Several algorithms for parallel disk systems have appeared in the literature recently, and they are asymptotically optimal in terms of the number of disk accesses. Scalable systems with parallel disks must be able to run these algorithms. We present for the first time a list of capabilities that must be provided by the system to support these optimal algorithms: control over declustering, querying about the configuration, independent I/O, and turning off parity, file caching, and prefetching. We summarize recent theoretical and empirical work that justifies the need for these capabilities. In addition, we sketch an organization for a parallel file interface with low-level primitives and higher-level operations.

Although several algorithms have been developed for the Parallel Disk Model (PDM), few have beenimplemented. Consequently, little has been known about the accuracy of thePDMin measuring I/O time and total running time toperform an out-of-core computation. This paper analyzes timing results on multiple-disk platforms fortwo PDM algorithms, out-of-core radix sort and BMMC permutations, to determine the strengths and weaknesses of thePDM. The results indicate the following. First, good PDM algorithms are usually not I/O bound. Second, of the four PDM parameters, one (problem size) is a good indicator of I/O time and running time, one (memory size) is a good indicator of I/O time but not necessarily running time, and the other two (block size and number of disks) do not necessarily indicate either I/O or running time. Third, because PDM algorithms tendnottobeI/Obound, using asynchronous I/O can reduce I/O wait times signi cantly. The software interface to the PDM is part of the ViC * run-time library. The interface is a set of wrappers that are designed to be both e cient and portable across several underlying le systems and target machines. 1