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: By maintaining consistency of instruction or operation identification between code prepared for profiling and that prepared using profiling results, efficacy of profile-directed code optimizations can be improved. In particular, profile-directed optimizations based on stall statistics are facilitated in an environment in which correspondence maintained between (i) instructions or operations whose execution performance may be optimized (or which may provide an opportunity for optimization of other instructions or operations) and (ii) particular instructions or operations profiled.

Abstract: By maintaining consistency of instruction or operation identification between code prepared for profiling and that prepared using profiling results, efficacy of profile-directed code optimizations can be improved. In particular, profile-directed optimizations based on stall statistics are facilitated in an environment in which correspondence maintained between (i) instructions or operations whose execution performance may be optimized (or which may provide an opportunity for optimization of other instructions or operations) and (ii) particular instructions or operations profiled.

Abstract: Program code executed in an environment in which latency exists between an execution event and detection of the execution event may be profiled using a technique that includes backtracking from a point in a representation of the program code, which coincides with the detection toward a preceding operation associated with the execution event. Backtracking identifies the preceding operation at a displacement from the detection point unless an ambiguity creating location is disposed between the detection point and the preceding operation. In general, the relevant set of ambiguity creating locations is processor implementation dependent and program code specific; however, branch targets locations, entry points, and trap or interrupt handler locations are common examples. In some realizations, the techniques may be used to associate cache miss (or hit) information with execution of particular memory access instructions.