Abstract: A method for application performance data collection includes steps or acts of: customizing a performance tool for collecting application performance data of an application; modifying the application by inserting the performance tool while the application does not need to be rebuilt from the source; executing the application; and collecting the application execution performance data such that only interesting data is collected. Customizing the performance tool proceeds by implementing at least one configurable tracing function that can be programmed by the user; compiling the function(s) into an object file; and inserting the object file into the performance tool using binary instrumentation.

Abstract: During runtime of a binary program file, streams of instructions are executed and memory references, generated by instrumentation applied to given ones of the instructions that refer to memory locations, are collected. A transformation is performed, based on the executed streams of instructions and the collected memory references, to obtain a table. The table lists memory events of interest for active data structures for each function in the program file. The transformation is performed to translate memory addresses for given ones of the instructions and given ones of the data structures into locations and variable names in a source file corresponding to the binary file. At least the memory events of interest are displayed, and the display is organized so as to correlate the memory events of interest with corresponding ones of the data structures.

Abstract: A target application is automatically tuned. A list of solutions for identified performance bottlenecks in a target application is retrieved from a storage device. A plurality of modules is executed to compute specific parameters for solutions contained in the list of solutions. A list of modification commands associated with specific parameters computed by the plurality of modules is generated. The list of modification commands associated with the specific parameters is appended to a command sequence list. The list of modification commands is implemented in the target application. Specific source code regions corresponding to the identified performance bottlenecks in the target application are automatically tuned using the implemented list of modification commands. Then, the tuned target application is stored in the storage device.

Abstract: Detecting performance bottlenecks in a target application is provided. In response to receiving hotspot selections from a user interface, bottleneck rules are extracted from a database. A hotspot is a region of source code that exceeds a time threshold to execute in the target application. Metrics needed to evaluate the bottleneck rules extracted from the database are identified. The identified metrics are computed. It is determined whether each bottleneck rule extracted from the database is evaluated to true using the computed metrics for hotspots in the target application. In response to determining that a bottleneck rule is evaluated to true using an appropriate computed metric corresponding to the bottleneck rule, a bottleneck description is created for the bottleneck rule. Then, the bottleneck description is sent to the user interface.

Abstract: During runtime of a binary program file, streams of instructions are executed and memory references, generated by instrumentation applied to given ones of the instructions that refer to memory locations, are collected. A transformation is performed, based on the executed streams of instructions and the collected memory references, to obtain a table. The table lists memory events of interest for active data structures for each function in the program file. The transformation is performed to translate memory addresses for given ones of the instructions and given ones of the data structures into locations and variable names in a source file corresponding to the binary file. At least the memory events of interest are displayed, and the display is organized so as to correlate the memory events of interest with corresponding ones of the data structures.

Abstract: A target application is automatically tuned. A list of solutions for identified performance bottlenecks in a target application is retrieved from a storage device. A plurality of modules is executed to compute specific parameters for solutions contained in the list of solutions. A list of modification commands associated with specific parameters computed by the plurality of modules is generated. The list of modification commands associated with the specific parameters is appended to a command sequence list. The list of modification commands is implemented in the target application. Specific source code regions corresponding to the identified performance bottlenecks in the target application are automatically tuned using the implemented list of modification commands. Then, the tuned target application is stored in the storage device.

Abstract: A system for detecting performance bottlenecks in a target application. In response to receiving hotspot selections from a user interface, bottleneck rules are extracted from a database. A hotspot is a region of source code that exceeds a time threshold to execute in the target application. Metrics needed to evaluate the bottleneck rules extracted from the database are identified. The identified metrics are computed. It is determined whether each bottleneck rule extracted from the database is evaluated to true using the computed metrics for hotspots in the target application. In response to determining that a bottleneck rule is evaluated to true using an appropriate computed metric corresponding to the bottleneck rule, a bottleneck description is created for the bottleneck rule. Then, the bottleneck description is sent to the user interface.

Abstract: A method for application performance data collection includes steps or acts of: customizing a performance tool for collecting application performance data of an application; modifying the application by inserting the performance tool while the application does not need to be rebuilt from the source; executing the application; and collecting the application execution performance data such that only interesting data is collected. Customizing the performance tool proceeds by implementing at least one configurable tracing function that can be programmed by the user; compiling the function(s) into an object file; and inserting the object file into the performance tool using binary instrumentation.

Abstract: A method and apparatus for creating a compressed trace for a program, wherein events are compressed separately to provide improved compression and tracing. A sequence of events for a program is selected, and a sequence of values is then determined for each of the selected events occurring during an execution of the program. Each sequence of values is then compressed to generate a compressed sequence of values for each event. These values are then ordered in accordance with information stored in selected events (such as for example, branch events), where the ordered values correspond to the trace.

Abstract: A method and apparatus for creating a compressed trace for a program, wherein events are compressed separately to provide improved compression and tracing. A sequence of events for a program is selected, and a sequence of values is then determined for each of the selected events occurring during an execution of the program. Each sequence of values is then compressed to generate a compressed sequence of values for each event. These values are then ordered in accordance with information stored in selected events (such as for example, branch events), where the ordered values correspond to the trace.