power-calibrate is a simple tool that hacked up to perform some synthetic loading of the processor, gather the RAPL and CPU stats and using simple linear regression to compute some power related metrics.
In the example below, I run power-calibrate on an Intel i5-3210M (2 Cores, 4 threads) with each test run taking 10 seconds (-r 10), using the RAPL interface to measure power and gathering 11 samples on CPU threads 1..4:
power-calibrate -r 10 -R -s 11
CPU load User Sys Idle Run Ctxt/s IRQ/s Ops/s Cycl/s Inst/s Watts
0% x 1 0.1 0.1 99.8 1.0 181.6 61.1 0.0 2.5K 380.2 2.485
0% x 2 0.0 1.0 98.9 1.2 161.8 63.8 0.0 5.7K 0.8K 2.366
0% x 3 0.1 1.3 98.5 1.1 204.2 75.2 0.0 7.6K 1.9K 2.518
0% x 4 0.1 0.1 99.9 1.0 124.7 44.9 0.0 11.4K 2.7K 2.167
10% x 1 2.4 0.2 97.4 1.5 203.8 104.9 21.3M 123.1M 297.8M 2.636
10% x 2 5.1 0.0 94.9 1.3 185.0 137.1 42.0M 243.0M 0.6B 2.754
10% x 3 7.5 0.2 92.3 1.2 275.3 190.3 58.1M 386.9M 0.8B 3.058
10% x 4 10.0 0.1 89.9 1.9 213.5 206.1 64.5M 486.1M 0.9B 2.826
20% x 1 5.0 0.1 94.9 1.0 288.8 170.0 69.6M 403.0M 1.0B 3.283
20% x 2 10.0 0.1 89.9 1.6 310.2 248.7 96.4M 0.8B 1.3B 3.248
20% x 3 14.6 0.4 85.0 1.7 640.8 450.4 238.9M 1.7B 3.3B 5.234
20% x 4 20.0 0.2 79.8 2.1 633.4 514.6 270.5M 2.1B 3.8B 4.736
30% x 1 7.5 0.2 92.3 1.4 444.3 278.7 149.9M 0.9B 2.1B 4.631
30% x 2 14.8 1.2 84.0 1.2 541.5 418.1 200.4M 1.7B 2.8B 4.617
30% x 3 22.6 1.5 75.9 2.2 960.9 694.3 365.8M 2.6B 5.1B 7.080
30% x 4 30.0 0.2 69.8 2.4 959.2 774.8 421.1M 3.4B 5.9B 5.940
40% x 1 9.7 0.3 90.0 1.7 551.6 356.8 201.6M 1.2B 2.8B 5.498
40% x 2 19.9 0.3 79.8 1.4 668.0 539.4 288.0M 2.4B 4.0B 5.604
40% x 3 29.8 0.5 69.7 1.8 1124.5 851.8 481.4M 3.5B 6.7B 7.918
40% x 4 40.3 0.5 59.2 2.3 1186.4 1006.7 0.6B 4.6B 7.7B 6.982
50% x 1 12.1 0.4 87.4 1.7 536.4 378.6 193.1M 1.1B 2.7B 4.793
50% x 2 24.4 0.4 75.2 2.2 816.2 668.2 362.6M 3.0B 5.1B 6.493
50% x 3 35.8 0.5 63.7 3.1 1300.2 1004.6 0.6B 4.2B 8.2B 8.800
50% x 4 49.4 0.7 49.9 3.8 1455.2 1240.0 0.7B 5.7B 9.6B 8.130
60% x 1 14.5 0.4 85.1 1.8 735.0 502.7 295.7M 1.7B 4.1B 6.927
60% x 2 29.4 1.3 69.4 2.0 917.5 759.4 397.2M 3.3B 5.6B 6.791
60% x 3 44.1 1.7 54.2 3.1 1615.4 1243.6 0.7B 5.1B 9.9B 10.056
60% x 4 58.5 0.7 40.8 4.0 1728.1 1456.6 0.8B 6.8B 11.5B 9.226
70% x 1 16.8 0.3 82.9 1.9 841.8 579.5 349.3M 2.0B 4.9B 7.856
70% x 2 34.1 0.8 65.0 2.8 966.0 845.2 439.4M 3.7B 6.2B 6.800
70% x 3 49.7 0.5 49.8 3.5 1834.5 1401.2 0.8B 5.9B 11.8B 11.113
70% x 4 68.1 0.6 31.4 4.7 1771.3 1572.3 0.8B 7.0B 11.8B 8.809
80% x 1 18.9 0.4 80.7 1.9 871.9 613.0 357.1M 2.1B 5.0B 7.276
80% x 2 38.6 0.3 61.0 2.8 1268.6 1029.0 0.6B 4.8B 8.2B 9.253
80% x 3 58.8 0.3 40.8 3.5 2061.7 1623.3 1.0B 6.8B 13.6B 11.967
80% x 4 78.6 0.5 20.9 4.0 2356.3 1983.7 1.1B 9.0B 16.0B 12.047
90% x 1 21.8 0.3 78.0 2.0 1054.5 737.9 459.3M 2.6B 6.4B 9.613
90% x 2 44.2 1.2 54.7 2.7 1439.5 1174.7 0.7B 5.4B 9.2B 10.001
90% x 3 66.2 1.4 32.4 3.9 2326.2 1822.3 1.1B 7.6B 15.0B 12.579
90% x 4 88.5 0.2 11.4 4.8 2627.8 2219.1 1.3B 10.2B 17.8B 12.832
100% x 1 25.1 0.0 74.8 2.0 135.8 314.0 0.5B 3.1B 7.5B 10.278
100% x 2 50.0 0.0 50.0 3.0 91.9 560.4 0.7B 6.2B 10.4B 10.470
100% x 3 75.1 0.1 24.8 4.0 120.2 824.1 1.2B 8.7B 16.8B 13.028
100% x 4 100.0 0.0 0.0 5.0 76.8 1054.8 1.4B 11.6B 19.5B 13.156
For 4 CPUs (of a 4 CPU system):
Power (Watts) = (% CPU load * 1.176217e-01) + 3.461561
1% CPU load is about 117.62 mW
Coefficient of determination R^2 = 0.809961 (good)
Energy (Watt-seconds) = (bogo op * 8.465141e-09) + 3.201355
1 bogo op is about 8.47 nWs
Coefficient of determination R^2 = 0.911274 (strong)
Energy (Watt-seconds) = (CPU cycle * 1.026249e-09) + 3.542463
1 CPU cycle is about 1.03 nWs
Coefficient of determination R^2 = 0.841894 (good)
Energy (Watt-seconds) = (CPU instruction * 6.044204e-10) + 3.201433
1 CPU instruction is about 0.60 nWs
Coefficient of determination R^2 = 0.911272 (strong)
The results at the end are estimates based on the gathered samples. The samples are compared to the computed linear regression coefficients using the coefficient of determination (R^2); a value of 1 is a perfect linear fit, less than 1 a poorer fit.
For more accurate results, increase the run time (-r option) and also increase the number of samples (-s option).
Power-calibrate is available in Ubuntu Wily 15.10. It is just an academic toy for getting some power estimates and may be useful to compare compute vs power metrics across different x86 CPUs. I've not been able to verify how accurate it really is, so I am interested to see how this works across a range of systems.
