I think what im after is more cant it be adapted to such a situation as
everyday desktop's to potentially improve communication between cores and maybe
manage the threads?
On 15/03/2019, 17:28, "Prentice Bisbal" <pbis...@pppl.gov> wrote:
We are definitely going that way, but for every day desktops, MPI is not
the way to go. Since most desktops are stand-alone islands,
multi-threading makes more sense, since it has less overhead compared to
MPI, and most desktop apps don't need the inter-node communications
provided by MPI.
--
Prentice
On 3/15/19 1:28 AM, Jonathan Aquilina wrote:
> I think what I was getting at is why not include the current HPC
practices to every day desktops in the sense since we are reaching certain limits
and have to write code to take advantage of more and more cores. Why not use MPI
and the like to help distribute the software side of things to the cores?
>
> It could be my entire concept of MPI is way of or im misunderstanding
completely how it works.
>
> -----Original Message-----
> From: Beowulf <beowulf-boun...@beowulf.org> On Behalf Of Prentice Bisbal
via Beowulf
> Sent: 14 March 2019 20:01
> To: beowulf@beowulf.org
> Subject: Re: [Beowulf] Large amounts of data to store and process
>
>> Then given we are reaching these limitations how come we don’t
integrate certain things from the HPC world into every day computing so to speak.
> We have. How many cores does your smartphone have?
>
> But in most cases over the past 25 years, HPC has been about
incorporating every day computing in HPC, not the other way around. For example,
the first beowulf clusters took desktop PCs and standard networking to build
supercomputers in the early/mid-90s. Then 10 years later, in the early/mid-00s,
HPC took GPUs and starting doing programming gymnastics to get the vector
processors on the GPUs to do physics calculations.
>
> --
> Prentice
>
> On 3/14/19 2:35 PM, Jonathan Aquilina wrote:
>> Then given we are reaching these limitations how come we don’t
integrate certain things from the HPC world into every day computing so to speak.
>>
>> On 14/03/2019, 19:14, "Douglas Eadline" <deadl...@eadline.org> wrote:
>>
>>
>> > Hi Douglas,
>> >
>> > Isnt there quantum computing being developed in terms of CPUs
at this
>> > point?
>>
>> QC is (theoretically) unreasonably good at some things at other
>> there may me classic algorithms that work better. As far as I
know,
>> there has been no demonstration of "quantum
>> supremacy" where a quantum computer is shown
>> to be faster than a classical algorithm.
>>
>> Getting there, not there yet.
>>
>> BTW, if you want to know what is going on with QC
>> read Scott Aaronson's blog
>>
>> https://www.scottaaronson.com/blog/
>>
>> I usually get through the first few paragraphs and
>> then whoosh over my scientific pay grade
>>
>>
>> > Also is it really about the speed any more rather then how
>> > optimized the code is to take advantage of the multiple cores
that a
>> > system has?
>>
>> That is because the clock rate increase slowed to a crawl.
>> Adding cores was a way to "offer" more performance, but introduced
>> the "multi-core tax." That is, programing for multi-core is
>> harder and costlier than a single core. Also, much
>> harder to optimize. In HPC we are lucky, we are used to
>> designing MPI codes that scale with more cores (no mater
>> where they live, same die, next socket, another server).
>>
>> Also, more cores usually means lower single core
>> frequency to fit into a given power envelope (die shrinks help
>> with this but based on everything I have read, we are about
>> at the end of the line) It also means lower absolute memory
>> BW per core although more memory channels help a bit.
>>
>> --
>> Doug
>>
>>
>> >
>> > On 13/03/2019, 22:22, "Douglas Eadline"
<deadl...@eadline.org> wrote:
>> >
>> >
>> > I realize it is bad form to reply ones own post and
>> > I forgot to mention something.
>> >
>> > Basically the HW performance parade is getting harder
>> > to celebrate. Clock frequencies have been slowly
>> > increasing while cores are multiply rather quickly.
>> > Single core performance boosts are mostly coming
>> > from accelerators. Added to the fact that speculation
>> > technology when managed for security, slows things down.
>> >
>> > What this means, the focus on software performance
>> > and optimization is going to increase because we can just
>> > buy new hardware and improve things anymore.
>> >
>> > I believe languages like Julia can help with this situation.
>> > For a while.
>> >
>> > --
>> > Doug
>> >
>> > >> 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-boun...@beowulf.org> On Behalf Of
Michael
>> > Di
>> > > Domenico
>> > >> Sent: 04 March 2019 17:39
>> > >> Cc: Beowulf Mailing List <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>
>> > >> 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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>> > >> http://www.beowulf.org/mailman/listinfo/beowulf
>> > >> _______________________________________________
>> > >> Beowulf mailing list, Beowulf@beowulf.org sponsored by
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>> > Computing
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>> > >> http://www.beowulf.org/mailman/listinfo/beowulf
>> > >
>> > >
>> > > --
>> > > Doug
>> > >
>> > >
>> > >
>> > >
>> > > _______________________________________________
>> > > Beowulf mailing list, Beowulf@beowulf.org sponsored by
Penguin
>> > Computing
>> > > To change your subscription (digest mode or unsubscribe)
visit
>> > > https://beowulf.org/cgi-bin/mailman/listinfo/beowulf
>> > >
>> >
>> >
>> > --
>> > Doug
>> >
>> >
>> >
>> >
>>
>>
>> --
>> Doug
>>
>>
>>
>> _______________________________________________
>> Beowulf mailing list, Beowulf@beowulf.org sponsored by Penguin
>> Computing To change your subscription (digest mode or unsubscribe)
>> visit https://beowulf.org/cgi-bin/mailman/listinfo/beowulf
> --
> Prentice Bisbal
> Lead Software Engineer
> Princeton Plasma Physics Laboratory
> https://www.pppl.gov
>
> _______________________________________________
> Beowulf mailing list, Beowulf@beowulf.org sponsored by Penguin Computing
To change your subscription (digest mode or unsubscribe) visit
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--
Prentice Bisbal
Lead Software Engineer
Princeton Plasma Physics Laboratory
https://www.pppl.gov
