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High Resolution Numerical Simulations of Environmental, Renewable Energy, and Biological Flows

Abstract: 

High-Resolution Numerical Simulations of Environmental, Renewable Energy, and Biological Flows

The Computational Hydrodynamics and Biofluids Group at the St. Anthony Falls Laboratory is developing high resolution, fluid-structure interaction computational fluid dynamics models for simulating: physiologic blood flow in mechanical heart valves implanted in patient-specific anatomies; flow past wind and hydrokinetic turbines; and turbulent flow and sediment transport in natural rivers with nature and man-made structures. The numerical algorithms employ a sharp-interface immersed boundary method for handling arbitrarily complex geometries with multiple moving immersed bodies enhanced with local grid refinement approach. Both loose and strongly coupled fluid structure interaction algorithms have been developed and validated. The computer code has been parallelized with MPI.

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Group name: 
sotiropo

Performance Evaluation of Storage Systems

Abstract: 

Performance Evaluation of Storage Systems

This group has focused their research on the design and evaluation of large-scale storage systems. They have investigated  the impact of NV-RAM on future computing and communication environments, and how to design and evaluate large scale Solid State Drives (SSD) and Shingled Magnetic Recording (SMR) drives. They are especially interested in I/O workload analyses for many emerging applications like Big Data or real-time video and audio streaming, and high-performance computing (like Lustre file system).

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Group name: 
dudh

High performance optimization for geographic optimization and feature detection

Abstract: 

High Performance Optimization for Geographic Optimization and Feature Detection

The purpose of this project is to design high-performance optimization algorithms for large-scale geographic and medical data with up to half a million feature vectors. These algorithms will make extensive use of parallel structures.

Group name: 
carlsson

MSI Research Exhibition 2016 - Physical Sciences and Engineering Posters

The titles for the posters submitted in the Physical Sciences and Engineering category for the 2016 MSI Research Exhibition are listed are shown below. See posters in the Biological and Medical Sciences category . Return to Research Exhibition 2016 page . A Multiphysics Model of the Pacinian...

Polar Geospatial Center to Map Alaska and Arctic

The University of Minnesota’s Polar Geospatial Center (PGC) has received funding from the National Geospatial-Intelligence Agency and the National Science Foundation to create the first publicly available, high-resolution satellite-based elevation maps of Alaska and the arctic. The Alaska map will...

Polar Geospatial Center Releases New Image Applications

posted on December 17, 2013 New applications created by the University of Minnesota’s Polar Geospatial Center (PGC) were recently highlighted by the The Antarctic Sun , a publication from the United States Antarctic Program. The article contains quotes from Professor Paul Morin , the Center...

Genomewide Predictions of Maize Performance

Abstract: 

Genomewide Predictions of Maize Performance

Genomewide predictions allow the evaluation of maize lines or hybrids without field testing (phenotyping) of the lines or hybrids themselves. In particular, genomewide marker effects are estimated from a training population that has been previously genotyped and phenotyped. These marker effects are then used to assess the performance of new lines or hybrids that have been genotyped, but not yet phenotyped. Genomewide predictions therefore leverage the lower costs of genotyping (about $15 per line or hybrid) than of phenotyping (about $120 per line or hybrid). Since 2011, the Bernardo research group has been given access by Monsanto to about $25 million worth of phenotypic and marker datasets from its own maize breeding program. These datasets have allowed the group to investigate ways to optimize genomewide predictions in maize breeding. Research efforts are now focusing not only on predicting performance averaged across environments, but also in specific environments. The scale of the datasets (969 populations, >4 million phenotypic data points, > 11 million marker data points, and environmental covariables on 432 year-location combinations) has necessitated the use of high-performance computing resources.

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Group name: 
bernardo

Parallel Simulations for Computer Architecture and Computer Aided Design

Abstract: 

Parallel Simulations for Computer Architecture and Computer Aided Design

For several emerging applications such as wearables, internet of things, and sensor networks, energy efficiency is of utmost importance. While custom ASICs have higher energy efficiency, general-purpose embedded processors are the preferred solution for many such applications due to the evolving nature of these applications and the high costs of custom IC design. The Sartori group discovers and exploits new opportunities for improving energy efficiency in general purpose embedded processors. They are currently focusing on new opportunities for energy efficiency enabled by detailed co-analysis of the design-level description of a processor and an application binary. Traditionally, co-analysis of the low-level hardware and details for a system has not been performed due to prohibitive costs. However, this group has developed anutomated analysis tools that perform unique analyses and expose new opportunities for energy efficiency. A few of their ongoing projects in this area are described below.

  • The group has created a tool that identifies the parts of a processor that can never be exercised by a particular application. As such, they can identify paths in a processor that can never be exercised for a particular workload. If the most critical paths in a processor are not exercised, then extra timing slack exists that can be exploited to reduce power or increase performance.
  • Knowing the parts of a processor that can never be exercised by an application or application phase also allows new opportunities for aggressive power gating. The researchers are developing techniques that allow the benefits of aggressive hardware-based power gating with costs similar to those of software power gating. Their techniques can provide guarantees that power gating decisions are safe without requiring hardware checking mechanisms and provide near-optimal power savings, compared to oracular control decisions.
  • Detailed activity analysis and guarantees for a hardware-software system can also allow researchers to more accurately and aggressively bound the peak power requirements of the system. The gap between conventional peak power rating and application-aware peak power rating can be exploited for reduced energy and area, improved performance and throughput, and greater efficiency.

The new techniques this group is creating require detailed analysis of a system's hardware and software. This detailed analysis relies on high-throughput parallel simulation methodologies to be performed in a reasonable amount of time. As such, it relies on high-performance parallel computing resources.

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Group name: 
sartorij

Supercomputing for Geospatial Data Processing

MSI PI Eric Shook (assistant professor; Geography, Environment and Society ) is featured on the College of Liberal Arts (CLA) website as part of a series about CLA faculty who use big data. You can read the article on the CLA website: Superpowered GIS . Professor Shook uses MSI’s supercomputers to...

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