Can We Guarantee Performance Requirements under Workload and Process Variations?

Modern many-core systems must cope with a wide range of heterogeneity due to both manufacturing process variations and extreme requirements of multi-application, multithreaded workloads. The latter is increasingly challenging in the context of different performance constraints per multithreaded application. Existing thread mapping methods primarily focus on maximizing performance under a global power budget, failing to provide thread- and application-specific performance guarantees. This paper provides a comprehensive approach for variation- and workload-aware thread mapping on heterogeneous multi-core systems that satisfies per-application performance requirements and is manufacturing process variation-aware, while providing an analysis of its robustness to uncertainties in the power and performance models. We formulate the variation-aware mapping problem as a constrained 0-1 integer linear program (ILP) and we propose a heuristic-based algorithm for efficiently solving it. Compared with an optimal solver, our method produces results less than 10% away from optimum on average, with four orders of magnitude improvement in runtime. Moreover, the newly proposed method is robust to model uncertainty and in meeting per application performance requirements, while agnostic approaches result in performance bound violations (up to 100% in many cases).