Data driven science, accompanied by the explosion of petabytes of data, has called into need dedicated analytics computing resources. Dedicated analytics clusters require large capital outlays due to their expensive hardware requirements. Additionally, if such resources are located far from the data they analyze, they also incur substantial data transfer, which has both cost and latency implications. In this paper, we benchmark a variety of high-performance computing (HPC) architectures for classic data science algorithms, as well as conduct a cost analysis of these architectures. Additionally, we compare algorithms across analytic frameworks, as well as explore hidden costs in the form of queuing mechanisms. We observe that node architectures with large memory and high memory bandwidth are better suited for big data analytics on HPC hardware. We also conclude that cloud computing is more cost effective for small or experimental data workloads, but HPC is more cost effective at scale. Additionally, we quantify the hidden costs of queuing and how it relates to data science workloads. Finally, we observe that software developed for the cloud, such as Spark, performs significantly worse than pbdR when run in HPC environments.

In this paper, we propose strategies and objectives for immersive data visualization with applications in materials science using the Oculus Rift virtual reality headset. We provide background on currently available analysis tools for neutron scattering data and other large-scale materials science projects. In the context of the current challenges facing scientists, we discuss immersive virtual reality visualization as a potentially powerful solution. We introduce a prototype immersive visualization system, developed in conjunction with materials scientists at the Spallation Neutron Source, which we have used to explore large crystal structures and neutron scattering data. Finally, we offer our perspective on the greatest challenges that must be addressed to build effective and intuitive virtual reality analysis tools that will be useful for scientists in a wide range of fields.

Extracting and visualizing temporal patterns in large scientific data is an open problem in visualization research. First, there are few proven methods to flexibly and concisely define general temporal patterns for visualization. Second, with large time-dependent data sets, as typical with today's large-scale simulations, scalable and general solutions for handling the data are still not widely available. In this work, we have developed a textual pattern matching approach for specifying and identifying general temporal patterns. Besides defining the formalism of the language, we also provide a working implementation with sufficient efficiency and scalability to handle large data sets. Using recent large-scale simulation data from multiple application domains, we demonstrate that our visualization approach is one of the first to empower a concept driven exploration of large-scale time-varying multivariate data.

Chromium Renderserver (CRRS) is software infrastructure that provides the ability for one or more users to run and view image output from unmodified, interactive OpenGL and X11 applications on a remote, parallel computational platform equipped with graphics hardware accelerators via industry-standard Layer 7 network protocols and client viewers. The new contributions of this work include a solution to the problem of synchronizing X11 and OpenGL command streams, remote delivery of parallel hardware accelerated rendering, and a performance analysis of several different optimizations that are generally applicable to a variety of rendering architectures. CRRS is fully operational, Open Source software. imagery and sending it to a remote viewer.