Stack Distance Histogram for Cache Miss Rate Prediction

The patent segments the memory reference trace into smaller units and uses sampling techniques to analyze only representative portions of the trace. By dividing the full trace into multiple segments and selecting samples from each, the system achieves accurate cache miss rate predictions without processing the entire trace, thus reducing time consumption while maintaining precision.

The patent creates simplified copies or models of the cache organization being analyzed. Instead of simulating the full complex cache system with all its details, the invention uses abstracted representations that capture the essential behavior needed for accurate miss rate prediction, significantly reducing simulation time while preserving measurement accuracy.

The patent extracts only the essential characteristics and parameters from long memory reference traces that are necessary for accurate cache performance prediction. By identifying and extracting key features such as access patterns, temporal locality, and spatial locality metrics, the system achieves accurate predictions without storing or processing the complete trace data, dramatically reducing storage requirements.

The patent transforms the raw memory reference trace data into a different parameter space by computing statistical measures and characteristics. Instead of storing individual memory addresses and timestamps, the system converts the trace into aggregated parameters such as stack distance histograms, reference interval distributions, and locality metrics, which occupy far less storage space while preserving the information needed for accurate cache simulation.