The objective of this research is to characterize and manage lifetime reliability, microarchitectural performance, and power tradeoffs in multicore processors. This dissertation is comprised of three research themes; 1) modeling and simulation method of interacting multicore processor physics, 2) characterization and management of performance and lifetime reliability tradeoff, and 3) extending Amdahl’s Law for understanding lifetime reliability, performance, and energy efficiency of heterogeneous processors. With continued technology scaling, processor operations are increasingly dominated by multiple distinct physical phenomena and their coupled interactions. Understanding these behaviors requires the modeling of complex physical interactions. This dissertation first presents a novel simulation framework that orchestrates interactions between multiple physical models and microarchitecture simulators to enable research explorations at the intersection of application, microarchitecture, energy, power, thermal, and reliability. Using this framework, workload-induced variation of device degradation is characterized, and its impacts on processor lifetime and performance are analyzed. This research introduces a new metric to quantify performance-reliability tradeoff. Lastly, the theoretical models of heterogeneous multicore processors are proposed for understanding performance, energy efficiency, and lifetime reliability consequences. It is shown that these system metrics are governed by Amdahl’s Law and correlated as a function of processor composition, scheduling method, and Amdahl’s scaling factor. This dissertation highlights the importance of multidimensional analysis and extends the scope of microarchitectural studies by incorporating the physical aspects of processor operations and designs.

Emerging mobile platforms integrate heterogeneous, multicore processors to efficiently deal with the heterogeneity of data (in magnitude, type, and quality). The main goal is to achieve a high degree of energy-proportionality which corresponds with the nature and fluctuation of mobile workloads. Most existing power and energy consumption analyses of these architectures rely on simulation or static benchmarks neither of which truly reflects the type of workload the processors handle in reality. By contrast, we generate two types of stochastic workloads and employ four types of dynamic voltage and frequency scaling (DVFS) policies to investigate the energy proportionality and the dynamic power consumption characteristics of a heterogeneous processor architecture when operating in different configurations. The analysis illustrates, both qualitatively and quantitatively, that knowledge of the statistics of the incoming workload is critical to determine the appropriate processor configuration.