Abstract

Abstract
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior knowledge of the data characteristics.

Abstract
Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for non-monotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.

Abstract
This paper presents a description of the image generation sub-system developed to allow the presentation of realistic visual feedback in interactive visual simulation with large scene databases. The developed image generator applies all the standard state-of-art image generation algorithms aimed to real-time interactive simulation. Some of these important algorithms are also explained in this document. I addition, some innovative optimization techniques like the hierarchical back face rejection of objects, the visibility preprocessing and the automatic optimization of levels-of-detail are being developed and detailed in this paper. These techniques will allow a better use of any image generation system and improve significantly the visualization of huge scene databases even in high-end graphics architectures.

Abstract

Abstract
Content generation is a heavy task in virtual worlds design. Procedural content generation techniques aim to agile this process by automating the 3D modelling with some degree of parametrisation. The novelty of this work is the procedural generation of the marine alga (Asparagopsis armata), taking into consideration the underwater environmental factors. The depth and the occlusion were the two parameters in this study to simulate how the alga growth is influenced by the environment where the alga grows. Starting by building a prototype to explore different L-systems categories to model the alga, the stochastic L-systems with parametric features were selected to generate different alga plasticities. Qualitative methods were used to evaluate the designed grammar and alga's animation results by comparing videos and images of the Asparagopsis armata with the computer-generated versions. © 2022 University of West Bohemia. All rights reserved.