Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization

The problem is that existing architectural simulators expose aggregate metrics or raw traces but fail to reveal complex interactions among microarchitectural events and their relationship to program execution. Microflow introduces an observability framework that elevates causality to a first-class analytical object by transforming execution traces into the Microflow Intermediate Representation (MFIR), which explicitly captures dependencies across software semantics, instructions, microarchitectural events, and hardware resources. Experimental evidence on two SPEC CPU 2017 benchmarks demonstrates that Microflow precisely attributes stalls, reveals unobservable phenomena, and enables exact critical-path decomposition, uncovering hidden misprediction costs in leela and cross-loop-iteration contention in mcf. This matters because making causality queryable provides a strong foundation for systematic performance analysis and hardware-software co-design, enabling architects to attribute performance symptoms to root causes across abstraction layers.