Runtime processor optimization

Patent metadata

Field	Value
Publication number	US-2018246762-A1
Application number	US-201715444390-A
Country	US
Kind code	A1
Filing date	Feb 28, 2017
Priority date	Feb 28, 2017
Publication date	Aug 30, 2018
Grant date	—

Abstract

In one embodiment, a processor comprises a processor optimization unit. The processor optimization unit is to collect runtime information associated with a computing device, wherein the runtime information comprises information indicating a performance of the computing device during program execution. The processor optimization unit is further to receive runtime optimization information for the computing device, wherein the runtime optimization information comprises information associated with one or more runtime optimizations for the computing device, and wherein the runtime optimization information is determined based on an analysis of the collected runtime information. The processor optimization unit is further to perform the one or more runtime optimizations for the computing device based on the runtime optimization information.

First claim

What is claimed is:

1 . A processor, comprising:
a processor optimization unit to:
collect runtime information associated with a computing device, wherein the runtime information comprises information indicating a performance of the computing device during program execution;
receive runtime optimization information for the computing device, wherein the runtime optimization information comprises information associated with one or more runtime optimizations for the computing device, and wherein the runtime optimization information is determined based on an analysis of the collected runtime information; and
perform the one or more runtime optimizations for the computing device based on the runtime optimization information.

2 . The processor of claim 1 , wherein the processor optimization unit to receive the runtime optimization information for the computing device is further to determine the runtime optimization information.

3 . The processor of claim 2 , wherein the runtime information comprises a plurality of event counters associated with a workload of the computing device.

4 . The processor of claim 3 , wherein the processor optimization unit to determine the runtime optimization information is further to perform phase recognition for the workload of the computing device.

5 . The processor of claim 4 , wherein the processor optimization unit to perform phase recognition for the workload of the computing device is further to perform noise reduction using soft-thresholding.

6 . The processor of claim 4 , wherein the processor optimization unit to perform phase recognition for the workload of the computing device is further to identify a phase associated with the workload using a convolutional phase comparison.

7 . The processor of claim 4 , wherein the processor optimization unit to perform phase recognition for the workload of the computing device is further to identify a phase associated with the workload using a chi-squared calculation.

8 . The processor of claim 1 , wherein the processor optimization unit to receive the runtime optimization information for the computing device is further to receive the runtime optimization information from a cloud service remote from the computing device.

9 . The processor of claim 8 :
wherein the runtime information comprises instruction trace data associated with an application executed on the computing device, wherein the instruction trace data comprises a plurality of branch instructions; and wherein the runtime optimization information is determined by identifying a relationship associated with the plurality of branch instructions to improve branch prediction performed by the computing device.

10 . At least one machine accessible storage medium having instructions stored thereon, the instructions, when executed on a machine, cause the machine to:
collect runtime information associated with a computing device, wherein the runtime information comprises information indicating a performance of the computing device during program execution; receive runtime optimization information for the computing device, wherein the runtime optimization information comprises information associated with one or more runtime optimizations for the computing device, and wherein the runtime optimization information is determined based on an analysis of the collected runtime information; and perform the one or more runtime optimizations for the computing device based on the runtime optimization information.

11 . The storage medium of claim 10 , wherein the instructions that cause the machine to receive the runtime optimization information for the computing device further cause the machine to determine the runtime optimization information.

12 . The storage medium of claim 11 :
wherein the runtime information comprises a plurality of event counters associated with a workload of the computing device; and wherein the instructions that cause the machine to determine the runtime optimization information further cause the machine to perform phase recognition for the workload of the computing device.

13 . The storage medium of claim 12 , wherein the instructions that cause the machine to perform phase recognition for the workload of the computing device further cause the machine to perform noise reduction using soft-thresholding.

14 . The storage medium of claim 12 , wherein the instructions that cause the machine to perform phase recognition for the workload of the computing device further cause the machine to identify a phase associated with the workload using a convolutional phase comparison.

15 . The storage medium of claim 12 , wherein the instructions that cause the machine to perform phase recognition for the workload of the computing device further cause the machine to identify a phase associated with the workload using a chi-squared calculation.

16 . The storage medium of claim 10 :
wherein the runtime information comprises instruction trace data associated with an application executed on the computing device, wherein the instruction trace data comprises a plurality of branch instructions; and wherein the runtime optimization information is determined by identifying a relationship associated with the plurality of branch instructions to improve branch prediction performed by the computing device.

17 . A method, comprising:
collecting runtime information associated with a computing device, wherein the runtime information comprises information indicating a performance of the computing device during program execution; receiving runtime optimization information for the computing device, wherein the runtime optimization information comprises information associated with one or more runtime optimizations for the computing device, and wherein the runtime optimization information is determined based on an analysis of the collected runtime information; and performing the one or more runtime optimizations for the computing device based on the runtime optimization information.

18 . The method of claim 17 , wherein receiving the runtime optimization information for the computing device further comprises determining the runtime optimization information.

19 . The method of claim 18 :
wherein the runtime information comprises a plurality of event counters associated with a workload of the computing device; and wherein determining the runtime optimization information comprises performing phase recognition for the workload of the computing device.

20 . The method of claim 19 , wherein performing phase recognition for the workload of the computing device comprises performing noise reduction using soft-thresholding.

21 . The method of claim 19 , wherein performing phase recognition for the workload of the computing device comprises identifying a phase associated with the workload using a convolutional phase comparison.

22 . The method of claim 19 , wherein performing phase recognition for the workload of the computing device comprises identifying a phase associated with the workload using a chi-squared calculation.

23 . The method of claim 17 :
wherein the runtime information comprises instruction trace data associated with an application executed on the computing device, wherein the instruction trace data comprises a plurality of branch instructions; and wherein the runtime optimization information is determined by identifying a relationship associated with the plurality of branch instructions to

Assignees

• Intel Corp

Inventors

• Tarsa Stephen J
• Chinya Gautham N
• Keskin Gokce
• Wang Hong
• Sankaranarayanan Karthik

Classifications

• G06F2209/501

  Performance criteria · CPC title

• G06F9/505 Primary

  considering the load · CPC title

• G06F9/5094

  where the allocation takes into account power or heat criteria (power management in computers in general G06F1/3203; thermal management in computers in general G06F1/206) · CPC title

• G06F9/5083 Primary

  Techniques for rebalancing the load in a distributed system · CPC title