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==== CPU vs GPU ====

So the question was raised what does our usage look like between CPU and GPU devices? I have no idea what the appropriate metrics would be but lets start with comparing the hardware deployed. We'll also need to make some assumptions

  * Data is period June 1 to June 25, 2018 (job information data ages out)
    * Maybe build monthly script if this turns out to be usable info
  * That period covers 600 hours of time
  * Assume 99% utilization of cpu core or gpu device
  * Available Hours is measured per physical CPU core but by GPU device (exclusivity and persistence modes on)
  * There is no good/bad metric
  * Never collated such data before
  * The GPU jobs are detected based on GPU resource reservations (gpu= flag)


^ Metric ^ CPU ^ Ratio ^ GPU ^ Notes June 2018 ^
| Device Count | 72  |  3:1  |  24 | cpu all intel, gpu all nvidia |
| Core Count | 1,192 |  1:54  |  64,300 | physical only  | 
| Memory | 7,408  |  51:1  |  144 | GB |
| Teraflops | 38  |  1.5:1  |  25 | double precision, floating point, theoretical |
| Job Count | 2,834  |  3:1  |  1,045 | processed jobs irregardless of exit status |
| Avail Hours | 715,200  |  50:1  |  14,400 | total for cpu cores, total for gpus |
| Job Hours | 221,136  |  77:1  |  2,872 | cumulative hours of consumed usage |
| Job Hours % | 31  |  6:1  |  5 | as a percentage of available |
| Avail Hours2 | 561,600  |  39:1  |  14,400 | total cpu cores minus hp12's 256 cores, total gpus |
| Job Hours2 % | 39  |  8:1  |  5 | more realistic...hp12 rarely used |

The logs showing gpu %util confirm the extremely low GPU usage. When concatenating the four gpu %util values into a string, since 01Jan2017, the string '0000' has occurred 10 million times out of 16 million observations. (GPUs are polled every 10 mins). The surprising strong GPU job count is due to the Amber group launching lots of small GPU jobs.

So were these 25 days in June 2018 an oddity? 

^  Total Monthly CPU+GPU Hours  ^^^^^^^^^^^
^Ju17^Aug17^Sep17^Oct17^Nov17^Dec17^Jan18^Feb18^Mar18^Apr18^May18^
|313,303|273,051|128,390|111,224|280,101|51,727|306,453|222,585|437,959|262,227|294,724|

March is Honors' Theses time so lets look at Jul17 (no GTX gpus) so we can compare that to Jul18 in august. 31 days in July is 744 hours.

^ Metric ^ CPU ^ Ratio ^ GPU ^ Notes July 2017 ^
| Device Count | 72  |  4:1  |  20 | cpu all intel, gpu all nvidia |
| Core Count | 1,192 |  1:42  |  50,000 | physical only  | 
| Memory | 7,408  |  74:1  |  100 | GB |
| Teraflops | 38  |  1.7:1  |  23 | double precision, floating point, theoretical |
| Job Count | 12,798  |  18:1  |  722 | processed jobs irregardless of exit status |
| Avail Hours | 886,848  |  60:1  |  14,880 | total cpu cores, total gpus |
| Job Hours |  260,997  |  69:1  |  3,805 | cumulative hours of consumed usage |
| Job Hours % | 30  |  1:1  |  26 | as a percentage of available |
| Avail Hours2 | 696,384  |  47:1  |  14,880 | total for cpu cores minus hp12's 256 cores, total for gpus |
| Job Hours2 % | 37  |  1.5:1  |  26 | more realistic...hp12 rarely used |

  * Some noise in this data with the inability to match start and end of job (~15% of records)
  * The assumption that ''hp12'' was barely used in July 2017 might not be correct

Based on Jul17 we process about 60-70 times more CPU Job Hours than GPU Job Hours, that seems consistent with Jun18. The metric of Job Hours consumed versus Available Hours in %, the picture is probably more like Jul17...30-40% of CPU cycles are consumed and 25% of GPU cycles. 

If we take total hours consumed from Usage Report (the 313,303 hours for Jul17) we consumed about 45% of available hours (without hp12 in the mix).

We shall wait for Jul18 metrics.

==== July 2018 ====

^ Metric ^ CPU ^ Ratio ^ GPU ^ Notes July 2017 ^
| Device Count | 74  |  3:1  |  24 | cpu all intel, gpu all nvidia |
| Core Count | 1,208 |  1:53  |  64,336 | physical only  | 
| Memory | 7,516  |  52:1  |  144 | GB |
| Teraflops | 38  |  1.5:1  |  25 | double precision, floating point, theoretical |
| Job Count | 12,798  |  18:1  |  722 | processed jobs irregardless of exit status |
| Avail Hours | 898,752  |  50:1  |  17,856 | total cpu cores, total gpus |
| Job Hours |  322,207  |  1732:1  |  186 | cumulative hours of consumed usage |
| Job Hours % | 36  |  36:1  |  1 | as a percentage of available |


Based on the utilization string '0000', meaning all gpus are idle on a single node as polled every 10 mins during July 2018, the GTX gpus were 65% completely idle and the K20s were 61% completely idle.

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