METHOD AND APPARATUS FOR TEMPERATURE AND VOLTAGE MANAGEMENT CONTROL

    公开(公告)号:US20180088606A1

    公开(公告)日:2018-03-29

    申请号:US15274697

    申请日:2016-09-23

    CPC classification number: G06F1/28 G06F1/3206 G06F1/3296 Y02D10/172

    Abstract: A method and apparatus for managing processing power determine a supply voltage to supply to a processing unit, such as a central processing unit (CPU) or graphics processing unit (GPU), based on temperature inversion based voltage, frequency, temperature (VFT) data. The temperature inversion based VFT data includes supply voltages and corresponding operating temperatures that cause the processing unit's transistors to operate in a temperature inversion region. In one example, the temperature inversion based VFT data includes lower supply voltages and corresponding higher temperatures in a temperature inversion region of a processing unit. The temperature inversion based VFT data is based on an operating frequency of the processing unit. The apparatus and method adjust a supply voltage to the processing unit based on the temperature inversion based VFT data.

    HOST CONTROLLER AND BUS-ATTACHED PERIPHERAL DEVICE POWER CONSUMPTION REDUCTION

    公开(公告)号:US20230273890A1

    公开(公告)日:2023-08-31

    申请号:US17682527

    申请日:2022-02-28

    CPC classification number: G06F13/385 G06F13/20 G06F2213/0042

    Abstract: Systems, apparatuses, and methods for a host controller inferring idleness based on activity generated by a bus-attached peripheral device are disclosed. A host controller detects activity by a first device attached to the host controller via a first bus. The host controller generates an activity vector based on the detected activity, and the host controller determines whether the activity vector indicates that the first device is only engaging in handshaking or control activity rather than data transfer. If the first device is merely communicating status information, then the host controller infers idleness and conveys an idleness indicator to a power manager. The power manager turns off power to system memory and/or other components based on the idleness indicator, but keeps enough power on to allow the host controller to communicate with the first device for handshaking or status purposes.

    Power efficiency optimization in throughput-based workloads

    公开(公告)号:US11054883B2

    公开(公告)日:2021-07-06

    申请号:US16011476

    申请日:2018-06-18

    Abstract: A power management algorithm framework proposes: 1) a Quality-of-Service (QoS) metric for throughput-based workloads; 2) heuristics to differentiate between throughput and latency sensitive workloads; and 3) an algorithm that combines the heuristic and QoS metric to determine target frequency for minimizing idle time and improving power efficiency without any performance degradation. A management algorithm framework enables optimizing power efficiency in server-class throughput-based workloads while still providing desired performance for latency sensitive workloads. The power savings are achieved by identifying workloads in which one or more cores can be run at a lower frequency (and consequently lower power) without a significant negative performance impact.

    Bandwidth-aware multi-frequency performance estimation mechanism

    公开(公告)号:US10048741B1

    公开(公告)日:2018-08-14

    申请号:US15416993

    申请日:2017-01-26

    Abstract: Systems, apparatuses, and methods for implementing performance estimation mechanisms are disclosed. In one embodiment, a computing system includes at least one processor and a memory subsystem. During a characterization phase, the system utilizes a memory intensive workload to detect when the memory subsystem reaches its saturation point. Then, the system collects performance counter values during a sampling phase of a target application to determine the memory bandwidth. If the memory bandwidth is greater than the saturation point, then the system generates a prediction of the memory time which is based on a ratio of the memory bandwidth over the saturation point. Otherwise, if the memory bandwidth is less than the saturation point, the system assumes memory time is constant versus processor frequency. Then, the system uses the memory time and an estimate of the compute time to estimate a phase time for the target application at different processor frequencies.

    REAL-TIME PERFORMANCE TRACKING USING DYNAMIC COMPILATION

    公开(公告)号:US20170371761A1

    公开(公告)日:2017-12-28

    申请号:US15192748

    申请日:2016-06-24

    CPC classification number: G06F11/3604 G06F9/45516

    Abstract: Systems, apparatuses, and methods for performing real-time tracking of performance targets using dynamic compilation. A performance target is specified in a service level agreement. A dynamic compiler analyzes a software application executing in real-time and determine which high-level application metrics to track. The dynamic compiler then inserts instructions into the code to increment counters associated with the metrics. A power optimization unit then utilizes the counters to determine if the system is currently meeting the performance target. If the system is exceeding the performance target, then the power optimization unit reduces the power consumption of the system while still meeting the performance target.

    MANAGING VARIATIONS AMONG NODES IN PARALLEL SYSTEM FRAMEWORKS

    公开(公告)号:US20170279703A1

    公开(公告)日:2017-09-28

    申请号:US15081558

    申请日:2016-03-25

    CPC classification number: H04L43/16 H04L43/08 H04L67/10 H04L67/1008

    Abstract: Systems, apparatuses, and methods for managing variations among nodes in parallel system frameworks. Sensor and performance data associated with the nodes of a multi-node cluster may be monitored to detect variations among the nodes. A variability metric may be calculated for each node of the cluster based on the sensor and performance data associated with the node. The variability metrics may then be used by a mapper to efficiently map tasks of a parallel application to the nodes of the cluster. In one embodiment, the mapper may assign the critical tasks of the parallel application to the nodes with the lowest variability metrics. In another embodiment, the hardware of the nodes may be reconfigured so as to reduce the node-to-node variability.

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