Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

The oneAPI Center will build a portable, scalable, performant ZFP backend using oneAPI and SYCL to advance exascale computing

Manycore and multicore architectures with large memory help with a wide variety of analyses, such as Morse-Smale decomposition to understand ion diffusion characteristics of simulated battery anode materials.

Monday the 19th, Salt Lake City – The University of Utah announces the creation of a new oneAPI Center of Excellence focused on developing portable, scalable, and performant data compression techniques. The Center for Extreme Data Management Analysis and Visualization (CEDMAV) at the University of Utah, in collaboration with the Lawrence Livermore National Laboratory’s (LLNL) Center for Applied Scientific Computing (CASC) will accelerate ZFP compression software using oneAPI open, standards-based, programming on multiple architectures to advance exascale computing.

The oneAPI Center’s efforts extend long-standing collaborations of these organizations dedicated to developing advanced data formats and layouts for efficient storage and providing access to large-scale scientific data for high performance computing (HPC) architectures. ZFP is state-of-the-art software for lossless and error-controlled lossy compression of floating-point data that is becoming a de facto standard in the HPC community, with numerous science and engineering applications and users. ZFP (de)compression is particularly amenable to data-parallel execution through its decomposition into small, independent data blocks, and parallel backends are developed for OpenMP, CUDA, and HIP programming models. The development of a high-performance SYCL port of ZFP on accelerator architectures supporting multiple vendors, including Intel Xe architecture GPUs, will benefit several high-visibility supercomputing applications and better showcase the power of an open, standards-based software ecosystem.

The University of Utah’s CEDMAV is internationally-recognized for its activities involving theoretical and algorithmic research, systems development, and tool deployment for dealing with extreme data. This research lies at the intersection of scientific visualization, big data management, HPC, and data analytics. CEDMAV’s research approach stems from a systematic assessment of HPC application needs and how they lead to new investigation and innovation, followed by practical validation and deployment to broader communities. CEDMAV collaborations with LLNL include shared research projects, staff with dual appointments, student interns, and postdocs. CASC serves as LLNL’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. With academic, industrial, and other government laboratory partners, it conducts world-class scientific research and development on problems critical to national security.

The oneAPI Center of Excellence with the ZFP development team at LLNL will develop a SYCL-based portable, scalable, and performant ZFP backend that runs on accelerator architectures across different vendors, including Intel Xe architecture GPUs. As one of the software technologies selected by the Department of Energy’s (DOE) Exascale Computing Project (ECP), ZFP is adopted by massively parallel simulations and technologies running on some of the world’s largest supercomputers, which will benefit several high-visibility scientific applications. Moreover, ZFP’s widespread adoption in industry and academia will help advance many large-scale data management technologies, including HDF5, ADIOS, OpenZGY, OpenVisus, and Zarr.

“Utah’s Center for Extreme Data Management Analysis and Visualization, in collaboration with LLNL’s Center for Applied Scientific Computing, has been pioneering research in managing extreme data applications involving scientific simulations and experimental facilities. This collaboration has a long track record of developing and deploying open-source scientific software that finds broad adoption in the communities of interest. This oneAPI Center of Excellence will strengthen this collaboration and help this academic research find practical adoption on Intel’s hardware-software computational environments,” says Manish Prashar, Director of the Scientific Computing and Imaging (SCI) Institute at the University of Utah.

Peter Lindstrom, a computer scientist of LLNL, says: “As lead of ZFP development, I am excited about this opportunity with our long-standing collaborators at the University of Utah to extend the capabilities of our ZFP compressor to run efficiently on next-generation supercomputers, including Argonne National Laboratory's Aurora system, one of the world's first exascale systems. The resulting compression software will allow large-scale scientific computing applications, among others, to effectively boost memory capacity and bandwidth while significantly reducing communication and I/O time and offline storage.”

“University of Utah and Lawrence Livermore National Laboratory’s work developing a highly performant SYCL based ZFP library aids the availability of large-scale scientific data for high performance computing architectures enabling exascale applications to target multiple accelerator architectures” said Scott Apeland, senior director of Intel Developer Ecosystem Programs.. “This latest Center of Excellence will showcase how open, standards-based oneAPI development benefits the developer community.”

About oneAPI

oneAPI is open, unified, cross-architecture programming model for CPUs and accelerator architectures (GPUs, FPGAs, and others). Based on standards, the programming model simplifies software development and delivers uncompromised performance for accelerated compute without proprietary lock-in, while enabling the integration of existing code. With oneAPI, developers can choose the best architecture for the specific problem they are trying to solve without needing to rewrite software for the next architecture and platform.

Valerio Pascucci

pascucciValerio Pascucci is the Inaugural John R. Parks Endowed Chair, the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV), a Faculty of the Scientific Computing and Imaging Institute, and a Professor of the School of Computing of the University of Utah. Valerio is also the President of ViSOAR LLC, a University of Utah spin-off, and the founder of Data Intensive Science, a 501(c) nonprofit providing outreach and training to promote the use of advanced technologies for science and engineering. Before joining the University of Utah, Valerio was the Data Analysis Group Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory and an Adjunct Professor of Computer Science at the University of California, Davis. Valerio's research interests include Big Data management and analytics, progressive multi-resolution techniques in scientific visualization, discrete topology, and compression. Valerio is the coauthor of more than two hundred refereed journal and conference papers and was an Associate Editor of the IEEE Transactions on Visualization and Computer Graphics.

Valerio said: “It is an honor for CEDMAV to establish this Intel center of excellence in collaboration with LLNL. This will give a great opportunity to solidify our collaboration and expand it with the support and collaboration of Intel engineers. It is exciting to see the emergence of the oneAPI programming model that we plan to fully embrace in this project. In particular, the SYCL cross-platform abstraction will tremendously increase the productivity of our teams in creating performant codes that run efficiently on modern, heterogeneous architectures. The Intel hardware-software ecosystem is ubiquitous in high-performance architectures and the oneAPI technology will increase tremendously the impact of ZFP in a broad spectrum of applications.”

Peter Lindstrom

lindstromPeter Lindstrom is a Computer Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research focuses on data compression, scientific visualization, and high-performance computing. Peter earned a Ph.D. in Computer Science from Georgia Institute of Technology in 2000 and holds B.S. degrees in Computer Science, Mathematics, and Physics from Elon University. He is the chief architect of the fpzip and zfp floating-point compressors and leads several ongoing research and development projects, including the zfp data compression effort as part of the DOE Exascale Computing Project. Peter is an IEEE Computer Society Distinguished Contributor and former Editor in Chief of Graphical Models.