SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

Created: 08 January 2015

Martin Berzins has been appointed a member of the Advanced Scientific Computing Advisory Committee (ASCAC). The committee provides advice and recommendations on scientific, technical, and programmatic issues relating to the ASCAC Program.

The Advanced Scientific Computing Advisory Committee (ASCAC), established on August 12, 1999, provides valuable, independent advice to the Department of Energy on a variety of complex scientific and technical issues related to its Advanced Scientific Computing Research program.

Learn more about ASCAC

Created: 07 January 2015

Credit: Jim Collins - Argonne Leadership Computing Facility
See original article: "Simulations Aimed at Safer Transport of Explosives"

In 2005, a semi-truck hauling 35,000 pounds of explosives through the Spanish Fork Canyon in Utah crashed and caught fire, causing a dramatic explosion that left a 30-by-70-foot crater in the highway.

Fortunately, there were no fatalities. With about three minutes between the crash and the explosion, the driver and other motorists had time to flee. Some injuries did occur, however, as the explosion sent debris flying in all directions and produced a shock wave that blew out nearby car windows.

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Created: 17 September 2014

Wednesday, September 17th 2014
10 am to 3 pm
University of Utah
Warnock Engineering Building, Catmull Gallary
72 So. Central Campus Dr.

The first UDCC open house will bring together our consortium partners and engineering students to a single venue. Partners interested in sponsoring student internships through the new Data Center Engineering Certificate will be present for questions, and students will have the opportunity to hear from and engage with some of our nation's leading experts in the field. You can visit our website or email us for more information.

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Created: 20 August 2014

The Image-Based Biomedical Modeling (IBBM) summer course was held from July 14 to July 24 in the Newpark Hotel, Park City, Utah.

The two-week summer course hosted 39 participants this year: 31 graduate students, 1 MD/PhD student, 2 postdoctoral fellows, 3 junior faculty, and 2 developers from a research laboratory / industry. Participants came from 24 institutions, including 4 from universities in Belgium and England. After the first week of common classes, participants were divided into two tracks: Bioelectricity (10 participants) and biomechanics (29 participants).

IBBM is a dedicated two-week course in the area of image-based modeling and simulation applied to bioelectricity and biomechanics, providing participants with training in the numerical methods, image analysis, visualization, and computational tools necessary to carry out end-to-end, image-based, subject-specific simulations in either bioelectricity or orthopedic biomechanics. The course focuses on using freely available, open-source software developed under the research of the CIBC (P41 GM103545) and FEBio suite (RO1 GM083925). Students use this software to learn and apply the complete dataflow pipeline to particular sets of data with specific goals.

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Created: 12 August 2014

This summer, high school students from the Salt Lake area are coming to the University of Utah to participate in hands-on research in image analysis of the human brain. In conjunction with graduate students and faculty in the School of Computing and the Scientific Computing and Imaging Institute, the students are learning how computer science can help neuroscience researchers understand the brain and disorders that affect it, such as Alzheimer's disease and Autism.

Advances in medical imaging devices, such as magnetic resonance imaging (MRI), have led to our ability to acquire detailed information about the living human brain, including its anatomical structure, function, and connectivity. However, making sense of this complex data is a difficult task, especially in large imaging studies that may include hundreds or even thousands of participants. This is where computer science can play an important role. Image analysis algorithms can automatically quantify properties of the brain, such as the size of brain structures, or the functional activity in different brain regions. This provides neuroscience researchers with insights into how the brain functions and what abnormalities are present in diseased brains.

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Created: 29 July 2014

There has been a recent explosion of interest in the use of noninvasive transcranial brain stimulation (or "neurostimulation"), both in clinical settings and as a research tool. One of the two main ways to stimulate the brain transcranially is to run a current to the brain through the magnetic fields generated by a coil that is held near the scalp. This approach has been approved by the FDA for treating depression. The other main approach uses electrodes placed on the scalp to "inject" current into the head, or apply voltage on the scalp, which is known as transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), depending on the type of current or voltage source used. Various neurostimulation technologies have been tested in human experiments for a huge variety of applications including motor rehabilitation, speech therapy, enhancement of cognitive learning, and treating depression and other affective and behavioral disorders, chronic pain syndrome, post-traumatic stress disorder, and others.

Transcranial Magnetic Stimulation (TMS) of the human motor cortex.

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Created: 29 July 2014

SCIRun is the integrated programming environment that has been a core technology of the CIBC since its inception and each major version release is an enormous undertaking. The program now contains hundreds of thousands of lines of C++ code and a new release requires at least a review of all this code, with replacement or updating of larger portions of it. We are nearing the first release of such a major new version, SCIRun 5.


There must be considerable motivation for such a major release, motivation which comes from both our users, collaborators, and DBP partners but also from advances in software engineering and scientific computing, with which we must also keep pace. Our users continue to demand more efficiency, more flexibility in programming the workflows created with SCIRun, more support for big data, and more transparent access to large compute resources when simulations exceed the useful capacity of local resources. The evolution of software engineering has led to changes in computer languages, programming paradigms, visualization hardware and processing, user interface design (and tools to support this critical component), and the third party libraries that form the building blocks of complex scientific software. SCIRun 5 is a response to all these changing conditions and needs and also represents some long awaited refactoring that will provide greater flexibility and freedom as we move into the next generation of scientific computing.

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Created: 29 July 2014

Figure 1: 3D surface mesh of a face.

Partial differential equations (PDEs) are ubiquitous in engineering applications. They mathematically model natural phenomena such as heat conduction, diffusion, and shock wave propagation. They also describe many bioelectrical and biomechanical functions and are a central element of the simulation research of the Center. Analytical solutions for most PDEs are known only for certain symmetric domains, such as a circle, square, or sphere. In order to obtain solutions to PDEs for more realistic domains, numerical approximations such as the finite element method (FEM) are used. In the FEM, both the domain and the PDE are discretized and a numerical solution is calculated using computational resources. The discretization of the geometric domain is called a mesh. Meshes play a vital role in the numerical solution of PDEs on a given geometric domain, the accuracy of which depends on parameters such as the shape and size of the mesh elements. The most commonly used meshes contain tetrahedral elements. While simple conceptually, mesh generation is one of the most computationally intensive tasks in solving a PDE numerically.

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Created: 28 May 2014

Introducing ViSOAR. As data acquisition advances, and data sizes increase, the need for tools to process and visualize the results in an effective and efficient manner is becoming increasingly important. The reliance on supercomputers for scientific visualization and analysis is already proving to be a hindrance for wide accessibility to researchers and scientists dealing with large data.

The Scientific Computing and Imaging (SCI) Institute and the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV), in collaboration with ARUP Laboratories and the University of Utah, Department of Neurobiology and Anatomy, have developed ViSOAR--a multi platform visualization application for accessing and processing very large imaging data.

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Created: 08 February 2014

From the University of Utah: Feb. 7, 2014 – The University of Utah's College of Engineering received approval this week for its new graduate certificate program in big data.

This emerging field – which addresses large sets of data too complex, diverse or rapidly changing for one computer to handle – affects everything from studying traffic patterns to managing sensitive information online. Big data is also big business – for example, using big data to improve efficiency and quality in the health care sector is estimated to be worth more than $300 billion each year.

"We're seeing a revolution in the availability of data. It's easy to collect information, but processing and analyzing large stores of data is becoming increasingly difficult. We are at the point where the traditional analytical tools for attacking this problem are breaking down," says Jeff Phillips, assistant professor of computer science and coordinator of the new program.

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