Automated overclocking using a prediction model

1. A method for overclocking a computer system, comprising:

predicting a stable operating frequency for a central processing unit (CPU) in a target system based, at least in part, on an output of a prediction model generated from data collected for a test system;

adjusting an operating frequency for the CPU to the stable operating frequency; and

running a benchmark test to confirm that the CPU is operating within limits.

2. The method of claim 1 , comprising:

running the benchmark test on the test system to generate a data set; and

creating the prediction model using the data set to correlate input parameters to output parameters in the data set for the test system.

3. The method of claim 2 , comprising:

determining a range of input parameters for the test system;

iterating through the range of input parameters; and

running the benchmark test at each combination of input parameters while logging the output parameters.

4. The method of claim 1 , comprising:

running the benchmark test on multiple test systems to generate a data set;

creating an average system configuration; and

creating the prediction model using the data and the average system configuration to correlate output parameters to input parameters in the data set.

5. The method of claim 1 , comprising collecting the data for the test system by:

identifying input parameters and ranges in a configuration of the test system;

logging the configuration of the test system;

configuring the test system with the input parameters;

running the benchmark test to collect output parameters associated with the input parameters; and

logging the output parameters for the test system.

6. The method of claim 5 , wherein the input parameters comprise at least one of CPU frequency, CPU voltage, cache frequency, or cache voltage.

7. The method of claim 5 , wherein the output parameters comprise at least one of CPU temperature, CPU core frequency, memory utilization, thermal throttling percentage, memory frequency, or CPU fan speed.

8. The method of claim 5 , comprising performing a statistical regression to create the prediction model correlating the input parameters and the output parameters for the test system.

9. The method of claim 8 , comprising determining a delta between the target system and the test system.

10. The method of claim 9 , comprising generating a set of input parameters for the target system based, at least in part, on the delta between the target system and the test system.

11. The method of claim 1 , wherein the prediction model comprises a neural network.

12. An overclocking system comprising:

a processor to:

generate a prediction model based on a benchmark test executed upon a test computer system, the test computer system comprising a first central processing unit (CPU) operating at a first CPU frequency and a first CPU voltage;

predict a stable operating frequency for a target computer system by using the prediction model and input parameters received from the target computer system, the target computer system comprising a second CPU operating at a second CPU frequency and a second CPU voltage; and

adjust the second CPU frequency of the target computer system based on the predicted stable operating frequency.

13. The overclocking system of claim 12 , wherein the prediction model correlates the input parameters with output parameters associated with the benchmark test.

14. The overclocking system of claim 13 , wherein the prediction model comprises a statistical correlation between the output parameters and the input parameters associated with the benchmark test.

15. The overclocking system of claim 14 , wherein the statistical correlation between the output parameters and the input parameters is determined responsive to performing a statistical regression.

16. The overclocking system of claim 15 , wherein the prediction model comprises a neural network.

17. A non-transitory machine-readable medium comprising instructions that, in response to being executed on a computing device, cause the computing device to:

predict a stable operating frequency for a central processing unit (CPU) in the computing device based, at least in part, on a prediction model generated from data collected for a second CPU in a second computing device; and

adjust an operating frequency for the CPU to the stable operating frequency.

18. The machine-readable medium of claim 17 , comprising instructions that, in response to being executed on the computing device, cause the computing device to:

identify a range for an input parameter associated with the second CPU;

run a benchmark test on the second CPU at the input parameter; and

log an output parameter associated with the input parameter based on the benchmark test;

increment the input parameter; and

iterate through the range for the input parameter to complete the benchmark test.

19. The machine-readable medium of claim 17 , comprising instructions that, in response to being executed on the computing device, cause the computing device to generate the prediction model by performing a statistical regression using the data collected for the second CPU in the second computing device.

20. The machine-readable medium of claim 19 , wherein the prediction model comprises a neural network.