[Beowulf] MPI, fault handling, etc.

[Justin Y. Shi]

Great conversations for starting an exciting weekend!

The critical architecture feature for extreme scale of anything is its
growth potential. Even for the large data transfer example, the
architecture does not breakdown. Fatter pipes and more pipes are guaranteed
to solve the problems at hand.

The performance trading I mentioned was meant for computing, not for
communication.  The "bare metal" APIs force the programs onto the hardware
so that the granularity of processing cannot be adjusted after the programs
are compiled. No one is willing to recode because the hardware is shared
with others or simply upgraded with a different processor or interconnect.
This results in great efficiency losses that most of us do not want to see.

The compiler and dependency questions were also well taken here. Syntacitc
"surgars" cannot solve the dependency issues since they are ultimately
static at the compile time. Even most savvy programmers cannot guarantee
the correct and most efficient partitioning. In my humble opinion, leaving
this mess to the runtime system is the only way out. We have tested this
hypothesis by comparing optimized MPI program against SMC (statistic
multiplexed computing) program with varying processing granularities.  We
have recorded consistent wins despite of larger overheads of SMC runtime
"data matching/fault tolerance engine". Two years ago, we even compared MPI
with SMC wrapped MPI and still scored consistent wins. Unfortunately, most
people in the SC committees did not believe these results. And no one is
willing to try for themselves either. I do not see any downloads from my
github site except for my own students.

Thanks again to Mr. C for restarting the life of this list!

Enjoy the great sunny weekend!

Justin



On Fri, Mar 11, 2016 at 4:28 PM, Lux, Jim (337C) <james.p.lux at jpl.nasa.gov>
wrote:

> I’ll agree that architecture is the key.
>
>
>
> Packet switching breaks when you need to move petabytes in a reasonable
> time across large distances, because each intermediate node has to have
> storage for DataRate*RoundTripTime (so that flow control works).  If you’re
> moving Tbits/sec (e.g. something like 3d medical images) across the
> country, that means your store and forward switches need to store a fair
> amount of data.
>
>
>
> That’s a narrow example..
>
>
>
> But I’m not sure you can always trade things to get more reliability
> without impacting performance.
>
>
>
> At some point, you can probably take an information theory approach.
> You’ve got a channel with a certain error rate, and you want to push a
> certain data rate through it at a particular (lower) error rate.
>
>
>
>
>
> Jim Lux
>
> (818)354-2075 (office)
>
> (818)395-2714 (cell)
>
>
>
> *From:* Justin Y. Shi [mailto:shi at temple.edu]
> *Sent:* Thursday, March 10, 2016 3:12 PM
> *To:* Lux, Jim (337C) <james.p.lux at jpl.nasa.gov>
> *Cc:* Douglas Eadline <deadline at eadline.org>; beowulf at beowulf.org
>
> *Subject:* Re: [Beowulf] MPI, fault handling, etc.
>
>
>
> Not only high reliability is possible, incrementally higher performance is
> also possible at the same time or the Internet would crumble by now.
>
>
>
> The key is in the architecture. The overheads can be traded for
> performance gains elsewhere without sacrificing reliability or scalability.
>
>
>
> In other words, the packet switching network proved that you can have the
> cake and eat it at the same time.
>
>
>
> Justin
>
>
>
> On Thu, Mar 10, 2016 at 4:43 PM, Lux, Jim (337C) <james.p.lux at jpl.nasa.gov>
> wrote:
>
> High reliability is possible, at the expense of substantial additional
> resources. Packet switching requires storage to hold unacknowledged packets
> that might need to be resent and it requires time for the resends.
>
>
>
> If you have time critical processes with fine granularity, then you have
> to budget in the memory and time to achieve that reliability.
>
>
>
> The other thing is that some high reliability approaches do not scale
> well.  A bit error rate of 1E-9 would be outstandingly good on a radio
> link, but if you’re sending data at 1Gbps, that’s an error every second.
> If you’re sending data at 1 Tbps, that’s an error every millisecond.  If
> your packets are a millisecond long, then you get no successful packets
> through.
>
>
>
> Fortunately, “over the wire” error rates are much better (1E-12 wouldn’t
> be unusual), but that’s still a once every 20 minutes sort of error rate at
> 1 Gbps.  And if you’re talking about interprocessor interconnects.. a 12x
> Infiniband is about 300 Gb/sec.  A 1E-12 error rate would be an error every
> few seconds.  At those rates, of course you’re going to use some sort of
> forward error correction (you’re not going to depend on retry), and that
> might be an effective data rate reduction of 30-50% (depending on the
> coding).
>
>
>
>
>
>
>
> Jim Lux
>
> (818)354-2075 (office)
>
> (818)395-2714 (cell)
>
>
>
> *From:* Justin Y. Shi [mailto:shi at temple.edu]
> *Sent:* Thursday, March 10, 2016 1:14 PM
> *To:* Douglas Eadline <deadline at eadline.org>
> *Cc:* Lux, Jim (337C) <james.p.lux at jpl.nasa.gov>; beowulf at beowulf.org
> *Subject:* Re: [Beowulf] MPI, fault handling, etc.
>
>
>
> Not that fast though. 100% reliability is practically achievable. And we
> are enjoying the results everyday. I mean the wireless and wired packet
> switching networks.
>
>
>
> The problem is our tendency of drawing fast conclusions. The one human
> made architecture that defies the "curse" of component failure is the
> statistic multiplexing principle (or packet switching).
>
>
>
> It has proven to work perfectly using growing number of not-so-reliable
> devises without suffering the scalability dilemma.  We should learn how to
> apply that technology to extreme scale computing. To this day, the full
> extends of protocol logics still cannot be adequately described formally on
> paper. But they work well, if done right.
>
>
>
>
>
> Justin
>
>
>
> On Thu, Mar 10, 2016 at 3:44 PM, Douglas Eadline <deadline at eadline.org>
> wrote:
>
>
> > I will supper C's "hater" listing effort just to keep a spot light on the
> > important subject.
> >
> > The question is not MPI is efficient or not. Fundamentally, all
> > electronics
> > will fail in unexpected ways. Bare metal computing was important decades
> > ago but detrimental to large scale computing. It is simply flawed for
> > extreme scale computing.
> >
> > The Alan Fekete, Nancy Lynch, John Spinneli's impossible proof is the
> > fundamental "line in the sand" that cannot be crossed.
> >
> > The corollary of that proof is that it is impossible to detect failure
> > reliably either. Therefore, those efforts for for runtime
> > detection/repair/reschedule are also flawed for extreme scale computing.
> >
>
> Well on that note, I suppose we should just call it day.
> Although, some thought Godel would put the whole math thing
> out of business as well.
>
> --
> Doug
>
>
>
>
>
> > Justin
> >
> > On Thu, Mar 10, 2016 at 8:44 AM, Lux, Jim (337C)
> > <james.p.lux at jpl.nasa.gov>
> > wrote:
> >
> >> This is interesting stuff.
> >> Think back a few years when we were talking about checkpoint/restart
> >> issues: as the scale of your problem gets bigger, the time to checkpoint
> >> becomes bigger than the time actually doing useful work.
> >> And, of course, the reason we do checkpoint/restart is because it’s
> >> bare-metal and easy.  Just like simple message passing is “close to
> >> the
> >> metal” and “straightforward”.
> >>
> >> Similarly, there’s “fine grained” error detection and correction:
> >> ECC
> >> codes in memory; redundant comm links or retries.  Each of them imposes
> >> some speed/performance penalty (it takes some non-zero time to compute
> >> the
> >> syndrome bits in a ECC, and some non-zero time to fix the errored
> >> bits… in
> >> a lot of systems these days, that might be buried in a pipeline, but the
> >> delay is there, and affects performance)
> >>
> >> I think of ECC as a sort of diffuse fault management: it’s pervasive,
> >> uniform, and the performance penalty is applied evenly through the
> >> system.
> >> Redundant (in the TMR sense) links are the same way.
> >>
> >> Retries are a bit different.  The “detecting” a fault is diffuse and
> >> pervasive (e.g. CRC checks occur on each message), but the correction of
> >> the fault is discrete and consumes resources at that time.  In a system
> >> with tight time coupling (a  pipelined systolic array would be the sort
> >> of
> >> worst case), many nodes have to wait to fix the one that failed.
> >>
> >> A lot depends on the application: tighter time coupling is worse than
> >> embarrassingly parallel (which is what a lot of the “big data” stuff
> >> is:
> >> fundamentally EP, scatter the requests, run in parallel, gather the
> >> results).
> >>
> >> The challenge is doing stuff in between:  You may have a flock with
> >> excess
> >> capacity (just as ECC memory might have 1.5N physical storage bits to be
> >> used to store N bits), but how do you automatically distribute the
> >> resources to be failure tolerant.   The original post in the thread
> >> points
> >> out that MPI is not a particularly facile tool for doing this.  But
> >> I’m not
> >> sure that there is a tool, and I’m not sure that MPI is the root of
> >> the
> >> lack of tools.    I think it’s that moving from close to the metal is
> >> a
> >> “hard problem” to do in a generic way.  (The issues about 32 bit
> >> counts are
> >> valid, though)
> >>
> >>
> >> James Lux, P.E.
> >>
> >> Task Manager, DHFR Space Testbed
> >>
> >> Jet Propulsion Laboratory
> >>
> >> 4800 Oak Grove Drive, MS 161-213
> >>
> >> Pasadena CA 91109
> >>
> >> +1(818)354-2075
> >>
> >> +1(818)395-2714 (cell)
> >>
> >>
> >>
> >> _______________________________________________
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> >>
> >
>
> > --
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>
> >
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>
> --
> Doug
>
> --
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