Shared Data Kills Real-Time Cache Analysis. How to Resurrect It?

While data sharing is becoming a necessity in modern multi-core real-time systems, it complicates system analyzability and leads to significantly pessimistic latency bounds. This work is a step towards facilitating high-performance and coherent data sharing in real-time systems by tackling two main problems. The first is a well-acknowledged one: shared caches render cache analysis techniques useless and all cache accesses have to be assumed misses. The second is a new one, where we show that coherence interference voids classical cache analysis techniques. We contribute a solution that tackles both problems by leveraging time-based cache coherence and a novel methodology to integrate its effect into cache analysis. Thanks to this solution, we enable the usage of shared memory hierarchy with coherent shared data, while we prove that we are able to restore cache analysis; and hence, provide much tighter memory latency bounds.