Modern real-time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst-case execution time analysis, often fail to capture the fine-grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems.

翻译：现代实时系统需要精确表征任务的时序行为以确保可预测的性能，尤其是在复杂硬件架构上。现有方法（如最坏情况执行时间分析）通常无法捕捉任务在不同资源上下文（例如缓存、内存带宽和CPU频率的分配）下的细粒度时序行为，而这正是实现高效资源利用所必需的。本文提出了一种新颖的生成式剖析方法，能够合成上下文相关的细粒度时序画像，涵盖实时任务在未测量资源分配下的情况。我们的方法利用非参数条件式多边缘薛定谔桥（MSB）公式，在最大似然保证下为未见过的资源上下文生成准确的执行画像。通过真实世界基准测试，我们证明了该方法的效率与有效性，并在一个典型的实时系统自适应多核资源分配案例研究中展示了其实用价值。