[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/
> 
>       
> 
>      [5]NewIRASRLogo
> 
>       
> 
>       
> 
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>    Dr. Adam Deller         
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>    Centre for Astrophysics & Supercomputing 
>    Swinburne University of Technology    
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