Sorting very large datasets is a key subroutine in almost any application that is built on top of a large database. Two ways to sort out-of-core data dominate the literature: merging-based algorithms and partitioning-based algorithms. Within these two paradigms, all the programs that sort out-of-core data on a cluster rely on assumptions about the input distribution. We propose a third way of out-of-core sorting: oblivious algorithms. In all, we have developed six programs that sort out-of-core data on a cluster. The first three programs, based completely on Leighton\u27s columnsort algorithm, have a restriction on the maximum problem size that they can sort. The other three programs relax this restriction; two are based on our original algorithmic extensions to columnsort. We present experimental results to show that our algorithms perform well. To the best of our knowledge, the programs presented in this thesis are the first to sort out-of-core data on a cluster without making any simplifying assumptions about the distribution of the data to be sorted
