Static task partitioning for locked caches in multicore real-time systems

Growing processing demand on multi-tasking real-time systems can be met by employing scalable multi-core architectures. For such environments, locking cache lines for hard real-time systems ensures timing predictability of data references and may lower worst-case execution time. This work studies the benefits of cache locking on massive multicore architectures with private caches in the context of hard real-time sys-tems. In shared cache architectures, the cache is a single resource shared among all the tasks. However, in scalable cache architectures with private caches, conflicts exist only among the tasks scheduled on one core. This calls for a cache-aware allocation of tasks onto cores. The objective of this work is to increase the predictability of memory accesses resolved by caches while reducing the number of cores for a given task set. This allows designers to reduce the footprint of their subsystem of real-time tasks and thereby cost, either by choosing a product with fewer cores as a target or to allow more subsystems to be co-located on a given fixed number of cores. Our work proposes a novel variant of the cache-unaware First Fit Decreasing (FFD) algorithm called Naive locked First Fit Decreasing (NFFD) policy. We propose two cache-aware static scheduling schemes: (1) Greedy First Fit Decreasing (GFFD) and (2) Colored First Fit Decreasing (CoFFD) for task sets where tasks do not have intra-task conflicts among locked regions (Scenario A). NFFD is capable of scheduling hig