Aashish Chaudhary

Ultrascale Visualization of Climate Data

Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.

Immersive ParaView: A community-based, immersive, universal scientific visualization application

The availability of low-cost virtual reality (VR) systems coupled with a growing population of researchers accustomed to newer interface styles makes this a ripe time to help domain science researchers cross the bridge to utilizing immersive interfaces. The logical next step is for scientists, engineers, doctors, etc. to incorporate immersive visualization into their exploration and analysis workflows. However, from past experience, we know having access to equipment is not sufficient. There are also several software hurdles to overcome. Obstacles must be lowered to provide scientists, engineers, and medical professionals low-risk means of exploring technologies beyond their desktops.

Virtual reality enabled scientific visualization workflow

For decades Virtual Reality (VR) has remained out of our offices and everyday workflows due to its high cost, its custom-built nature, and its often narrow design for highly specific tasks. These characteristics create a very high entry barrier for most users to use VR technology. Users typically had to be well funded, possess a high degree of expertise, spend weeks of effort porting VR capable software, or go to a specialized visualization laboratory. Recently, the cost is coming down significantly as new affordable consumer-grade VR devices are becoming available (e.g., zSpace). However, in order to incorporate VR technology into our everyday office settings, the entry barrier must be lowered significantly. This paper describes a VR framework that lowers the entry barrier for everyday users. The framework is designed on the visualization workflow of typical users working with scientific visualization in university settings. It includes a regular desktop computer, a typical non-VR display, a consumer-grade immersive VR display (zSpace), and frequently-used visualization applications (ParaView, EnSight). By designing and building this framework, we aim at measuring and studying the usefulness and effects of integrating VR technology in everyday office settings. While the framework will be initially tested with ParaView, the study aims at producing generalizable findings that extend to users of other 3D desktop applications such as 3DS Max or Maya. This can help users and software companies to add similar support in their packages and allow users to experience VR in everyday environments.

Enhancements to VTK enabling scientific visualization in immersive environments

Modern scientific, engineering and medical computational simulations, as well as experimental and observational data sensing/measuring devices, produce enormous amounts of data. While statistical analysis provides insight into this data, scientific visualization is tactically important for scientific discovery, product design and data analysis. These benefits are impeded, however, when scientific visualization algorithms are implemented from scratch — a time-consuming and redundant process in immersive application development. This process can greatly benefit from leveraging the state-of-the-art open-source Visualization Toolkit (VTK) and its community. Over the past two (almost three) decades, integrating VTK with a virtual reality (VR) environment has only been attempted to varying degrees of success. In this paper, we demonstrate two new approaches to simplify this amalgamation of an immersive interface with visualization rendering from VTK. In addition, we cover several enhancements to VTK that provide near real-time updates and efficient interaction. Finally, we demonstrate the combination of VTK with both Vrui and OpenVR immersive environments in example applications.

Building near-real-time processing pipelines with the spark-MPI platform

Advances in detectors and computational technologies provide new opportunities for applied research and the fundamental sciences. Concurrently, dramatic increases in the three V’s (Volume, Velocity, and Variety) of experimental data and the scale of computational tasks produced the demand for new real-time processing systems at experimental facilities. Recently, this demand was addressed by the Spark-MPI approach connecting the Spark data-intensive platform with the MPI high-performance framework. In contrast with existing data management and analytics systems, Spark introduced a new middleware based on resilient distributed datasets (RDDs), which decoupled various data sources from high-level processing algorithms. The RDD middleware significantly advanced the scope of data-intensive applications, spreading from SQL queries to machine learning to graph processing. Spark-MPI further extended the Spark ecosystem with the MPI applications using the Process Management Interface. The paper explores this integrated platform within the context of online ptychographic and tomographic reconstruction pipelines.

A model for optimizing file access patterns using spatio-temporal parallelism

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.