When adopting multi-core systems for safety-critical applications, certification requirements mandate bounding the delays incurred in accessing shared resources. This is the case of global memories, whose access is often regulated by memory controllers optimized for average-case performance and not designed to be predictable. As a consequence, worst-case bounds on memory access delays often result to be too pessimistic, drastically reducing the advantage of having multiple cores. This paper proposes a fine-grained analysis of the memory contention experienced by parallel tasks running on a multi-core platform. To this end, an optimization problem is formulated to bound the memory interference by leveraging a three-phase execution model and holistically considering multiple memory transactions issued during each phase. Experimental results show the advantage in adopting the proposed approach on both synthetic task sets and benchmarks.
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