A data interface sleep and wakeup method includes receiving data information sent by a second electronic device, where the data information includes sleep information, and setting, based on the sleep information, at least one data interface to either a sleep state or a wakeup state.

A method for power management based on synthetic machine learning benchmarks, including generating a record of synthetic machine learning benchmarks for synthetic machine learning models that are obtained by changing machine learning network topology parameters, receiving hardware information from a client device executing a machine learning program or preparing to execute a machine learning program, selecting a synthetic machine learning benchmark based on the correlation of the hardware information with the synthetic machine learning models, and determining work schedules based on the selected synthetic machine learning benchmark.

A method for power management based on synthetic machine learning benchmarks, including generating a record of synthetic machine learning benchmarks for synthetic machine learning models that are obtained by changing machine learning network topology parameters, receiving hardware information from a client device executing a machine learning program or preparing to execute a machine learning program, selecting a synthetic machine learning benchmark based on the correlation of the hardware information with the synthetic machine learning models, and determining work schedules based on the selected synthetic machine learning benchmark.

As the volume of data under management expands rapidly, so do the costs associated with storing and that data on secondary storage devices. The illustrative approach provides an improvement to the information management system by delaying certain tasks that meet a set of criteria until a specified threshold is met. The system receives a request to be performed on a set of data stored on secondary devices. Power management module determines whether the task satisfies a set of criteria for delayed execution, queues the task, and when a specified threshold of the queued tasks is met powers up the necessary components to execute the tasks.

As the volume of data under management expands rapidly, so do the costs associated with storing and that data on secondary storage devices. The illustrative approach provides an improvement to the information management system by delaying certain tasks that meet a set of criteria until a specified threshold is met. The system receives a request to be performed on a set of data stored on secondary devices. Power management module determines whether the task satisfies a set of criteria for delayed execution, queues the task, and when a specified threshold of the queued tasks is met powers up the necessary components to execute the tasks.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

Systems and methods are disclosed for scheduling threads on a processor that has at least two different core types, such as an asymmetric multiprocessing system. Each core type can run at a plurality of selectable voltage and frequency scaling (DVFS) states. Threads from a plurality of processes can be grouped into thread groups. Execution metrics are accumulated for threads of a thread group and fed into a plurality of tunable controllers for the thread group. A closed loop performance control (CLPC) system determines a control effort for the thread group and maps the control effort to a recommended core type and DVFS state. A closed loop thermal and power management system can limit the control effort determined by the CLPC for a thread group, and limit the power, core type, and DVFS states for the system. Deferred interrupts can be used to increase performance.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

A cooling mechanism apparatus, system, and method for adaptively controlling heat of a vehicle processor, includes a processor configured to control deactivation or activation of an application driving the in-vehicle controller depending on grades of a predetermined functional safety level of the application and to determine an order of the deactivation or the activation of the application when heat is generated in the controller.