Deep-Learning-Driven Prefetching for Far Memory

Modern software systems face increasing runtime performance demands, particularly in emerging architectures like far memory, where local-memory misses incur significant latency. While machine learning (ML) has proven effective in offline systems optimization, its application to high-frequency, runtime-level problems remains limited due to strict performance, generalization, and integration constraints. We present FarSight, a Linux-based far-memory system that leverages deep learning (DL) to efficiently perform accurate data prefetching. FarSight separates application semantics from runtime memory layout, allowing offline-trained DL models to predict access patterns using a compact vocabulary of ordinal possibilities, resolved at runtime through lightweight mapping structures. By combining asynchronous inference, lookahead prediction, and a cache-resident DL model, FarSight achieves high prediction accuracy with low runtime overhead. Our evaluation of FarSight on four data-intensive workloads shows that it outperforms the state-of-the-art far-memory system by up to 3.6 times. Overall, this work demonstrates the feasibility and advantages of applying modern ML techniques to complex, performance-critical software runtime problems.