High Performance I/O
Many scientific problems make use of very large data sets. Examples include land
cover dynamics, submarine structural acoustics, analysis of earth observation data,
computational biology, computational quantum chemistry, seismic data processing etc. The
goals of our research are to determine the I/O requirements of such applications and to
develop compile-time and run-time techniques to optimize their performance on
multiprocessor architectures with multiple disks or disk arrays. Currently, the major
driving applications for this work are the University of Maryland's Land Cover Dynamics
Grand Challenge project, submarine structural acoustics and analysis of the data generated
by the Earth Observation System project which is a part
of NASA's Mission to Planet Earth. This research is being carried out in collaboration
with the Scalable I/O consortium and CRPC.
and characterization of data-intensive applicationsthe goal of this project is to
find out it takes to achieve good I/O performance for real-life data-intensive tasks on
parallel machines suitable for I/O.
compiler analysis for overlapping large I/O operations with computation the goal
of this project is to schedule large I/O operations which are beyond the capacity of
standard operating system prefetch and write-behind.
and resource management for I/O intensive tasks on peer-to-peer systems the goal
of this project is to develop techniques for achieving high performance for I/O intensive
tasks on peer-to-peer systems.
placement techniques for multidimensional datasets on large disk farms the goal of
this project is to develop data placement techniques for multidimensional datasets both
across multiple disks and within individual disks to minimize query response time.
NSF/ARPA Grand Challenge
project on Land Cover Dynamics this project focuses on employing high performance
computing for applications in remote sensing, specifically applications in land cover
dynamics. Understanding land cover dynamics is one of the most important challenges in the
study of global change. Research involves developing scalable and portable programs for a
variety of image and map data processing applications, eventually ntegrated with new
models for parallel I/O of large scale images and maps.
Publications and Talks
Interprocedural Framework for Placement of Asynchronous I/O Operations, Gagan
Agarwal, Anurag Acharya, Joel Saltz. ICS'96
of Scalable Declustering Algorithms for Parallel Grid Files Bongki Moon,
Anurag Acharya, Joel Saltz IPPS'96
the Performance of I/O-Intensive Parallel Applications Anurag Acharya,
Mustafa Uysal, Robert Bennett, Assaf Mendelson, Michael Beynon, Jeff Hollingsworth, Joel
Saltz, Alan Sussman. IOPADS'96
A Framework for Optimizing Parallel I/O Robert Bennett, Kelvin Bryant, Alan
Sussman, Raja Das, Joel Saltz, Proceedings of the 1994 Scalable Parallel Libraries
the Grand Challenge Meeting, July 1994