[Difx-users] Speedup Factor plot_logtime.py
Geoff Crew
gbc at haystack.mit.edu
Fri Apr 28 08:00:04 EDT 2017
I'm always asked (Q):"how long is it going to take". So
1/Speedup = total(corr_time) / total(obs_time)
is of some practical value. And the corollary to (Q) is
"that's not fast enough".
--
Geoff (gbc at haystack.mit.edu)
On Fri, Apr 28, 2017 at 09:44:06AM +1000, Adam Deller wrote:
> Depends on what people want to use the reported speedup for, I suppose.
> The easy way is to simply report sum(obs_time) / max(correlate_time), like
> you suggest. That naturally takes into account inefficiencies in
> allocation across multiple jobs and gives a 'real world' speedup. So if
> someone splits their jobs in such a way that one job takes much longer,
> then the speedup reported is representative of the real-world time taken
> to finish everything. I think this is a good default.
> That's one extreme. The other extreme is if you assume that as soon as
> one job finishes someone else takes those resources and starts using them
> for something else useful, and so you want to report the weighted average
> speedup of the individual jobs. Each individual job has a speedup of s_i
> = obs_time_i/correlate_time_i, and then you form the overall speedup by
> sum(s_i * obs_time_i) / sum(obs_time_i).
> Those two measures are useful for different things, so probably it would
> be good to report both. But I think for most people the first is more
> useful.
> Cheers,
> Adam
> On 28 April 2017 at 08:37, Stuart Weston <[1]stuart.weston at aut.ac.nz>
> wrote:
>
> The simple one head -> many workers we have speedup factor:
>
>
>
> Speedup = obs_time / corr_time
>
>
>
> So say 4hrs observing is correlated in ~10.3 hrs, a speedup of 0.388.
>
>
>
> Now in the scenario where we split the job between multiple heads, so
> head_1 does 0-2 hrs and head_2 does 2-4 hrs. How do we want to define
> the speedup here ?
>
>
>
> Each will take slightly different times due to many external factors,
> but both are started at the same time (within fractions of a second)
>
>
>
> So head_1 takes 5.1 hrs to correlate 2 hrs of observing, and head_2
> takes 5.3 hrs for the other 2 hrs.
>
>
>
> We have correlated the same 4hrs of data but in approx half the elapsed
> time. So would you be happy for me to define speedup now as:
>
>
>
> Speedup = sum(obs_time_1, obs_time_2) / max(corr_time(head_1,head_2))
>
>
>
> In this case it would be say 4/5.3 = 0.75. Now this doesn’t work if
> the split is un-even for the obs_time between the two heads.
>
>
>
> Just looking for a way to define my benchmarks (agreeable to the
> community) for cloud correlation and using multiple heads and breaking a
> job up between heads on different hypervisors.
>
>
>
> Stuart Weston Bsc (Hons), MPhil (Hons), MInstP
>
> Mobile: 021 713062
>
> Skype: stuart.d.weston
> Email: [2]stuart.weston at aut.ac.nz
>
> [3]http://www.atnf.csiro.au/people/Stuart.Weston/index.html
>
> Software Engineer
> Institute for Radio Astronomy & Space Research (IRASR)
> School of Computing & Mathematical Sciences
> Faculty of Creative Technologies
> Auckland University of Technology, New Zealand.
>
> [4]http://www.irasr.aut.ac.nz/
>
>
>
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>
>
>
>
>
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> --
> !=============================================================!
> Dr. Adam Deller
> ARC Future Fellow, Senior Lecturer
> Centre for Astrophysics & Supercomputing
> Swinburne University of Technology
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