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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%.

Purpose: To develop a web environment where the TG142 proposed test actions and results can be entered and retrieved for review and electronic approval. Methods and Materials: AAPM Task Group 142 published recently a report with recommendations and tests for a comprehensive quality assurance program for external beam radiation therapy for linear accelerators. The recommendations address frequency and tolerances for the proposed tests. In addition, there are qualifiers for tests that are specific to linacs that are used for IMRT and stereotactic procedures. We have developed a web based environment using the asp.net tools where the TG142 tests have been implemented for the recommended frequencies for seven different linear accelerators in our institution. The end user (therapist or medical physicist) has a unique login and password and follows the prompts of the web page to complete each test. Baselines and tolerances are built into the program and data analysis is performed automatically, reporting a pass or fail result. Data trend analysis can be plotted and the medical physicist can review and approve the qa results electronically. The pdf report of the qa analysis can be printed or uploaded to the RV system as part of an EMR QA environment. Results: We have implemented this web based QA tool in our department with great success. The sequence ofmorning qa tasks has also been optimized so that the entire suite of tests per TG142 takes no more than 20min to perform. Electronic approval of the QA is available only to the faculty physicist. Once the document is approved, it is automatically uploaded to Mosaiq under the patient name corresponding to the machine name. Conclusion: A web based TG142 qa tool was developed and implemented in our clinic, allowing for data entry, analysis, trend plotting and electronic approval.

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