Christoph Müller

Visualisation is an essential tool for analysing unstructured particle data as they are produced, for instance, in molecular dynamics or astrodynamics simulations. Such simulations often comprise multiple time steps, and scientists produce datasets with more and more particles in each step. To handle the steadily increasing size of these simulations, many solutions based on organising the data efficiently or reducing their amount using compression have been proposed over the years. Recently, a new storage API called DirectStorage has been introduced on Xbox consoles and Windows. DirectStorage promises a dramatic reduction of loading times in games by making the transfer from NVMe drives to graphics memory more efficient. That begs the question of whether DirectStorage is also beneficial for visualising time-dependent particle data that have to be streamed from disc to the GPU and whether a potential improvement in throughput enables interactive streaming of frames that traditional APIs cannot handle. For that, we implemented a benchmarking application that supports different streaming methods. We report on the results of an extensive series of tests with varying parameters that influence the streaming performance, which show that the performance of DirectStorage is highly dependent on the choice of various parameters. Graphic We provide an empirical evaluation for the performance gains to be expected when using the DirectStorage API for rendering dynamic particle data set like the one on the right side. For that, we compare the overlapping DirectStorage-based I/O scheme depicted on at the bottom with traditional methods

Gaming consoles, whether stationary or handheld, are designed to provide a reasonably high level of computing power to run contemporary video games at an attractive price point, a compact form factor and modest energy consumption. While consoles have traditionally been closed-off systems, recent versions of the Xbox allow the development of apps for the Universal Windows Platform (UWP) on retail devices, making it potentially a suitable platform for bringing scientific visualisation (SciVis) applications to the masses. We describe how to run such applications, namely volume rendering and ray casting of spherical glyphs, on commodity gaming systems, not only on the Xbox Series X/S, but also on handheld devices like the Steam Deck. We detail the challenges and limitations we encountered during the implementation and provide the results of an extensive study of rendering performance, not only proving the viability of the approach but also allowing for a cost and benefit evaluation compared to standard desktop computers. Graphical

Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs. Graphical abstract

Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective (e.g., to determine causal dependencies between the faults of different machines). We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context, and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.

Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a pre-processing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.

As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.

Immersive technologies like stereo rendering, virtual reality, or augmented reality (AR) are often used in the field of molecular visualisation. Modern, comparably lightweight and affordable AR headsets like Microsoft’s HoloLens open up new possibilities for immersive analytics in molecular visualisation. A crucial factor for a comprehensive analysis of molecular data in AR is the rendering speed. HoloLens, however, has limited hardware capabilities due to requirements like battery life, fanless cooling and weight. Consequently, insights from best practises for powerful desktop hardware may not be transferable. Therefore, we evaluate the capabilities of the HoloLens hardware for modern, GPU-enabled, high-quality rendering methods for the space-filling model commonly used in molecular visualisation. We also assess the scalability for large molecular data sets. Based on the results, we discuss ideas and possibilities for immersive molecular analytics. Besides more obvious benefits like the stereoscopic rendering offered by the device, this specifically includes natural user interfaces that use physical navigation instead of the traditional virtual one. Furthermore, we consider different scenarios for such an immersive system, ranging from educational use to collaborative scenarios.

The term code ownership is used in software engineering to describe who authored a certain piece of software. Code ownership is commonly determined by investigating the data from version control systems, defining the owner as the person who contributed the most lines to a file, module, etc. Existing visualisation for ownership usually relies on per-line annotations from the version control system, thus only conveying the information who changed a line the last time, potentially adding some visual cue about how old the respective change is. This, however, can be misleading, because any change of even a single character changes ownership. In this paper, we propose a visualisation that accounts for fractional ownership changes over time on a per-character basis. Our technique incorporates visual cues to convey that typical definitions of ownership have an inherent uncertainty and provides details on the cause of this uncertainty on demand. For our definition of code ownership being a low-level one, we propose implementing the visualisation as an in-situ visualisation in the code editor of modern development environments. We will show examples of the efficacy of our approach and discuss its advantages and disadvantages compared to conventional line-based ownership.