Cache locking is an effective technique to improve timing predictability in real-time systems. In static cache locking, the locked memory blocks remain unchanged throughout the program execution. Thus static locking may not be effective for large programs where multiple memory blocks are competing for few cache lines available for locking. In comparison, dynamic cache locking overcomes cache space limitation through time-multiplexing of locked memory blocks. Prior dynamic locking technique partitions the program into regions and takes independent locking decisions for each region. We propose a flexible loop-based dynamic cache locking approach. We not only select the memory blocks to be locked but also the locking points (e.g., loop level). We judiciously allow memory blocks from the same loop to be locked at different program points for WCET improvement. We design a constraint-based approach that incorporates a global view to decide on the number of locking slots at each loop entry point and then select the memory blocks to be locked for each loop. Experimental evaluation shows that our dynamic cache locking approach achieves substantial improvement of WCET compared to prior techniques.

The Worst-Case Response Time (WCRT) of multi-tasking applications running on multi-cores is an important metric for real-time embedded systems. The WCRT is determined by the mapping of the tasks to the cores (which determines load balancing) and the Worst-Case Execution Time (WCET) of the tasks. However, the WCET of a task is also influenced by the conflicts in the shared cache from concurrently executing tasks on other cores in a multi-core system. In other words, the mapping of the tasks to the cores indirectly influences the WCET of the tasks, which in turn impacts the WCRT of the entire application. Thus the mapping of the tasks to the cores should simultaneously maximize workload balance and minimize shared cache interference. We propose an integer-linear programming (ILP) formulation to achieve this objective. Experimental evaluation shows that shared cache aware task mapping achieves on an average 25% and 33% WCRT reduction for real-life and synthetic applications, respectively, compared to traditional approach that is agnostic to shared cache conflicts and solely focuses on load balancing.

Cache locking improves timing predictability at the cost of performance. We explore a novel approach that opportunistically employs both cache analysis and locking to enhance schedulability in preemptive multi-tasking real-time systems. The cache is spatially shared among the tasks by statically locking a portion of the cache per task. To overcome the issue of limited cache space per task, we keep a portion of the cache unlocked and let all the tasks use it through time-multiplexing. Compared to locking the entire cache for each task during execution, our approach obviates the cost of reloading locked blocks at preemption. But we require static cache analysis for WCET estimation and cache related preemption delay (CRPD) analysis of the unlocked cache space. We design an algorithm to make appropriate locking decisions through accurate cost-benefit analysis. Experimental results show that our integrated approach leads to substantially improved schedulability results compared to cache analysis and cache locking employed individually.

Caches play an important role in embedded systems by bridging the performance gap between high speed processors and slow memory. At the same time, caches introduce imprecision in Worst-case Execution Time (WCET) estimation due to unpredictable access latencies. Modern embedded processors often include cache locking mechanism for better timing predictability. As the cache contents are statically known, memory access latencies are predictable, leading to precise WCET estimate. Moreover, by carefully selecting the memory blocks to be locked, WCET estimate can be reduced compared to cache modeling without locking. Existing static instruction cache locking techniques strive to lock the entire cache to minimize the WCET. We observe that such aggressive locking mechanisms may have negative impact on the overall WCET as some memory blocks with predictable access behavior get excluded from the cache. We introduce a partial cache locking mechanism that has the flexibility to lock only a fraction of the cache. We judiciously select the memory blocks for locking through accurate cache modeling that determines the impact of the decision on the program WCET. Our synergistic cache modeling and locking mechanism achieves substantial reduction in WCET for a large number of embedded benchmark applications.