PREDICT COMPUTING PLATFORM MEMORY POWER UTILIZATION

• US 20100191997A1
• Filed: 04/01/2010
• Published: 07/29/2010
• Est. Priority Date: 06/06/2006
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Mukherjee, Udayan, Hiremath, Chetan, Chandwani, Neelam, Dodeja, Rakesh, Ambrose, Anthony
• Granted Patent: US 9,104,409 B2
• Time in Patent Office: Days
• US Class Current: 713/323
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• CPC Class Codes: G06F 1/3225 of memory devices

Abstract

A method is to include implementing at least one statistical prediction model to predict memory power utilization and reduce power consumption for a computing platform. The implementation includes determining a configuration parameter for the computing platform, monitoring an operating parameter for the computing platform and predicting memory power utilization for the computing platform based on the determined configuration parameter and the monitored operating parameter. The method is to also include transitioning at least one memory module resident on the computing platform to one of a plurality of power states based at least in part on memory power utilization predicted via the implementation of the at least one statistical prediction model.

Claims

1. A method to reduce memory power consumption for a computing platform, the method comprising:

inspecting an operating parameter associated with a resource of the computing platform that is updated by the resource of the computing platform during runtime of the computing platform;

predicting memory power utilization for the computing platform during the runtime of the computing platform based at least in part on the operating parameter; and

transitioning a current power state of at least one memory module resident on the computing platform to one of a plurality of power states based on the predicting of the memory power utilization.

2. The method of claim 1, wherein inspecting the operating parameter comprises monitoring a memory location storing the operating parameter, wherein the memory location includes at least one of a register, a table in memory, or a buffer.

3. The method of claim 1, wherein the operating parameter includes at least one of a network traffic parameter, a processing element utilization parameter, a processing element performance parameter, a processing element power state parameter, a processing element memory allocation parameter, a memory access pattern parameter, or a memory module power state parameter.

4. The method of claim 1, wherein the resource comprises at least one of a processing element, a network interface, or a memory controller.

5. The method of claim 1, wherein the predicting of the memory power utilization includes using a statistical parameter in a statistical prediction algorithm that is determined during a given period of time that begins as the computing platform is initially powered-up.

6. The method of claim 1, wherein the predicting of the memory power utilization includes using a statistical parameter in a statistical prediction algorithm that is determined during a given period of time and tuned periodically during the runtime of the computing platform.

7. The method of claim 6, wherein the statistical parameter includes at least one of memory requests made to a memory controller for the at least one memory module, processing element utilization for a processing element on the computing platform, power state of a processing element on the computing platform, memory capacity utilization for a processing element on the computing platform and network bandwidth on at least one communication link between the computing platform and a network.

8. The method of claim 7, wherein the power state of the processing element includes one of a suspend power state, a standby power state and a deep sleep power state.

9. The method of claim 1, further comprising:

obtaining a configuration parameter from a memory controller for the at least one memory module, the configuration parameter to include at least one usage configuration for the at least one memory module selected from the following group of;

memory interleaving, memory mirroring, memory sparing and rank order allocation.

10. The method of claim 1, wherein the plurality of power states includes an offline state, an online state, a standby state, or a suspend state.

11. The method of claim 1, wherein the at least one memory module is a fully buffered dual inline memory module (FB-DIMM).

12. The method of claim 11, wherein transitioning the FB-DIMM to one of the plurality of power states results in another FB-DIMM resident on the computing platform also transitioning to the same power state.

13. A machine-accessible storage medium storing content, which, when executed by a machine resident on a computing platform causes the machine to:

monitoring an operating parameter associated with a resource of the computing platform during runtime of the computing platform by inspecting a memory location that is updated by the resource of the computing platform during runtime of the computing platform;

predicting memory power utilization for the computing platform during the runtime of the computing platform based at least in part on the monitored operating parameter; and

transitioning a current power state of at least one memory module resident on the computing platform to one of a plurality of power states based on the predicting of the memory power utilization.

14. The machine-accessible storage medium of claim 13, wherein the memory location comprises at least one of a register, a table in memory, or a buffer.

15. The machine-accessible storage medium of claim 13, wherein the operating parameter includes at least one of a network traffic parameter, a processing element utilization parameter, a processing element performance parameter, a processing element power state parameter, a processing element memory allocation parameter, a memory access pattern parameter, or a memory module power state parameter.

16. The machine-accessible storage medium of claim 13, wherein the resource comprises at least one of a processing element, a network interface, or a memory controller.

17. The machine-accessible storage medium of claim 13, wherein the predicting of the memory power utilization includes using a statistical parameter in a statistical prediction algorithm that is determined during a given period of time that begins as the computing platform is initially powered-up.

18. The machine-accessible storage medium of claim 13, wherein the predicting of the memory power utilization includes using a statistical parameter in a statistical prediction algorithm that is determined during a given period of time and tuned periodically during the runtime of the computing platform.

19. The machine-accessible storage medium of claim 18, wherein the statistical parameter includes at least one of memory requests made to a memory controller for the at least one memory module, processing element utilization for a processing element on the computing platform, power state of a processing element on the computing platform, memory capacity utilization for a processing element on the computing platform and network bandwidth on at least one communication link between the computing platform and a network.

20. The machine-accessible storage medium of claim 13, wherein the plurality of power states includes an offline state, an online state, a standby state, and a suspend state.