[Beowulf] Large amounts of data to store and process

[Douglas Eadline]

> 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?


First, IMO good choice.

Second a short list of actual conversations.

1) "This code is written in Fortran." I have been met with
puzzling looks when I say the the word "Fortran." Then it
comes, "... ancient language, why not port to modern ..."
If you are asking that question young Padawan you have
much to learn, maybe try web pages"

2) I'll just use Python because it works on my Laptop.
Later, "It will just run faster on a cluster, right?"
and "My little Python program is now kind-of big and has
become slow, should I use TensorFlow?"

3) <mcoy>
"Dammit Jim, I don't want to learn/write Fortran,C,C++ and MPI.
I'm a (fill in  domain specific scientific/technical position)"
</mcoy>

My reply,"I agree and wish there was a better answer to that question.
The computing industry has made great strides in HW with
multi-core, clusters etc. Software tools have always lagged
hardware. In the case of HPC it is a slow process and
in HPC the whole programming "thing" is not as "easy" as
it is in other sectors, warp drives and transporters
take a little extra effort.

4) Then I suggest Julia, "I invite you to try Julia. It is
easy to get started, fast, and can grow with you application."
Then I might say, "In a way it is HPC BASIC, it you are old
enough you will understand what I mean by that."

The question with languages like Julia (or Chapel, etc) is:

  "How much performance are you willing to give up for convenience?"

The goal is to keep the programmer close to the problem at hand
and away from the nuances of the underlying hardware. Obviously
the more performance needed, the closer you need to get to the hardware.
This decision goes beyond software tools, there are all kinds
of cost/benefits that need to be considered. And, then there
is IO ...

--
Doug







> Regards,
> Jonathan
> -----Original Message-----
> From: Beowulf <beowulf-bounces at beowulf.org> On Behalf Of Michael Di
Domenico
> Sent: 04 March 2019 17:39
> Cc: Beowulf Mailing List <beowulf at beowulf.org>
> Subject: Re: [Beowulf] Large amounts of data to store and process On
Mon, Mar 4, 2019 at 8:18 AM Jonathan Aquilina
<jaquilina at 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
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-- 
Doug