Program execution traces provide the most intimate details of a program's dynamic behavior. They can be used for program optimization, failure diagnosis, collecting software metrics like coverage, test prioritization, etc. Two major obstacles to exploiting the full potential of information they provide are: (i) performance overhead while collecting traces, and (ii) significant size of traces even for short execution scenarios. Reducing information output in an execution trace can reduce both performance overhead and the size of traces. However, the applicability of such traces is limited to a particular task. We present a runtime framework with a goal of collecting a complete, machine-and task-independent, user-mode trace of a program's execution that can be re-simulated deterministically with full fidelity down to the instruction level. The framework has reasonable runtime overhead and by using a novel compression scheme, we significantly reduce the size of traces. Our framework enables building a wide variety of tools for understanding program behavior. As examples of the applicability of our framework, we present a program analysis and a data locality profiling tool. Our program analysis tool is a time travel debugger that enables a developer to debug in both forward and backward direction over an execution trace with nearly all information available as in a regular debugging session. Our profiling tool has been used to improve data locality and reduce the dynamic working sets of real world applications.

No abstract

Large applications are typically partitioned into separately compiled modules. Large performance gains in these applications are available by optimizing across module boundaries. One barrier to applying crossmodule optimization (CMO) to large applications is the potentially enormous amount of time and space consumed by the optimization process.We describe a framework for scalable CMO that provides large gains in performance on applications that contain millions of lines of code. Two major techniques are described. First, careful management of in-memory data structures results in sub-linear memory occupancy when compared to the number of lines of code being optimized. Second, profile data is used to focus optimization effort on the performance-critical portions of applications. We also present practical issues that arise in deploying this framework in a production environment. These issues include debuggability and compatibility with existing development tools, such as make . Our framework is deployed in Hewlett-Packard's (HP) UNIX compiler products and speeds up shipped independent software vendors' applications by as much as 71%.