A Simple and Efficient Parallel Disk Mergesort

The original publication is available at www.springerlink.comExternal sorting|the process of sorting a le that is too large to t into the computer's internal memory and must be stored externally on disks|is a fundamental subroutine in database sys- tems [Gra93, IBM90]. Of prime importance are techniques that use multiple disks in parallel in order to speed up the performance of external sorting. The simple randomized merging (SRM) mergesort algorithm proposed by Barve et al. [BGV97] is the rst parallel disk sorting algorithm that requires a provably optimal number of passes and that is fast in practice. Knuth [Knu98, Section 5.4.9] recently identi ed SRM (which he calls \randomized striping") as the method of choice for sorting with parallel disks. In this paper we present an eÆcient implementation of SRM, based upon novel and elegant data structures. We give a new implementation for SRM's lookahead forecasting technique for parallel prefetching and its forecast and ush technique for bu er management. Our techniques amount to a signi cant improvement in the way SRM carries out the parallel, independent disk accesses necessary to read blocks of input runs eÆciently during external merging. Our implementation is based on synchronous parallel I/O primitives provided by the TPIE programming environment [TPI99]; whenever our program issues an I/O read (write) operation, one block of data is synchronously read from (written to) each disk in parallel. We compare the performance of SRM over a wide range of input sizes with that of disk-striped mergesort (DSM), which is widely used in practice. DSM consists of a standard mergesort in conjunction with striped I/O for parallel disk access. SRM merges together signi cantly more runs at a time compared with DSM, and thus it requires fewer merge passes. We demonstrate in practical scenarios that even though the streaming speeds for merging with DSM are a little higher than those for SRM (since DSM merges fewer runs at a time), sorting using SRM is often signi cantly faster than with DSM (since SRM requires fewer passes). The techniques in this paper can be generalized to meet the load-balancing requirements of other applications using parallel disks, including distribution sort and multiway partitioning of a le into several other les. Since both parallel disk merging and multimedia processing deal with streams that get \consumed" at nonuniform and partially predictable rates, our techniques for lookahead based upon forecasting data may have relevance in video server applications