Interesting stuff for sure but how does something like Julia redefine the
meaning of HPC. 1) with the cpu performance we have and all these multicore
enterprise systems and even off the shelf desktop systems question becomes is
HPC nearing end of life so to speak with languages such as Julia?
Regards,
Jonathan
From: John Hearns <hear...@googlemail.com>
Sent: 10 March 2019 12:06
To: Jonathan Aquilina <jaquil...@eagleeyet.net>; Beowulf Mailing List
<beowulf@beowulf.org>
Subject: Re: [Beowulf] Large amounts of data to store and process
Also interesting to me is mixed precision arithmetic - which Julia makes easy.
We are going to see more and more codes which will choose lower precision to
save energy, not just for running Deep Learning modesl on GPUs.
I share a code snippet not written by me. I think it is a brilliant idea.
Here a researcher in ocean modelling is able to change the types of numbers his
model uses. Run with lower precision and see what changes.
I guess this is easy in C/C++ also, but the concept is fantastic.
# NUMBER FORMAT OPTIONS
const Numtype = Float32
#const Numtype = Posit{16,2}
#const Numtype = Main.FiniteFloats.Finite16
#const Numtype = BigFloat
#setprecision(7)
Using Julias tyoe system you can change the type of the numbers.
Here the calculation is run as NumType - and you can make NumType to be 32 bit
floats or arbitrarily large floats
I see Float64 is not listed - that should be there.
On Sun, 10 Mar 2019 at 10:57, John Hearns
<hear...@googlemail.com<mailto:hear...@googlemail.com>> wrote:
Jonathan, damn good question.
There is a lot of debate at the moment on how 'traditional' HPC can co-exist
with 'big data' style HPC.
Regarding Julia, I am a big fan of it and it bring a task-level paradigm to HPC
work.
To be honest though, traditional Fortran codes will be with us forever. No-one
is going to refactor say a weather forecasting model in a national centre.
Also Python has the mindset at the moment. I have seen people in my company
enthusiastically taking up Python.
Not because of a measured choice after scanning dozens of learned papers and
Reddit reviews etc.
If that was the case then they might opt for Go or some niche language.
No, the choice is made because their colleagues already use Python and pass on
start up codes, and there is a huge Python community.
Same with traditional HPC codes really - we all know that batch scripts are
passed on through the generations like Holy Books,
and most scientists dont have a clue what these scratches on clay tablets
actually DO.
Leading people to continue to run batch jobs which are hard wired for 12 cores
on a 20 core machine etc. etc.
(*) this is worthy of debate. In Formula 1 whenever we updated the version of
our CFD code we re-ran a known simulation and made sure we still had
correlation.
It is inevitable that old versions of codes will sop being supported
On Sun, 10 Mar 2019 at 09:29, Jonathan Aquilina
<jaquil...@eagleeyet.net<mailto:jaquil...@eagleeyet.net>> wrote:
Hi All,
Basically I have sat down with my colleague and we have opted to go down the
route of Julia with JuliaDB for this project. But here is an interesting
thought that I have been pondering if Julia is an up and coming fast language
to work with for large amounts of data how will that affect HPC and the way it
is currently used and HPC systems created?
Regards,
Jonathan
-----Original Message-----
From: Beowulf <beowulf-boun...@beowulf.org<mailto:beowulf-boun...@beowulf.org>>
On Behalf Of Michael Di Domenico
Sent: 04 March 2019 17:39
Cc: Beowulf Mailing List <beowulf@beowulf.org<mailto:beowulf@beowulf.org>>
Subject: Re: [Beowulf] Large amounts of data to store and process
On Mon, Mar 4, 2019 at 8:18 AM Jonathan Aquilina
<jaquil...@eagleeyet.net<mailto:jaquil...@eagleeyet.net>> wrote:
>
> As previously mentioned we don’t really need to have anything indexed so I am
> thinking flat files are the way to go my only concern is the performance of
> large flat files.
potentially, there are many factors in the work flow that ultimately influence
the decision as others have pointed out. my flat file example is only one,
where we just repeatable blow through the files.
> Isnt that what HDFS is for to deal with large flat files.
large is relative. 256GB file isn't "large" anymore. i've pushed TB files
through hadoop and run the terabyte sort benchmark, and yes it can be done in
minutes (time-scale), but you need an astounding amount of hardware to do it
(the last benchmark paper i saw, it was something
1000 nodes). you can accomplish the same feat using less and less complicated
hardware/software
and if your dev's are willing to adapt to the hadoop ecosystem, you sunk right
off the dock.
to get a more targeted answer from the numerous smart people on the list, you'd
need to open up the app and workflow to us. there's just too many variables
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