Conventional solutions for I/O have attempted to provide hardware
and software parallelism via RAIDs or parallel machines/supercomputers.
However, the problems associated with cost, scalability,
and/or accessibility of these environments make them unattractive
for widespread usage. This research addresses this important
deficiency in high-performance I/O support, by proposing a shared storage
system using an off-the-shelf cluster of workstations, disks, and
networks. The proposed research goes beyond current state-of-the-art in I/O
support for clusters and examines a broad spectrum of
issues related to I/O software on clusters, that include
application-directed, compiler-directed, and runtime system-directed
optimizations. These optimizations are crucial to reduce/hide the
latencies to different levels of the I/O hierarchy which will help
accelerate the deployment of clusters for I/O-intensive applications.
|Effective start/end date||8/1/01 → 7/31/05|
- National Science Foundation: $239,527.00