[Beowulf] At exascale, being oblivious to a fault keeps apps running

Eugen Leitl eugen at leitl.org
Thu Dec 13 04:00:01 PST 2012


http://ascr-discovery.science.doe.gov/exascale/exa_fault_print.html

ASCR DISCOVERY – Exascale Science

At exascale, being oblivious to a fault keeps apps running

Posted December 12, 2012

Computer scientist Maya Gokhale is optimistic about exascale computing’s
fast, bright future. But to achieve this success, the Lawrence Livermore
National Laboratory researcher and her colleagues focus on an exascale
computer’s inevitable failures, from processor cores, to memory and
communications links – the unprecedented millions of hardware parts. The
failure of any one of these elements could hobble a 1018
floating-point-operation scientific calculation.

“Failure will be a constant companion in exascale computing,” says Gokhale, a
30-year veteran in thinking about the silicon edge of high-performance
computing (HPC). “We expect applications will have to function in an
environment of near-continual failure: something, somewhere in the machine
will always be malfunctioning, either temporarily, or simply breaking.”

Yet just as the most successful entrepreneurs learn and grow from overcoming
failure, HPC scientists like Gokhale believe planning for breakdowns at the
exascale will not only improve scientific computing; it may be a tipping
point toward a new paradigm in how supercomputers are programmed.

As part of its effort to outsmart faults at the exascale, the DOE Office of
Science is looking to FOX – the Fault-Oblivious eXtreme-scale execution
environment project.

Led by Gokhale, FOX is an ambitious project, now in the last of its three
years, with a transformational vision. To succeed at the exascale, FOX’s
creators believe, HPC must fundamentally change its approach to failure –
from one of seeking hardware perfection at all costs to one of embracing
hardware failure when designing a new generation of operating systems.

The failure frontier

With 100 million to a billion processing cores spread among millions of
nodes, an exascale supercomputer will represent an unprecedented opportunity
for scientific computing. But it also will spawn a new frontier in terms of
failure rates.

Although the projected hardware failure rate in an exascale machine is still
a subject of intense debate, in 2011 the DOE Advanced Scientific Computing
Advisory Committee identified exascale resilience (the ability to deal with
such faults) “as a black swan – the most difficult, under-addressed issue
facing HPC.”

What makes it difficult is that there’s an HPC community consensus that
traditional failure-avoidance strategies themselves break down at the
exascale.

“The underlying assumption with supercomputers as they’re operated today is
that either the machine is perfect or you just reset,” says Ron Minnich, a
computer scientist now at Google, who initiated the FOX research program
while at Sandia National Laboratories. “As an engineering accomplishment this
near perfection is really incredible, but if we think about scaling up, it’s
hard to see how that assumption can be sustained either technically or in
terms of costs.”

The problem is that to avoid data loss to a system shutdown, today’s largest
computers operate on the same approach to hardware glitches as personal
computers: If there’s a significant error, the system shuts down, and you
reboot it. This is called defensive checkpointing: routinely pausing a
computation to save data from all nodes back to a parallel file system. It’s
the equivalent of everyone in a Fortune 500 company stopping work to back up
their hard drives simultaneously onto a common drive, then going back to
work.

One estimate suggests that regular checkpointing, which requires 30 minutes
to restart a computation, would slow an exascale machine to less than half
its projected efficiency.

“We know that with defensive checkpointing,” Gokhale says, “given the
expected fault rates, you could spend all of your time checkpointing and
getting no work done, so we absolutely have to have something different.”

That something different encompasses an evolutionary approach toward working
with, rather than against, faults.

Rethinking failure

Rather than perfection, FOX achieves its objectives through adaptability,
Gokhale says. It’s a fault-tolerant approach that grows out of three primary
intertwined innovations: core specialization, a distributed data store and a
fault-aware task management approach.

What ties these together is a fundamental shift in the software responsible
for dealing with faults – moving from a specific application to a
fault-oblivious operating system, like FOX.

“What we’re envisioning with fault-oblivious computing is that the
application wouldn’t know about it,” Gokhale explains, “and the application
wouldn’t be responsible for saving data periodically, or recovering data.”

A fault-oblivious approach to HPC would be a major about-turn. Today, on
supercomputers such as Intrepid, Argonne National Laboratory’s Blue Gene/P,
the operating system starts a run and then essentially disappears, handing
over control to the scientific application.

“The effort in HPC for the past 20 years has been operating system bypass,”
Minnich says. “That’s just another way of saying the operating system can’t
do a good enough job, and we have to let the application do the job of moving
the data.”

Past weaknesses in HPC operating systems, he adds, spurred the move away from
them. However, at the exascale, Minnich says, it’s time to give the job of
dealing with faults to a new breed of operating systems.

Building FOX

Getting there won’t be easy. The FOX project to date is an amalgam of
software systems, all of which together create a prototype fault-oblivious
computing environment.

To tie the pieces together and test its approach, the research team is using
an allotment of 10 million processor hours on Intrepid, awarded through DOE’s
Innovative and Novel Computational Impact on Theory and Experiment (INCITE)
program.

One of FOX’s key elements is TASCEL, a middleware task management library
developed by a team, led by scientist Sriram Krishnamoorthy, at DOE’s Pacific
Northwest National Laboratory. With TASCEL, all of a computation’s task
queues are stored in a shared memory location. Thus, if a computing node
fails, its current task queue can be recovered and reassigned to another
node.

“We also have middleware and libraries that keep a consistent data store,”
Gokhale says. “We don’t get rid of the task until the work of the task has
been completed and stored in the data store. In case of node failure, the
replicated task queues and data storage are automatically rebalanced, and the
remaining nodes continue oblivious to the failure.”

The FOX project also employs systems software capable of core and node
specialization – of simultaneously running various programs and operating
systems on different types of cores and nodes, all in the same exascale
machine.

NIX, a prototype operating system Minnich co-developed, efficiently divides
cores on multicore processors according to function. Kittyhawk, a cloud
environment developed by scientists at Boston University, lets users run
different operating systems on different nodes. This has enabled the FOX team
to port code to the Blue Gene architecture and run a variety of codes to
solve problems.

In the FOX project’s final year, Gokhale says, they’ll speed test their
fault-oblivious environment with a massively parallel quantum chemistry
program originated at Sandia.

“Quantum chemistry computation is enormously challenging because it’s highly
irregular, with some parts requiring an enormous amount of communication,”
she says. “This will really put FOX to the test.”

Minnich suggests that the challenge fault-oblivious computing faces is more
human than technical. From a programming perspective, it’s as big of a shift
as the historic move from vector to massively parallel computing.

Regardless of whether the future of exascale HPC is fault-tolerant, he says,
the FOX project’s approach is part of what’s needed to get there. 


More information about the Beowulf mailing list