Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing performance issues. One of the methods includes maintaining, for a plurality of devices at least some of which have different contexts, metric data for an application that executed on each of the plurality of devices; determining, for a metric attribute from a plurality of metric attributes and a subset of the plurality of devices each of which have at least one common context, a potential performance issue for the subset of the plurality of devices using aggregated metric data for the metric attribute; determining, using at least a portion of the aggregated metric data, a portion of a code base or a hardware subcomponent that likely caused the potential performance issue; and providing data for the portion of the code base or the hardware subcomponent that likely caused the potential performance issue.

Abstract: This disclosure provide various techniques for decreasing the amount of energy consumed on an electronic device by one or more background processes. By implementing a fast energy accounting engine that may quickly detect changes in energy usage by the background processes and report the changes to a dynamic activity scheduler, a system may decrease the overall energy consumed by the one or more background processes.

Abstract: A method for generating an energy usage profile for a software program executed in a computing device includes generating a call trace of an executed system call, identifying a first power consumption and duration of a first power state due to the first system call using a model, identifying a second power consumption and duration of a second power state based on the model, and generating an energy usage profile for the software program. The energy usage profile includes energy consumption of the computing device for the system call based on the first power consumption level multiplied by the first duration and the second power consumption level multiplied by the second duration associated an identifier for the call trace.

Abstract: Embodiments of the present disclosure include methods and systems for generating and processing energy consumption reports received from a plurality of devices. The reports may include a plurality of call stacks captured and stored by remote device as a result of an energy consumption of the corresponding device. For each energy consumption report, the plurality of call stacks may be analyzed to determine a representative call stack of the energy consumption report. The energy consumption reports may be assigned to one or more of a plurality of energy consumption categories based on distances between the representative call stacks of the energy consumption reports and corresponding representative call stacks of the energy consumption categories. A request may be received for energy consumption reports for a particular energy consumption category. Upon receiving the request, providing a representative set of energy consumption reports assigned to the particular energy consumption category.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.

Abstract: In some embodiments, an electronic device displays one or more representations of power usage of the electronic device, including across various periods of time and subperiods of time within those periods. In some embodiments, the displayed information reflects power usage both for periods of the display being on and periods of the display being off. In some embodiments, the displayed information includes power usage attributed to various mobile applications running on the electronic device. In some embodiments, the electronic device displays recommendations to reduce the usage of power by the electronic device, which a user has the option of applying. In some embodiments, the electronic device displays prose insight into power usage, indicating causes of the power usage.

Abstract: This disclosure provide various techniques for decreasing the amount of energy consumed on an electronic device by one or more background processes. By implementing a fast energy accounting engine that may quickly detect changes in energy usage by the background processes and report the changes to a dynamic activity scheduler, a system may decrease the overall energy consumed by the one or more background processes.

Abstract: A method for generating an energy usage profile for a software program executed in a computing device includes generating a call trace of an executed system call, identifying a first power consumption and duration of a first power state due to the first system call using a model, identifying a second power consumption and duration of a second power state based on the model, and generating an energy usage profile for the software program. The energy usage profile includes energy consumption of the computing device for the system call based on the first power consumption level multiplied by the first duration and the second power consumption level multiplied by the second duration associated an identifier for the call trace.

Abstract: A method for generating an energy usage profile for a software program executed in a computing device includes generating a call trace of an executed system call, identifying a first power consumption and duration of a first power state due to the first system call using a model, identifying a second power consumption and duration of a second power state based on the model, and generating an energy usage profile for the software program. The energy usage profile includes energy consumption of the computing device for the system call based on the first power consumption level multiplied by the first duration and the second power consumption level multiplied by the second duration associated an identifier for the call trace.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.

Abstract: An electronic device having a user interface for displaying battery usage of the device over a given time period. Suggestions to improve device battery life are displayed along with usage when battery savings suggestions criteria are met.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.

Abstract: Techniques are disclosed performing a power logging in a computer system at a sub-process level. An exemplary method includes an operating system of the computer system determining process information indicative of which sub-portions of one or more processes are running on the computer system at different points in time, as well as may determining power information for the computer system at different points in time. The operating system may the create, from the process information and the power information, a power log indicative of power usage of sub-portions of processes at a plurality of points in time. The power logging may extend to both core and non-core resources of the system. For non-core resources, the power usage may be estimated in some cases based on the type of non-core resource being called as well as parameters passed to the non-core resource.

Abstract: Embodiments of the present invention provide a system and methods for detecting power bugs. In one embodiment, a computer-implemented method for analyzing a computer code includes generating a control flow graph for at least a portion of the computer code at a processor. The method further includes identifying power bugs by traversing the control flow graph if the control flow graph exits without performing a function call to deactivate power to any component of a device configured to execute computer executable instructions based on the computer code after performing a function call to activate power.

Abstract: Circuits, methods, and apparatus that may estimate the power being consumed by an OLED display screen of an electronic device, may provide further information about that power usage, may modify or change functions performed by the electronic device based on that power usage, and may inform an application's developer about the amount of power being used by the electronic device while the electronic device is running the application. One example may estimate the power being used by an OLED display screen of an electronic device by determining the content of images being displayed during a duration. The estimated power may then be presented to a user. The estimated power may be used in decisions to modify or change parameters of the screen or other device components.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.

Abstract: Embodiments of the present disclosure include methods and systems for generating and processing energy consumption reports received from a plurality of devices. The reports may include a plurality of call stacks captured and stored by remote device as a result of an energy consumption of the corresponding device. For each energy consumption report, the plurality of call stacks may be analyzed to determine a representative call stack of the energy consumption report. The energy consumption reports may be assigned to one or more of a plurality of energy consumption categories based on distances between the representative call stacks of the energy consumption reports and corresponding representative call stacks of the energy consumption categories. A request may be received for energy consumption reports for a particular energy consumption category. Upon receiving the request, providing a representative set of energy consumption reports assigned to the particular energy consumption category.

Abstract: Embodiments of the present disclosure include methods and systems for generating and processing energy consumption reports received from a plurality of devices. The reports may include a plurality of call stacks captured and stored by remote device as a result of an energy consumption of the corresponding device. For each energy consumption report, the plurality of call stacks may be analyzed to determine a representative call stack of the energy consumption report. The energy consumption reports may be assigned to one or more of a plurality of energy consumption categories based on distances between the representative call stacks of the energy consumption reports and corresponding representative call stacks of the energy consumption categories. A request may be received for energy consumption reports for a particular energy consumption category. Upon receiving the request, providing a representative set of energy consumption reports assigned to the particular energy consumption category.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements.