Adaptive technique for automatic communication access pattern discovery applied to data prefetching in distributed applications using neural networks and stochastic models

The distributed computing performance is usually limited by the data transfer rate and access latency. Techniques such as data caching and prefetching were developed to overcome this limitation. However, such techniques require the knowledge of application behavior in order to be effective. In this sense, we propose new application communication behavior discovery techniques that, by classifying and analyzing application access patterns, is able to predict future application data accesses. The proposed techniques use stochastic methods for application state change prediction and neural networks for access pattern discovery based on execution history, and is evaluated using the NAS Parallel Benchmark suite.