To address the growing difficulty of performance debugging on modern processors with increasingly complex micro-architectures, we present Hierarchical Cycle Accounting (HCA), a structured, hierarchical, architecture-agnostic methodology for the identification of performance issues in workloads running on these modern processors. HCA reports to the user the cost of a number of execution components, such as load latency, memory bandwidth, instruction starvation, and branch misprediction. A critical novel feature of HCA is that all cost components are presented in the same unit, core pipeline cycles. Their relative importance can therefore be compared directly. These cost components are furthermore presented in a hierarchical fashion, with architecture-agnostic components at the top levels of the hierarchy and architecture-specific components at the bottom. This hierarchical structure is useful in guiding the performance debugging effort to the places where it can be the most effective. For a given architecture, the cost components are computed based on the observation of architecture-specific events, typically provided by a performance monitoring unit (PMU), and using a set of formulas to attribute a certain cost in cycles to each event. The selection of what PMU events to use, their validation, and the derivation of the formulas are done offline by an architecture expert, thereby freeing the non-expert from the burdensome and error-prone task of directly interpreting PMU data. We have implemented the HCA methodology in Gooda, a publicly available tool. We describe the application of Gooda to the analysis of several workloads in wide use, showing how HCA's features facilitated performance debugging for these applications. We also describe the discovery of relevant bugs in Intel hardware and the Linux Kernel as a result of using HCA.