Profile

Several group navigation techniques enable a single navigator to control travel for all group members simultaneously in social virtual reality. A key aspect of this process is the ability to rearrange the group into a new formation to facilitate the joint observation of the scene or to avoid obstacles on the way. However, the question of how users should be distributed within the new formation to create an intuitive transition that minimizes disruptions of ongoing social activities is currently not explored. In this paper, we begin to close this gap by introducing four user placement strategies based on mathematical considerations, discussing their benefits and drawbacks, and sketching further novel ideas to approach this topic from different angles in future work. Our work, therefore, contributes to the overarching goal of making group interactions in social virtual reality more intuitive and comfortable for the involved users.

Visualization is used throughout all scientific domains for efficient analysis of data and experiments. Learning, underestanding, implementing, and applying suitable, state-of-the-art visualization techniques in HPC projects takes time and and a high level of technological ability and experience. DaVE sets out to offer a user-friendly resource with detailed descriptions, samples, and HPC-specific implementations of visualization methods. It simplifies method discovery with tags and encourages collaboration to foster a platform for sharing best practices and staying updated thus filling a crucial gap in the HPC community by providing a central resource for advanced visualization.

Due to the ever-increasing availability of high-performance computing infrastructure, developers can simulate increasingly complex models. However, the increased complexity comes with new challenges regarding data processing and visualization due to the sheer size of simulations. Exploring simulation results needs to be handled efficiently via in-situ/in-transit analysis during run-time. However, most existing in-transit solutions require sophisticated and prior knowledge and significant alteration to existing simulation and visualization code, which produces a high entry barrier for many projects. In this work, we report how Insite, a lightweight in-transit pipeline, provided in-transit visualization and computation capability to various research applications in the neuronal network simulation domain. We describe the development process, including feedback from developers and domain experts, and discuss implications.

COTS VR hardware and modern game engines create the impression that bringing even complex data into VR has become easy. In this work, we investigate to what extent game engines can support the development of immersive visualization software with a case study. We discuss how the engine can support the development and where it falls short, e.g., failing to provide acceptable rendering performance for medium and large-sized data sets without using more sophisticated features.

Simulation of neuronal networks has steadily advanced and now allows for larger and more complex models. However, scaling simulations to such sizes comes with issues and challenges.Especially the amount of data produced, as well as the runtime of the simulation, can be limiting.Often, storing all data on disk is impossible, and users might have to wait for a long time until they can process the data.A standard solution in simulation science is to use in-transit approaches.In-transit implementations allow users to access data while the simulation is still running and do parallel processing outside the simulation.This allows for early insights into the results, early stopping of simulations that are not promising, or even steering of the simulations.Existing in-transit solutions, however, are often complex to integrate into the workflow as they rely on integration into simulators and often use data formats that are complex to handle.This is especially constraining in the context of multi-disciplinary research conducted in the HBP, as such an important feature should be accessible to all users.

To remedy this, we developed Insite, a pipeline that allows easy in-transit access to simulation data of multiscale simulations conducted with TVB, NEST, and Arbor.

Neuronal network simulators are central to computational neuroscience, enabling the study of the nervous system through in-silico experiments. Through the utilization of high-performance computing resources, these simulators are capable of simulating increasingly complex and large networks of neurons today. Yet, the increased capabilities introduce a challenge to the analysis and visualization of the simulation results. In this work, we propose a pipeline for in-transit analysis and visualization of data produced by neuronal network simulators. The pipeline is able to couple with simulators, enabling querying, filtering, and merging data from multiple simulation instances. Additionally, the architecture allows user-defined plugins that perform analysis tasks in the pipeline. The pipeline applies traditional REST API paradigms and utilizes data formats such as JSON to provide easy access to the generated data for visualization and further processing. We present and assess the proposed architecture in the context of neuronal network simulations generated by the NEST simulator.

Particle advection is the approach for the extraction of integral curves from vector fields. Efficient parallelization of particle advection is a challenging task due to the problem of load imbalance, in which processes are assigned unequal workloads, causing some of them to idle as the others are performing computing. Various approaches to load balancing exist, yet they all involve trade-offs such as increased inter-process communication, or the need for central control structures. In this work, we present two local load balancing methods for particle advection based on the family of diffusive load balancing. Each process has access to the blocks of its neighboring processes, which enables dynamic sharing of the particles based on a metric defined by the workload of the neighborhood. The approaches are assessed in terms of strong and weak scaling as well as load imbalance. We show that the methods reduce the total run-time of advection and are promising with regard to scaling as they operate locally on isolated process neighborhoods.

Geodesic ray tracing is the numerical method to compute the motion of matter and radiation in spacetime. It enables visualization of the geometry of spacetime and is an important tool to study the gravitational fields in the presence of astrophysical phenomena such as black holes. Although the method is largely established, solving the geodesic equation remains a computationally demanding task. In this work, we present Astray; a high-performance geodesic ray tracing library capable of running on a single or a cluster of computers equipped with compute or graphics processing units. The library is able to visualize any spacetime given its metric tensor and contains optimized implementations of a wide range of spacetimes, including commonly studied ones such as Schwarzschild and Kerr. The performance of the library is evaluated on standard consumer hardware as well as a compute cluster through strong and weak scaling benchmarks. The results indicate that the system is capable of reaching interactive frame rates with increasing use of high-performance computing resources. We further introduce a user interface capable of remote rendering on a cluster for interactive visualization of spacetimes.

Radiofrequency ablation is a minimally invasive, needle-based medical treatment to ablate tumors by heating due to absorption of radiofrequency electromagnetic waves. To ensure the complete target volume is destroyed, radiofrequency ablation simulations are required for treatment planning. However, the choice of tissue properties used as parameters during simulation induce a high uncertainty, as the tissue properties are strongly patient-dependent. To capture this uncertainty, a simulation ensemble can be created. Understanding the dependency of the simulation outcome on the input parameters helps to create improved simulation ensembles by focusing on the main sources of uncertainty and, thus, reducing computation costs. We present an interactive visual analysis tool for radiofrequency ablation simulation ensembles to target this objective. Spatial 2D and 3D visualizations allow for the comparison of ablation results of individual simulation runs and for the quantification of differences. Simulation runs can be interactively selected based on a parallel coordinates visualization of the parameter space. A 3D parameter space visualization allows for the analysis of the ablation outcome when altering a selected tissue property for the three tissue types involved in the ablation process. We discuss our approach with domain experts working on the development of new simulation models and demonstrate the usefulness of our approach for analyzing the influence of different tissue properties on radiofrequency ablations.

The analysis of multirun oceanographic simulation data imposes various challenges ranging from visualizing multifield spatio-temporal data over properly identifying and depicting vortices to visually representing uncertainties. We present an integrated interactive visual analysis tool that enables us to overcome these challenges by employing multiple coordinated views of different facets of the data at different levels of aggregation.

Ordinary differential equations (ODE) are used to describe the evolution of one or more dependent variables using their derivatives with respect to an independent variable. They arise in various branches of natural sciences and engineering. We present a modern, efficient, performance-oriented ODE solving library built in C++20. The library implements a broad range of multi-stage and multi-step methods, which are generated at compile-time from their tableau representations, avoiding runtime overhead. The solvers can be instantiated and iterated on the CPU and the GPU using identical code. This work introduces the prominent features of the library with examples

We present a modern C++20 interface for MPI 4.0. The interface utilizes recent language features to ease development of MPI applications. An aggregate reflection system enables generation of MPI data types from user-defined classes automatically. Immediate and persistent operations are mapped to futures, which can be chained to describe sequential asynchronous operations and task graphs in a concise way. This work introduces the prominent features of the interface with examples. We further measure its performance overhead with respect to the raw C interface.