It is also possible that RM "prediction" has decided that additional
processes are not needed. It
appears that there were likely 64 work items dispatched, plus the 6
completed, leaving only
30 that were "idle". If these work items appeared to be completing
quickly, the RM would decide
that scale-up would be wasteful and not do it.
Very gory details if you're interested:
The time to start a new processes is measured by the RM based on the
observed initialization time of the processes plus an estimate of how
long it would take to get
a new process actually running. A fudge-factor is added on top of this
because in a large operation
it is wasteful to start processes (with associated preemptions) that
only end up doing a "few" work
tems. All is subjective and configurable.
The average time-per-work item is also reported to the RM.
The RM then looks at the number of work items remaining, and the
estimated time needed to
processes this work based on the above, and if it determines that the
job will be completed before
new processes can be scaled up and initialized, it does not scale up.
For short jobs, this can be a bit inaccurate, but those jobs are short :)
For longer jobs, the time-per-work-item becomes increasingly accurate so
the RM prediction tends
to improve and ramp-up WILL occur if the work-item time turns out to be
larger than originally
thought. (Our experience is that work-item times are mostly uniform
with occasional outliers, but
the prediction seems to work well).
Relevant configuration parameters in ducc.properties:
# Predict when a job will end and avoid expanding if not needed. Set to
false to disable prediction.
ducc.rm.prediction = true
# Add this fudge factor (milliseconds) to the expansion target when
using prediction
ducc.rm.prediction.fudge = 120000
You can observe this in the rm log, see the example below. I'm
preparing a guide to this log; for now,
the net of these two log lines is: the projection for the job in
question (job 208927) is that 16 processes
are needed to complete this job, even though the job could use 20
processes at full expanseion - the BaseCap -
so a max of 16 will be scheduled for it, subject to fair-share constraint.
17 Nov 2014 15:07:38,880 INFO RM.RmJob - */getPrjCap/* 208927 bobuser
O 2 T 343171 NTh 128 TI 143171 TR 6748.601431980907 R 1.8967e-02 QR 5043
P 6509 F 0 ST 1416254363603*/return 16/*
17 Nov 2014 15:07:38,880 INFO RM.RmJob - */initJobCap/* 208927 bobuser
O 2 */Base cap:/* 20 Expected future cap: 16 potential cap 16 actual cap 16
Jim
On 11/17/14, 3:44 PM, Eddie Epstein wrote:
DuccRawTextSpec.job specifies that each job process (JP)
run 8 analytic pipeline threads. So for this job with 100 work
items, no more than 13 JPs would ever be started.
After successful initialization of the first JP, DUCC begins scaling
up the number of JPs using doubling. During JP scale up the
scheduler monitors the work item completion rate, compares that
with the JP initialization time, and stops scaling up JPs when
starting more JPs will not make the job run any faster.
Of course JP scale up is also limited by the job's "fair share"
of resources relative to total resources available for all preemptable jobs.
To see more JPs, increase the number and/or size of the input text files,
or decrease the number of pipeline threads per JP.
Note that it can be counter productive to run "too many" pipeline
threads per machine. Assuming analytic threads are 100% CPU bound,
running more threads than real cores will often slow down the overall
document processing rate.
On Mon, Nov 17, 2014 at 6:48 AM, Simon Hafner <reactorm...@gmail.com> wrote:
I fired the DuccRawTextSpec.job on a cluster consisting of three
machines, with 100 documents. The scheduler only runs the processes on
two machines instead of all three. Can I mess with a few config
variables to make it use all three?
id:22 state:Running total:100 done:0 error:0 retry:0 procs:1
id:22 state:Running total:100 done:0 error:0 retry:0 procs:2
id:22 state:Running total:100 done:0 error:0 retry:0 procs:4
id:22 state:Running total:100 done:1 error:0 retry:0 procs:8
id:22 state:Running total:100 done:6 error:0 retry:0 procs:8
