Rendering on conventional computers is capable of generating realistic imagery, but the computational complexity of these light transport algorithms is a limiting factor of image synthesis. Quantum computers have the potential to significantly improve rendering performance through reducing the underlying complexity of the algorithms behind light transport. This article investigates hybrid quantum-classical algorithms for ray tracing, a core component of most rendering techniques. Through a practical implementation of quantum ray tracing in a 3D environment, we show quantum approaches provide a quadratic improvement in query complexity compared to the equivalent classical approach. Based on domain specific knowledge, we then propose algorithms to significantly reduce the computation required for quantum ray tracing through exploiting image space coherence and a principled termination criteria for quantum searching. We show results obtained using a simulator for both Whitted style ray tracing, and for accelerating ray tracing operations when performing classical Monte Carlo integration for area lights and indirect illumination.

Scientific ray tracing now can include realistic shading and material properties, but tracing rays of various depths to conclusion through partitioned data is inefficient. For such data, many ray scheduling methods have demonstrated improved rendering performance. However, synchronicity and non-adaptivity inherent in prior methods hinder further performance optimizations. In this paper, we attempt to relax these constraints. Specifically, we incorporate prediction models capable of dynamically adjusting levels of speculation in ray-data queries, making ray scheduling highly adaptable to a spectrum of scene characteristics. In addition, we organize rays in a tree of speculation nodes, where speculation is coordinated pairwise within a subtree of adaptive ray groups, facilitating concurrency and parallelism. Compared to prior non-predictive methods, we achieve up to three times higher throughput for volume and geometry rendering on a distributed system, making our method fit for both interactive and offline applications.

The conference on Practice and Experience in Advanced Research Computing (PEARC) provides a forum for discussing challenges, opportunities, and solutions among the broad range of participants in the research computing community. This community-driven effort builds on successes of the past and aims to grow and be more inclusive by involving additional local, regional, national, and international cyberinfrastructure and research computing partners spanning academia, government, and industry. The PEARC conference series is working to integrate and meet the collective interests of our growing community. PEARC originated from the XSEDE conference series, which showcased the discoveries, innovations, challenges, and achievements of those who use and support NSF Extreme Science and Engineering Discovery Environment (XSEDE) resources and services, as well as other digital resources and services throughout the world. The following papers represent the best papers from the transitionary conferences of this research community: XSEDE16, the final XSEDE conference, and PEARC17, the inaugural PEARC conference. These papers capture both important research contributions to the advanced computing community and best practices for advanced computing systems and remote user interfaces. These three papers cover a diverse set of topics: the impact of science gateways, innovative hardware impacting cyberinfrastructure, and improving computational frameworks with workflows, machine learning, and visualizations. The paper by Hu et al1 discusses how geospatial data have exploded to massive volume and diversity and subsequently cause serious usability issues for researchers in various scientific areas. This paper describes TopoLens, a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. TopoLens delivers community data services developed for easy and efficient access to high-resolution topographic data. It supports on-demand data and map services, powered by hybrid cyberinfrastructure with cloud and HPC support, to efficiently produce datasets that are customized based on a user's request. The usability of TopoLens has been acknowledged in the topographic user community evaluation. The paper by Hancock et al2 dives into Jetstream, the NSF's first distributed production cloud resource. Jetstream offers a unique capability within the XSEDE-supported US national cyberinfrastructure, delivering interactive virtual machines (VMs) via the atmosphere interface. As a multi-region deployment that operates as an integrated system, Jetstream is proving effective in supporting modes and disciplines of research traditionally underrepresented on larger XSEDE-supported clusters and supercomputers. Jetstream has been used to perform research and education in biology, biochemistry, atmospheric science, earth science, and computer science. Lastly, the paper written by Li and Song3 discusses the challenges of large-scale simulations generating huge amounts of data with potentially critical information that is saved in intermediate files and is not instantly visible until advanced data analytics techniques are applied after reading all simulation. In this paper, the authors build a new computational framework to couple scientific simulations with multi-step machine learning processes and in situ data visualizations. This computational framework is built upon different software components and provides plug-in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real-time notifications of special patterns or anomalies from ongoing extreme-scale turbulent flow simulations. These three papers demonstrate the wide range of topics addressed by the PEARC community, which spans academic researchers, resource providers, industry partners, and other affiliates. The work done by this community and the insights shared at the annual PEARC conference are intended for broad application for those active in advanced computing research and practice. If you are already part of the PEARC community, we look forward to seeing you soon; if you have not yet attended a PEARC conference, we hope these papers will provide motivation and justification to invest your time and travel to join us. The editors would like to acknowledge the hard work of the program committees, both for XSEDE16 and for PEARC17, whose combined efforts resulted in this special issue. Sincerely, Paul Navrátil Maytal Dahan Technical Program Chair Technical Program Chair XSEDE16 PEARC17

Scientific data is continually increasing in complexity, variety and size, making efficient visualization and specifically rendering an ongoing challenge. Traditional rasterization-based visualization approaches encounter performance and quality limitations, particularly in HPC environments without dedicated rendering hardware. In this paper, we present OSPRay, a turn-key CPU ray tracing framework oriented towards production-use scientific visualization which can utilize varying SIMD widths and multiple device backends found across diverse HPC resources. This framework provides a high-quality, efficient CPU-based solution for typical visualization workloads, which has already been integrated into several prevalent visualization packages. We show that this system delivers the performance, high-level API simplicity, and modular device support needed to provide a compelling new rendering framework for implementing efficient scientific visualization workflows.

Current architectural trends on supercomputers have dramatic increases in the number of cores and available computational power per die, but this power is increasingly difficult for programmers to harness effectively. High-level language constructs can simplify programming many-core devices, but this ease comes with a potential loss of processing power, particularly for cross-platform constructs. Recently, scientific visualization packages have embraced language constructs centering around data parallelism, with familiar operators such as map, reduce, gather, and scatter. Complete adoption of data parallelism will require that central visualization algorithms be revisited, and expressed in this new paradigm while preserving both functionality and performance. This investment has a large potential payoff: portable performance in software bases that can span over the many architectures that scientific visualization applications run on. With this work, we present a method for ray tracing consisting of entirely of data parallel primitives. Given the extreme computational power on nodes now prevalent on supercomputers, we believe that ray tracing can supplant rasterization as the work-horse graphics solution for scientific visualization. Our ray tracing method is relatively efficient, and we describe its performance with a series of tests, and also compare to leading-edge ray tracers that are optimized for specific platforms. We find that our data parallel approach leads to results that are acceptable for many scientific visualization use cases, with the key benefit of providing a single code base that can run on many architectures.

Abstract Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain‐space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two‐pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi‐field, multi‐material classification of particle data, providing better insight and analysis.

This paper extends and evaluates a family of dynamic ray scheduling algorithms that can be performed in-situ on large distributed memory parallel computers. The key idea is to consider both ray state and data accesses when scheduling ray computations. We compare three instances of this family of algorithms against two traditional statically scheduled schemes. We show that our dynamic scheduling approach can render data sets that are larger than aggregate system memory and that cannot be rendered by existing statically scheduled ray tracers. For smaller problems that fit in aggregate memory but are larger than typical shared memory, our dynamic approach is competitive with the best static scheduling algorithm.

Display Cluster is an interactive visualization environment for cluster-driven tiled displays. It provides a dynamic, desktop-like windowing system with built-in media viewing capability that supports ultra high-resolution imagery and video content and streaming that allows arbitrary applications from remote sources (such as laptops or remote visualization machines) to be shown. This support extends to high-performance parallel visualization applications, enabling interactive streaming and display for hundred-mega pixel dynamic content. Display Cluster also supports multi-user, multi-modal interaction via devices such as joysticks, smart phones, and the Microsoft Kinect. Further, our environment provides a Python-based scripting interface to automate any set of interactions. In this paper, we describe the features and architecture of Display Cluster, compare it to existing tiled display environments, and present examples of how it can combine the capabilities of large-scale remote visualization clusters and high-resolution tiled display systems. In particular, we demonstrate that Display Cluster can stream and display up to 36 mega pixels in real time and as many as 144 mega pixels interactively, which is 3× faster and 4× larger than other available display environments. Further, we achieve over a gig pixel per second of aggregate bandwidth streaming between a remote visualization cluster and our tiled display system.

We describe our visualization process for a particle-based simulation of the formation of the first stars and their impact on cosmic history. The dataset consists of several hundred time-steps of point simulation data, with each time-step containing approximately two million point particles. For each time-step, we interpolate the point data onto a regular grid using a method taken from the radiance estimate of photon mapping. We import the resulting regular grid representation into ParaView, with which we extract isosurfaces across multiple variables. Our images provide insights into the evolution of the early universe, tracing the cosmic transition from an initially homogeneous state to one of increasing complexity. Specifically, our visualizations capture the build-up of regions of ionized gas around the first stars, their evolution, and their complex interactions with the surrounding matter. These observations will guide the upcoming James Webb Space Telescope, the key astronomy mission of the next decade.