A Cost-Benefit Scheme for High Performance Predictive Prefetching

High-performance computing systems will increasingly rely on prefetching data from disk to overcome long disk access times and maintain high utilization of parallel I/O systems. This paper evaluates a prefetching technique that chooses which blocks to prefetch based on their probability of access and decides whether to prefetch a particular block at a given time using a cost-benefit analysis. The algorithm uses a probability tree to record past accesses and to predict future access patterns. We simulate this prefetching algorithm with a variety of I/O traces. We show that our predictive prefetching scheme combined with simple one-block-lookahead prefetching produces good performance for a variety of workloads. The scheme reduces file cache miss rates by up to 36% for workloads that receive no benefit from sequential prefetching. We showthat the memory requirements for building the probability tree are reasonable, requiring about a megabyte for good performance. The probability tree constructed by the prefetching scheme predicts around 60-70% of the accesses. Next, we discuss ways of improving the performance of the prefetching scheme. Finally, we show that the cost-benefit analysis enables the tree-based prefetching scheme to perform an optimal amount of prefetching.