Microsoft Technology Licensing, LLC

Sandeep Prabhakar

Iulian Doroftei Calinov

Jihad Tafas

Tristan Anthony Brown

Paresh J Maisuria

Samarth Harish Shah

Mark Allan Bellon

Taofiq Ezaz

Zachary Sandburg Holmes

Cenk Ergan

Rahul Nair

Steve Pronovost

Rico Mariani

Gregory Nielsen

Oreste Dorin Ungureanu

Javier Nisim Flores Assad

Rajagopal Venkatachalam

M Nashaat Soliman

Harneet Singh Sidhana

Mohamed Sadek

Christopher Peter Kleynhans

Eric Kratzer

Vijay Sundaram

Gabriel Knezek

Tapan Ansel

Brendan W Flynn

Mika Megan Latimer

Rahul Narayanan Nair

James Kennyth Truckle

Brandon Michael Heenan

Julian Doroftei Calinov

Joshua Daniel Patterson

Thomas Arthur Sewell

Cho Yu Chong

Paresh Maisuria

Eric R Kratzer

A computing device includes a cooling device and a cooling activity monitor configured to assess a cooling activity of the cooling device. A cooling activity reporter is configured to, based at least in part on the cooling activity of the cooling device crossing a predefined cooling activity threshold, communicate a cooling activity indication to a resource manager of the computing device.

Computer cooling device activity indication

Systems and methods for managing a power budget are provided. The method includes designating, by a power budget manager implemented on at least one processor, each of one or more applications with an individual quality of service (QoS) designation, the one or more applications executable by the at least one processor, assigning, by the power budget manager, a throttling priority to each of the one or more applications based on the individual QoS designations, determining, by the power budget manager, whether a platform mitigation threshold is exceeded, and responsive to determining that the platform mitigation threshold is exceeded, throttling, by the power budget manager, processing power allocated to at least one application of the one or more applications based on the throttling prioritization.

Power budget management using quality of service (QoS)

Usage data of a device is maintained over some time period, such as a couple weeks or a couple months. This usage data reflects an amount of energy that is used or stored for different time durations each having a various combinations of different state values of the device, such as a screen state, a power source state, a power mode state, a battery saver state, a processor consumption state, and so forth. This provides a record of a capacity drain rate or capacity charge rate for the battery for each combination of state values. At any given time, the device can determine the current state values of the device. The capacity drain rates or capacity charge rates for previous time durations having the same state values as the current state values are used to determine an estimated capacity drain rate or capacity charge rate for the device.

Usage data based battery charge or discharge time determination

A computing device has an energy storage device system with one or more energy storage devices. A target run-time is obtained, which refers to how long the computing device is to run given the current amount of energy in the energy storage device(s). A predicted power usage over the target run-time is determined, and what, if any, power management actions to take in order to achieve the target run-time are determined. The power management actions are then taken. A target charge-time is also obtained, which refers to how long the computing device is to take to charge the energy storage device(s) to a threshold level (e.g., 100% or fully charged). A predicted power gain over the target charge-time is determined, and what, if any, power management actions to take in order to achieve the target charge-time are determined. The power management actions are then taken.

Target based power management

Embodiments relate to enabling software to interface with a power consumption telemetry system. A process may have tagging instructions that interface with an energy estimation engine. While the energy estimation engine is logging energy consumption by the process, the process may also mark, tag, or otherwise delineate periods of processing during the execution time of the process. The logic of the process may determine how such periods should be labeled and when they being and end. The tagged periods are correlated with energy consumed by the process so that details of what the process was doing during different periods of energy consumption may be understood.

Process-level activity tagging

Consumption of power by device components is profiled on a per-session basis of user interaction. A session engine determines start of a user interaction session with the device (e.g., by detecting screen turn on). The engine generates a session identifier (SID) and broadcasts same to device component(s). In response to the SID, the component(s) record power consumption data. Upon receiving a signal indicating end of the user interaction session (e.g., screen turn off), the session engine broadcasts a notice allowing component(s) to stop recording power consumption data. The components communicate aggregated data to the session engine for storage in a centralized location (e.g., trace buffer). In response to a query posed within a command line prompt, stored data may be parsed on a per-session basis for inclusion in a detailed report of power consumption. Power consumption inefficiencies endemic to components and/or user behaviors may be thus be identified and remediated.

Per-screen on session power consumption profiling

Embodiments described herein relate to improving throughput of a CPU and a GPU working in conjunction to render graphics. Time frames for executing CPU and GPU work units are synchronized with a refresh rate of a display. Pending CPU work is performed when a time frame starts (a vsync occurs). When a prior GPU work unit is still executing on the GPU, then a parallel mode is entered. In the parallel mode, some GPU work and some CPU work is performed concurrently. When the parallel mode is exited, for example when there is no CPU work to perform, the parallel mode may be exited.

CPU-GPU parallelization

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Bellevue, WA, United States of America

Sandeep Prabhakar