Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: Provided are methods and systems for inter-procedural optimization (IPO). A new IPO architecture (referred to as “ThinLTO”) is designed to address the weaknesses and limitations of existing IPO approaches, such as traditional Link Time Optimization (LTO) and Lightweight Inter-Procedural Optimization (LIPO), and become a new link-time-optimization standard. With ThinLTO, demand-driven and summary-based fine grain importing maximizes the potential of Cross-Module Optimization (CMO), which enables as much useful CMO as possible ThinLTO also provides for global indexing, which enables fast function importing; parallelizes some performance-critical but expensive inter-procedural analyses and transformations; utilizes demand-driven, lazy importing of debug information that minimizes memory consumption for the debug build; and allows easy integration of third-party distributed build systems. In addition, ThinLTO may also be implemented using an IPO server, thereby removing the need for the serial step.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.

Abstract: Provided are methods and systems for inter-procedural optimization (IPO). A new IPO architecture (referred to as “ThinLTO”) is designed to address the weaknesses and limitations of existing IPO approaches, such as traditional Link Time Optimization (LTO) and Lightweight Inter-Procedural Optimization (LIPO), and become a new link-time-optimization standard. With ThinLTO, demand-driven and summary-based fine grain importing maximizes the potential of Cross-Module Optimization (CMO), which enables as much useful CMO as possible ThinLTO also provides for global indexing, which enables fast function importing; parallelizes some performance-critical but expensive inter-procedural analyses and transformations; utilizes demand-driven, lazy importing of debug information that minimizes memory consumption for the debug build; and allows easy integration of third-party distributed build systems. In addition, ThinLTO may also be implemented using an IPO server, thereby removing the need for the serial step.

Abstract: A method for using profiling to obtain application-specific, preferred parameter values for an application is disclosed. First, a parameter for which to obtain an application-specific value is identified. Code is then augmented for application-specific profiling of the parameter. The parameter is profiled and profile data is collected. The profile data is then analyzed to determine the application's preferred parameter value for the profile parameter.