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 Fox, Andrew Lumsdaine, Ryan Newton, Judy Qiu, Gregory Rawlins, Prateek Sharma, Jeremy Siek, Thomas Sterling, Martin Swany, Clint Whaley, Grigory Yaroslavtsev