Apple Inc.

Soren C Spies

Amit K Vyas

Abhinav Pathak

Prajakta S Karandikar

Albert S Liu

Conor J O'Reilly

Anthony J Chivetta

Anthony R Newnam

Brian K Tearse-Doyle

Phillip J Azar

Matthew C Widmann

Ashish Patro

Daniel J Etter

Andreas C Bergen

Andrew P Sakai

Terrence R Long

Nehal Bhandari

Anand Subramanian

Jeffrey S Lale

Conor J O'REILLY

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.

Microprocessor power logging at a sub-process level

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. The user interfaces can provide an intelligent method for visualizing performance changes for significant changes in application versions.

Logging techniques for third party application data

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San Jose, CA, United States of America

Soren C Spies