Cache Predict ML: Big Data ML Models for Predicting Cache Behavior in Multi-Core Processor Architectures

Quite often, cache performance is a deterministic factor of throughput, latency, and power efficiency in current multi-core processor architecture specially when it is subject to data-intensive routines in scientific computing, artificial intelligence, and large-scale cloud workloads. Existing machine learning techniques are effective in some cases but often inaccurate or difficult to scale to large workload sets because they require training on the computing platform whereas traditional cache simulators, though accurate, are far too slow to simulate multi-terabyte execution traces. This study proposes CachePredictML, a machine learning system that uses big data to predict the similarities of the cache behavior on multi-core processors. The framework can use a distributed preprocessing pipeline using Apache Spark and HDFS to process up to 4.6 TB of memory access traces of SPEC CPU2017, PARSEC 3.0 and SPLASH-2 benchmarks being run on 16-core Intel Xeon Platinum 8380 and 64-core AMD EPYC 7742 processors. Our Hybrid-Grid solution extracts temporal locality, spatial locality, and reuse distance, and with workload characterization using Random Forest, Gradient Boosting and LSTM networks, plus Graph Neural Networks to model coherence patterns, workload characterization is provided. VO results indicate that CachePredictML delivered 92.34 percent accuracy in prediction, 4.5 percent error in predicting miss ratio, and a 13.4-h reduction in simulation time in contrast to existing state of art ML-based cache predictors. These results demonstrate the power of CachePredictML to speed-up the design-space exploration process, help to tune the cache management policies, and further enable scaleable prediction in a multi-core environment.