A Small and Effective Data Cache for Real-Time Multitasking Systems

In multitasking real-time systems, the WCET of each task and also the effects of interferences between tasks in the worst-case scenario need to be calculated. This is especially complex with data caches. In this paper, we propose a small instruction-driven data cache (256 bytes) that effectively exploits locality. It works by preselecting a subset of memory instructions that will have data cache replacement permission. Selection of such instructions is based on data reuse theory. Since each selected memory instruction replaces its own data cache line, it prevents pollution and performance in tasks becomes independent of the size of the associated data structures. We have modeled several memory configurations using the Lock-MS WCET analysis method. Our results show that, on average, our data cache effectively services 88% of program data. Such results translate into doubling the performance of the tested real-time multitasking experiments, which (increasing from 75 to 89%) approaches the ideal case of always hitting in instruction and data caches. Additionally, we show that using partitioning on our proposed hardware only provides marginal benefits.