Parallel and Distributed Computing

Multiple computers clustered and interconnected by networks, applications and concurrent processes opens scientists into an expansive realm of exploration that includes:

  • The intersection of mobile and pervasive computing in healthcare, user acceptance of ubiquitous and mobile computing technologies
  • Compiler techniques to eliminate performance bottlenecks in high-level programming languages with focus on MATLAB and Ruby.
  • Software component systems for scientific computing, and in particular the XCAT, Indiana University’s distributed computing implementation of the Common Component Architecture specification (more»)
  • FutureGrid – a new facility to enable development of new approaches to computing, Multicore, Grid Computing, Open Grid Computing Environment
  • Open Systems Lab, Open Message Passing Interface, LAM (Local Area Multicomputer) implementation of MPI, Boost Graph Library, Parallel Boost Graph Library, Interative Template Library, Matirix Template Library (more»)
  • Long-term preservation and access to scientific data, enabling computational access to large-scale data, tools for metadata and provenance capture, data repositories, cyberinfrastructure for large-scale data analysis, geoinformatics
  • Data-intensive computing at the intersection of Cloud and multicore technologies with an emphasis on life science applications using MapReduce and traditional parallel and distributed computing approaches (more»)
  • Object-oriented programming, Java, the open-source movement, data mining, spatial interfaces, and adaptive software

Faculty in this area include:
Ariful Azad, Randall Bramley, Arun Chauhan, Geoffrey Charles Fox, Andrew Lumsdaine, Ryan Newton, Judy Qiu, Gregory J. E. Rawlins, Prateek Sharma, Jeremy Siek, Thomas Sterling, Martin Swany, R. Clint Whaley, Grigory Yaroslavtsev