionality and performance of the design. This thesis provides the ground work for push forward the use of FPGA technology in graphics processing applications

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial