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- Cache Persistence-Aware Memory Bus Contention Analysis for Multicore SystemsPublication . Aftab Rashid, Syed; Nelissen, Geoffrey; Tovar, EduardoMemory bus contention strongly relates to the number of main memory requests generated by tasks running on different cores of a multicore platform, which, in turn, depends on the content of the cache memories during the execution of those tasks. Recent works have shown that due to cache persistence the memory access demand of multiple jobs of a task may not always be equal to its worst-case memory access demand in isolation. Analysis of the variable memory access demand of tasks due to cache persistence leads to significantly tighter worst-case response time (WCRT) of tasks.In this work, we show how the notion of cache persistence can be extended from single-core to multicore systems. In particular, we focus on analyzing the impact of cache persistence on the memory bus contention suffered by tasks executing on a multi-core platform considering both work conserving and non-work conserving bus arbitration policies. Experimental evaluation shows that cache persistence-aware analyses of bus arbitration policies increase the number of task sets deemed schedulable by up to 70 percentage points in comparison to their respective counterparts that do not account for cache persistence.
- A Holistic Memory Contention Analysis for Parallel Real-Time Tasks under Partitioned SchedulingPublication . Casini, Daniel; Biondi, Alessandro; Nelissen, Geoffrey; Buttazzo, GiorgioWhen 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.