  This thesis introduces PEMS2, an improvement to PEMS (Parallel External
Memory System). PEMS executes Bulk-Synchronous Parallel (BSP) algorithms in an
External Memory (EM) context, enabling computation with very large data sets
which exceed the size of main memory. Many parallel algorithms have been
designed and implemented for Bulk-Synchronous Parallel models of computation.
Such algorithms generally assume that the entire data set is stored in main
memory at once. PEMS overcomes this limitation without requiring any
modification to the algorithm by using disk space as memory for additional
"virtual processors". Previous work has shown this to be a promising approach
which scales well as computational resources (i.e. processors and disks) are
added. However, the technique incurs significant overhead when compared with
purpose-built EM algorithms. PEMS2 introduces refinements to the simulation
process intended to reduce this overhead as well as the amount of disk space
required to run the simulation. New functionality is also introduced, including
asynchronous I/O and support for multi-core processors. Experimental results
show that these changes significantly improve the runtime of the simulation.
PEMS2 narrows the performance gap between simulated BSP algorithms and their
hand-crafted EM counterparts, providing a practical system for using BSP
algorithms with data sets which exceed the size of RAM.

Distributed, Parallel, and Cluster Computing;Data Structures and Algorithms