Kitware and Intel Combine Expertise to Advance Proven Software Solutions
Kitware announced its collaboration with Intel to integrate two open-source rendering libraries into the Visualization Toolkit (VTK) and ParaView: OpenSWR to rasterize images and OSPRay to ray trace images. The integration will make OpenSWR and OSPRay more accessible to VTK and ParaView users, amplifying their capacity to interactively visualize Big Data on supercomputers without employing graphics processing units (GPUs).
“OpenSWR and OSPRay will benefit VTK and ParaView users by reducing costs and memory constraints while increasing parallel rendering performance and fidelity,” Dave DeMarle, who leads the integration effort at Kitware, said. “In our tests, we found OpenSWR to be competitive with GPU rendering on moderate and larger sized meshes.”
The integration of OpenSWR and OSPRay into VTK and ParaView will allow users to easily switch between image rasterization and ray-tracing processes to maximize results. OpenSWR is a software rasterization engine optimized on Intel processors, making it fast enough to be suitable for rendering extraordinarily large data sets in VTK and ParaView. Also optimized for Intel processors, OSPRay enhances the quality of rendered images and provides VTK and ParaView users with unprecedented capabilities. Realistic yet still interactive illumination models, for example, enable users to simultaneously recognize small- and large-scale properties in their data.
“One of our top priorities since launching this initiative has been to ensure that ParaView and VTK users can make optimal use of the Intel supported Software Defined Visualization open-source projects (http://www.sdvis.org), providing new levels of interactivity and higher fidelity visualization for large-scale data,” Jim Jeffers, Principal Engineer and Manager for Intel’s HPC Visualization team, said. “Kitware has been a fantastic partner, as we’ve worked together to integrate and deliver these new software-rendering technologies, enabling more flexible and rapid methods to drive new scientific insights.”
ParaView 5.0 introduces binaries that include OpenSWR and OSPRay. Integration of the libraries is also underway in VTK’s rendering backend. The integration effort is just one push to modernize VTK’s rendering capabilities. In 2014, Kitware began rewriting the majority of VTK’s rendering code. As part of the rewrite, Kitware introduced a new rendering engine that became available in VTK 6.2. The new engine significantly enhances rendering speed and memory utilization, with improvements to volume rendering performance reaching 300 percent. To further modernize VTK, Kitware has been working with Intel to support the toolkit’s efficient use of central processing units (CPUs) and CPU coprocessors such as Intel® Xeon Phi™ Coprocessor.
“Modernizing visualization tools like ParaView and VTK to better leverage the available compute resources in multicore Intel® Xeon® processors and many-core Intel® Xeon Phi™ products, both of which utilize the same source code and programming models, will provide the foundation for the performance and features that are needed to meet future visualization requirements, which are being driven by the exponential growth in data as we move toward exascale computing,” Intel’s Jeffers said.
VTK is an open-source, freely available software system for three-dimensional (3D) computer graphics, modeling, image processing, volume rendering, scientific visualization, and information visualization. It provides the basis for data processing and rendering capabilities for many advanced visualization applications, such as ParaView. ParaView analyzes extremely large data sets using distributed memory computing resources. Its flexibility allows developers to tailor functionality to specific problem domains.
The 1.44 million-atom GROMACS data set used for this visualization is courtesy of Peter Tieleman of the University of Calgary.
ParaView Receives Honor in HPCwire Readers’ and Editors’ Choice Awards
The annual HPCwire Readers’ and Editors’ Choice Awards—presented at the 2015 International Conference for High Performance Computing, Networking, Storage and Analysis (SC15), in Austin, Texas—recognized Kitware on behalf of the ParaView community. HPCwire revealed the list of winners at its booth and on its website at http://www.HPCwire.com. Kitware accepted the following honor for ParaView: Readers’ Choice – Best HPC Visualization Product or Technology.
“We would like to thank HPCwire and its readers for recognizing ParaView, as well as the ParaView community for contributing to the development of the open-source application,” Berk Geveci, Kitware’s Senior Director of Scientific Computing, said. “ParaView has witnessed major advancement in terms of high-performance computing (HPC) this year. This advancement will continue with the version 5.0 release and the introduction of the all-new rendering backend.”
A nomination and voting process with the global HPCwire community and selections from the HPCwire editors determine the coveted HPCwire Readers’ and Editors’ Choice Awards. Revealed each year to kick off the supercomputing conference, which showcases high-performance computing, networking, storage, and data analysis, the awards are feature of the HPCwire publication and constitute prestigious recognition from the HPC community.
“HPCwire readers are among the most informed in the HPC community, and these awards are ultimately given to the organizations that are making the greatest impact in advancing technology and humanity itself through HPC,” Tom Tabor, CEO of Tabor Communications, publisher of HPCwire, said. “The HPCwire Readers’ and Editors’ Choice Awards send a strong message of support and appreciation from those in the global HPC community. We are proud to be able to recognize these efforts each year, and our congratulations go out to all the winners.”
For more information on these awards, please visit http://www.HPCwire.com or Twitter through the following hashtag: #HPCwireAwards.
Collaboration with NICE Pushes Visualization to the Next Level
Kitware, the lead developer of ParaView, and NICE, whose Desktop Cloud Visualization (DCV) enables technical computing users to remotely access interactive engineering and scientific applications, announced a partnership to enhance ParaView’s remote accessibility from supercomputing centers and the cloud.
“We want to optimize support for ParaView users and to make the experience of using the application as easy and as uniform as possible,” Berk Geveci, Kitware’s Senior Director of Scientific Computing, said.
Using ParaView to analyze computations run on supercomputers or in the cloud generally entails copying large data sets onto workstations for exploration and visualization. As the sizes of simulation outputs increase, copying data becomes impractical. Coupling ParaView with NICE DCV enables users to run ParaView on the same resources that produced the data and access it remotely on DCV EndStations used solely for display.
“With DCV, only rendered images are sent between the computational systems and the EndStations,” Geveci said. “Large computation data such as geometry and scene information is left on the systems. This allows ParaView users to leverage the high-memory and high-performance input/output of more advanced systems, such as those at data centers, without waiting for data to load on their EndStations.”
Using ParaView in combination with NICE DCV has additional benefits. For one, users can access ParaView without the added step of downloading clients onto their EndStations. ParaView users can also benefit from enhanced collaboration through DCV’s session-sharing capabilities for multiple EndStations.
“NICE DCV was designed and built with the ParaView use case in mind,” Andrea Rodolico, NICE’s CTO, said. “We aimed to help engineers and scientists to work on larger models, collaborate, and not be bound to their workstations so they can get the most from their application software. Our partnership with Kitware will expand the base of users who are able to experience NICE DCV benefits and ensure they get the top-notch performance they deserve.”
While ParaView runs on supercomputers, desktops, laptops, and tablets, scenarios well-suited to using ParaView with DCV exist. For example, organizations may have multiple people who need to use ParaView remotely, in full mobility, over slow/unreliable networks, or without the possibility of installing ParaView’s software on their clients. In such cases, they can run ParaView on corporate servers and access it through DCV.
“We are excited about this partnership, as both Kitware and NICE leverage the latest NVIDIA graphics processing unit (GPU) innovations in their flagship products for handling large data analysis and visualization challenges,” Bhushan Desam, NVIDIA’s Senior Alliances and Marketing Manager for Visualization in HPC, said. “ParaView’s interactive performance on GPUs in supercomputers and high-performance computing (HPC) centers can now be seamlessly delivered to scientists and users who are visualizing large data remotely with NICE DCV.”
ParaView is an open-source, multi-platform data analysis and visualization application. It allows users to quickly build visualizations to analyze data using qualitative and quantitative techniques. ParaView’s flexibility enables developers to tailor functionality to specific problem domains.
Álvaro joined Kitware’s Clifton Park office as a member of the Scientific Computing team. He received his master’s in medical imaging from Kungl Tekniska Högskolan (KTH) Royal Institute of Technology in Sweden. Prior to joining Kitware, Álvaro was a software engineer at SurgicEye GmbH.
François became a member of the Medical Computing team in Carrboro, NC. He graduated with a master’s degree from École Supérieure de Chimie Physique Électronique de Lyon (CPE Lyon). François brings experience as a research associate at the Neuro-Image Research and Analysis Laboratories (NIRAL) at the University of North Carolina at Chapel Hill.
Chengjiang joined Kitware’s Computer Vision team in Clifton Park. He received his doctorate in computer science from Stevens Institute of Technology, where he has experience as a teaching assistant in the areas of artificial intelligence, machine learning, and computer vision.
Bastien became a Technical Leader on the Computer Vision team in Lyon, France. He is currently pursuing a doctoral degree at Eidgenössische Technische Hochschule (ETH) Zürich. His research interests include three dimensional (3D) reconstruction.
Reid started in Santa Fe as a research and development engineer on the Computer Vision team. He holds a doctorate in electrical engineering and has experience as a research scientist at Los Alamos National Laboratory.
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