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On 02/11/2007, at 8:09 AM, Wilt, Charles wrote:

Well all the jobs are submitted to a single threaded queue.... (single threaded due to a design
limitation ).

Right now, there's 1800+ jobs waiting in the queue to run. CPU usage at 100%.

Seems like the CPU time spent creating the separate jobs would be better used running the process needed.

Yes, it would be. Although the overhead is less noticeable on newer hardware. Not sure if that is because Rochester have made the job start more efficient or simply because new hardware is so much faster.

Performance Tools will show you the job overhead. In fact this very scenario was used in performance classes (e.g., interactive job printed a picking slip, process changed to submit print, performance sucked, changed to populate data queue instead of submit, performance better).


Ideally, I'd like to have the process using data queues and multiple NEP's to handle the processing.
But it'd be a bigger job to allow multiple process to run at the same time if it's even possible (the
limitation is not an iSeries application design issue, an AIX box is involved).

I seem to remember an article with a example program that used data queues and automatically increased
or decreased the number of NEP jobs running. Anybody got a link to that?

I don't have a link but I know I've discussed this in previous appends. Check the archives. I've written code to do this. Essentially you start a minimum number of NEPs to handle the data queue. Each NEP tracks statistics of how many entries it is processing over a given time. As each NEP pulls an entry off the queue it checks the queue attributes to see how many entries are left. If this exceeds a certain threshold based on the processing statistics the NEP submits another copy of itself. Each NEP must also time-out when waiting for an new entry and check how many copies are active. If too many (again based on a certain threshold) it ends itself. Nett result is a wave of NEPs increasing as work load increases and reducing as there is less to do.

Regards,
Simon Coulter.
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