This study examined the interplay among processor speed, cluster interconnect and file I/O, using parallel applications to quantify interactions. We focused on a common case where multiple compute nodes communicate with a single master node for file accesses. We constructed a predictive model that used time characteristics critical for application performance to estimate the number of nodes beyond which further performance improvement became unattainable. Predictions were experimentally validated with NAMD, a representative parallel application designed for molecular dynamics simulation. Such predictions can help guide decision making to improve machine allocations for parallel codes in large clusters.

Inadequate I/O performance remains a major challenge in using high-end computing systems effectively. To address this problem, we present TsModeler, an automatic time series modeling and prediction framework for adaptive I/O prefetching that uses ARIMA time series models to predict the temporal patterns of I/O requests. These online pattern analysis techniques and cutoff indicators for autocorrelation patterns enable multistep online predictions suitable for multiblock prefetching. This work also combines time series predictions with spatial Markov model predictions to determine when, what, and how many blocks to prefetch. Experimental results show reductions in execution time compared to the standard Linux file system across various hardware configurations.

Bursty application I/O patterns, together with transfer limited storage devices, combine to create a major I/O bottleneck on parallel systems. This paper explores the use of time series models to forecast application I/O request times, then prefetching I/O requests during computation intervals to hide I/O latency. Experimental results with I/O intensive scientific codes show performance improvements compared to standard UNIX prefetching strategies.