High performance computing is a topic that has risen to the top in the era ofdigitalization, AI and automation. Therefore, the search for more cost and timeeffective ways to implement HPC work is always a subject extensively researched.One part of this is to have hardware that is capable to improve on these criteria. Different hardware usually have different code languages to implement theseworks though, cross-platform solution like Intel’s oneAPI framework is startingto gaining popularity.In this thesis, the capabilities of Intel’s oneAPI framework to implement andexecute HPC benchmarks on different hardware platforms will be discussed. Using the hardware available through Intel’s DevCloud services, Intel’s Xeon Gold6128, Intel’s UHD Graphics P630 and the Arria10 FPGA board were all chosento use for implementation. The benchmarks that were chosen to be used wereGEMM (General Matrix Multiplication) and BUDE (Bristol University DockingEngine). They were implemented using DPC++ (Data Parallel C++), Intel’s ownSYCL-based C++ extension. The benchmarks were also tried to be improved uponwith HPC speed-up methods like loop unrolling and some hardware manipulation.The performance for CPU and GPU were recorded and compared, as the FPGAimplementation could not be preformed because of technical difficulties. Theresults are good comparison to related work, but did not improve much uponthem. This because the hardware used is quite weak compared to industry standard. Though further research on the topic would be interesting, to compare aworking FPGA implementation to the other results and results from other studies. This implementation also probably has the biggest improvement potential,so to see how good one could make it would be interesting. Also, testing someother more complex benchmarks could be interesting.

This project is about finding ways to improve performance of a Computer-Aided-Design (CAD) application running in the web browser. With the new Web API WebGPU, it is now possible to use the GPU to accelerate calculations for CAD applications in the web. In this project, we tried to find if using the GPU could yield significant performance improvements and if they are worth implementing. Typical tasks for a CAD application are split and union, used for finding intersections and combining shapes in geometry, which we parallelized during this project. Our final implementation utilizes lazy evaluation and the HistoPyramid data structure, to compete with a state-of-the-art line-sweep based algorithm called Polygon Clipping. Although the Polygon Clipping intersection is still faster than our implementations in most cases, we found that WebGPU can still give significant performance boosts.

Background. Several methods have been used to simulate soft body deformation, such as mass-spring systems and position-based dynamics. This has been done using tetrahedral mesh models for preservation of shape and volume. In real-time applications however, there is a limitation to how high resolution the model can be, creating the need for optimizations. Objectives. To achieve better performance for high resolution models, tetrahedral deformation is used, making it possible for the tetrahedral mesh and triangle mesh to use different resolutions. In combination with this, the GPU is used to execute the simulation in parallel, improving performance further. Methods. For evaluation of performance and accuracy, an implementation was created to simulate soft body deformation using extended position-based dynamics and the Vulkan graphics API, with the option to use tetrahedral deformation. By experimentation, comparisons are made between using different resolutions on the tetrahedral mesh to the full resolution in terms of performance and accuracy. Results. The results show that performance and accuracy are altered when using tetrahedral deformation on lower resolution tetrahedral mesh. The performance is improved based on the decrease in workload, such as with higher base resolution models or multiple soft bodies. The accuracy is however not correlated to the reduction of resolution, but instead dependant on the rest shape of the model used. Conclusions. The implementation created demonstrates a new optimization that can be used to simulate soft body deformation in parallel on the GPU, with a smaller change in accuracy. Improvements exist in areas of usability, features and other optimizations that can be further explored in future research.

Graphics Processing Units (GPUs) are commonly used to accelerate massively parallel workloads across a wide range of applications from machine learning to cryptocurrency mining. The original application for GPUs, however, was to accelerate graphics rendering which remains popular today through video gaming and video rendering. While GPUs began as fixed function hardware with minimal programmability, modern GPUs have adopted a design with many programmable cores and supporting fixed function hardware for rasterization, texture sampling, and render output tasks. This balance enables GPUs to be used for general purpose computing and still remain adept at graphics rendering. Previous work at the Georgia Institute of Technology has been done to implement a general purpose GPU (GPGPU) in the open source RISC-V ISA. The implementation features many programmable cores and texture sampling support. However, creating a truly modern GPU based on the RISC-V ISA requires the addition of fixed function hardware units for rasterization and render output tasks in order to meet the demands of current graphics APIs such as OpenGL or Vulkan. This thesis discusses the work done by students at the Georgia Institute of Technology and California Polytechnic State University SLO to accelerate graphics rendering on RISC-V GPUs including the specific contributions made to implement and connect fixed function graphics hardware for the render output unit (ROP) to the programmable cores in a RISC-V GPU. This thesis also explores the performance and area cost of different hardware configurations within the implemented GPU.

Creation of visually-pleasing images has always been one of the main goals of computer graphics. Two important components are necessary to achieve this goal --- artists who design visual aspects of an image (such as materials or lighting) and sophisticated algorithms that render the image. Traditionally, rendering has been of greater interest to researchers, while the design part has always been deemed as secondary. This has led to many inefficiencies, as artists, in order to create a stunning image, are often forced to resort to the traditional, creativity-baring, pipelines consisting of repeated rendering and parameter tweaking. Our work shifts the attention away from the rendering problem and focuses on the design. We propose to combine non-physical editing with real-time feedback and provide artists with efficient ways of designing complex visual aspects such as global illumination or all-frequency shadows. We conform to existing pipelines by inserting our editing components into existing stages, hereby making editing of visual aspects an inherent part of the design process. Many of the examples showed in this work have been, until now, extremely hard to achieve. The non-physical aspect of our work enables artists to express themselves in more creative ways, not limited by the physical parameters of current renderers. Real-time feedback allows artists to immediately see the effects of applied modifications and compatibility with existing workflows enables easy integration of our algorithms into production pipelines.

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