Abstract: A data space profiler may include an analysis engine that associates runtime events of profiled software applications with execution costs and extended address elements. Relational agents in the analysis engine may apply functions to profile data collected for each event to determine the extended address element values to be associated with the event. Each extended address element may correspond to a data profiling object (e.g., hardware component, software construct, data allocation construct, abstract view) involved in each event. The extended address element values may be used to index into an event set for the profiled software application to present costs from the perspective of these profiling objects. A filtering mechanism may also be used to extract profile data from the event set corresponding to events that satisfy the filter criteria. By alternating between presentation of profiling object views and filtered event data, performance bottlenecks and their causes may be identified.

Abstract: A system and method for profiling a software application may include means for capturing profiling information corresponding to an instruction identified as having executed coincident with the occurrence of a runtime event, and for associating the profiling information with the event in an event set. In some embodiments, the identified instruction, which may have triggered the event, may be located in the program code sequence at a predetermined position relative to the current program counter value at the time the event was detected. The predetermined relative position may be fixed dependent on the processor architecture and may also be dependent on the event type. The predetermined relative position may be zero, indicating that when the event was detected, the program counter value corresponded to an instruction associated with the event. If the identified instruction is an ambiguity-creating instruction, an indication of ambiguity may be associated with the event.

Abstract: A data space profiler may include a graphical user interface (GUI) for sorting, aggregating and displaying profile data associated with runtime events of a profiled software application. This profile data may include costs associated with events as well as extended address elements and other code behavior attributes associated with them. The GUI may include means for selecting a perspective from which cost data is to be presented as well as presentation options for displaying the data. The presentation options may include panning and zooming options, which may determine how the data is sorted and/or aggregated for display. The GUI may also include means for specifying filter criteria, which may be used to determine which data to display. By providing means to alternate the display of profile data according to different perspectives and filtering criteria, the GUI may facilitate identification of performance bottlenecks of the profiled application and the causes thereof.

Abstract: Correlating profile data facilitates sophisticated code optimization. Going beyond one to one relationships between code execution hindrances and single code behavior attributes provides insight into code behavior at a finer level of granularity. The capability to aggregate profile data based on multiple code behavior attributes and filter based on instances thereof, allows code optimization decisions to be made based on presentation of profile data from various perspectives. Profile data, which includes code behavior attributes correlated with code execution hindrances, is aggregated based at least in part on a first code behavior attribute. Code behavior attributes include one or more of memory references, memory reference objects, functions, time ranges, processors, processes, threads, and source-level data objects. The aggregated profile data is filtered based on an instance of the first code behavior attribute.

Abstract: Data address profiling allows determination of sources of code execution hindrance with different perspectives of memory references and allows correlation of sampled runtime events and memory reference objects, such as cache lines. Associating sampled runtime events with data addresses provides for efficient and targeted optimization of code with respect to data addresses and physical and/or logical memory reference objects (e.g., memory segments, heap variables, variable instances, stack variables, etc.). An instruction instance is identified in relation to a sampled runtime event. A data address is determined from the instruction instance. From the determined address, a memory reference object is ascertained.

Abstract: Including source-level data object information in code profiling data enhances code optimization because it provides new perspectives to view code behavior. A method provides for identifying an operation instance of code that corresponds to a runtime event, which is detected in execution of the code. The detected event is attributed to a source-level data object that corresponds to a source-level representation of a language construct. The attribution is based on a predefined association between the identified operation instance and the language construct of the source-level representation that corresponds to the source-level data object.

Abstract: A data space profiler may include a graphical user interface (GUI) for sorting, aggregating and displaying profile data associated with runtime events of a profiled software application. This profile data may include costs associated with events as well as extended address elements and other code behavior attributes associated with them. The GUI may include means for selecting a perspective from which cost data is to be presented as well as presentation options for displaying the data. The presentation options may include panning and zooming options, which may determine how the data is sorted and/or aggregated for display. The GUI may also include means for specifying filter criteria, which may be used to determine which data to display. By providing means to alternate the display of profile data according to different perspectives and filtering criteria, the GUI may facilitate identification of performance bottlenecks of the profiled application and the causes thereof.

Abstract: A data space profiler may include an analysis engine that associates runtime events of profiled software applications with execution costs and extended address elements. Relational agents in the analysis engine may apply functions to profile data collected for each event to determine the extended address element values to be associated with the event. Each extended address element may correspond to a data profiling object (e.g., hardware component, software construct, data allocation construct, abstract view) involved in each event. The extended address element values may be used to index into an event set for the profiled software application to present costs from the perspective of these profiling objects. A filtering mechanism may also be used to extract profile data from the event set corresponding to events that satisfy the filter criteria. By alternating between presentation of profiling object views and filtered event data, performance bottlenecks and their causes may be identified.

Abstract: A system and method for profiling a software application may include means for capturing profiling information corresponding to an instruction identified as having executed coincident with the occurrence of a runtime event, and for associating the profiling information with the event in an event set. In some embodiments, the identified instruction, which may have triggered the event, may be located in the program code sequence at a predetermined position relative to the current program counter value at the time the event was detected. The predetermined relative position may be fixed dependent on the processor architecture and may also be dependent on the event type. The predetermined relative position may be zero, indicating that when the event was detected, the program counter value corresponded to an instruction associated with the event. If the identified instruction is an ambiguity-creating instruction, an indication of ambiguity may be associated with the event.