Performance Comparison of Prefetching and Placement Policies using Parallel I/O???

We present a simulation study of several prefetching policies to improve the I/O performance of external merging using parallel I/O. In particular we consider traditional sequential prefetch, forecast-based greedy prefetching, and oblivious prefetching. In conjunction with the prefetching policies we evaluate the bene t of two di erent data placement strategies: run-level striping and block-random placement, in the presence of data skew. We show that the I/O performance is greatly improved by using forecasting techniques. This method outperforms the other policies in achieving higher disk parallelism, and scales well with increased numbers of disks and increasing data skew. Additionally, the performance of block-random data placement is shown to be uniformly good, independent of the data skew, while the performance of run-level striped placement degrades with increasing skew.