[Wien] .machines problem

John Rundgren jru at kth.se
Sat Oct 1 15:19:00 CEST 2016


On 09/30/2016 09:44 AM, Peter Blaha wrote:
> Parallelization has always its limits and you need to do it 
> "sensible". In general it is NOT true, that more cores always mean 
> faster execution, but it could even slow down the calculations 
> dramatically.
>
> a) Hardware: You say your computers have "8 threads". Do you mean they 
> have really 8 cores (like some Xeons), or are these 4 core machines 
> with hyperthreading. In the latter case 8 parallel jobs are useless, 
> as hyperthreading provides only a logical core, but not a "real" one.
> In addition, it is well known that modern multi-core cpus are very 
> often "memory-bound", this means, their memory bus is too slow to 
> saturate all cores simultaneously. Thus it is often "natural" that a N 
> core job is NOT N times as fast as a single core job.
> Another factor is disk I/O, which on some systems can become VERY slow 
> (over the network or on a single node) the more jobs are running.
>
> b) Software: There is a "multithreading" option with Intel, and 
> setting OMP_NUM_THREAD=2 makes lapw1 nearly twice as fast as 
> OMP_NUM_THREAD=1. Of course, when using this, you should reduce the 
> number of parallel jobs by 2. Check with "top" your cpu usage. When 
> you see "200 %" for an lapw1 process, it is this multithreading.
> lapw1para: it starts the parallel processes with some "DELAY", 
> otherwise this leads to problems on some systems. If for instance 
> DELAY=1, it means that spanning 16 lapw1 will take at least 16 
> seconds. If your testcase runs only for 2 seconds/lapw1, you can 
> imagine that you will not get any speedup, but a drastic slowdown. If 
> it runs for 5 min, the 16 seconds are negligible and you should see a 
> speedup from 5 to 2.5 min (provided you have enough k-points !, check 
> with "testpara").
>
> It is always good, if you can "watch" your parallel job on the two 
> nodes with "top" (in two different windows). You should see how they 
> start, how they run (do the get nearly 100 or 200% of the cores most 
> of the time), and how they stop (nearly same time, or very unbalanced) ?
>
>
> On 09/28/2016 03:21 PM, John Rundgren wrote:
>> Dear W2k team,
>> On my desk are two identical computers alpha and beta of 8 threads each.
>>
>> How is .machines set up such that k-point parallelization goes twice as
>> fast using alpha & beta compared with using single alpha?
>>
>> Unfortunately, my testing UG 5.5.4 responds with error diagnostics.
>>
>> When I try the following .machines with and without #,
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   1:alpha
>>   #1:beta
>>   granularity:1
>>   extrafine:1
>> computing time comes out similar in both cases. I would like to see
>> sixteen threads executing twice as fast as eight.
>>
>> Regards,
>> John Rundgren KTH
>>
>>
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>
Dear Peter,
Thanks for your comments on the use of several computers. A simple 
reason to my failure seems to be that my Linux set-up is defective.

My computers are,
   homer = Xenon E3-1270 v3 @ 3.50GHz, 4 cpus and 8 threads,
   odysvs = i7-3770 3.40GHz, 4 cpus and 8 threads,
homer being the main computer.

When the following .machines files,
   1:homer
   1:homer
   1:homer
   1:homer
   1:homer
   1:homer
   1:homer
   1:homer
   granularity:1
   extrafine:1
and
   1:odysvs
   1:odysvs
   1:odysvs
   1:odysvs
   1:odysvs
   1:odysvs
   1:odysvs
   1:odysvs
   granularity:1
   extrafine:1
are used separately as input to homer, the execution takes place in 
homer. In both cases the System Monitor of odysvs is idle, although in 
the second case the dayfile refers to odysvs.

The following ssh commands were made beforehand,
  homer> ssh-keygen -t rsa
  homer> ssh-copy-id odysvs,
test,
  homer> ssh odysvs pwd > /home/jru, without password.
Any computer mentioned in .machines seems to be treated as "localhost".

Does this test give a clue to what fails?
Regards, John



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