[Beowulf] Supercomputers face growing resilience problems

Justin YUAN SHI shi at temple.edu
Thu Nov 22 20:19:51 PST 2012

The fundamental problem rests in our programming API. If you look at
MPI and OpenMP carefully, you will find that these and all others have
one common assumption: the application-level communication is always

We knew full well that this cannot be true.

Thus, the bigger the application we build, the higher probability of
failure. This should not be a surprise.

Proposed fault tolerance methods, such as redundant execution, is
really like "borrow John to pay Paul" where both John and Paul are
personal friends.

What we need is a true sustainable solution that can gain performance
and reliability at the same time as we up scale the application.

This is NOT an impossible dream. The packet-switching network is a
living example of such an architecture. The missing piece in HPC
applications is the principle of statistic multiplexed computing. In
other words, the application architecture should be considered as a
whole in the design space, not a "glued" together piece using lower
layers with unsealed semantic "holes". The semantic "holes" between
the layers are the real evils for all our troubles.

Our research exhibit (booth 3360) demonstrate a prototype data
parallel system using this idea. The Sustainable HPC Cloud Workshop at
the end of SC12 (Friday AM)) had one paper touching on this topic as


On Thu, Nov 22, 2012 at 5:03 AM, Eugen Leitl <eugen at leitl.org> wrote:
> http://www.computerworld.com.au/article/442703/supercomputers_face_growing_resilience_problems/
> Supercomputers face growing resilience problems
> As the number of components in large supercomputers grows, so does the
> possibility of component failure
> Joab Jackson (IDG News Service)
> 21 November, 2012 21:58
> As supercomputers grow more powerful, they'll also grow more vulnerable to
> failure, thanks to the increased amount of built-in componentry. A few
> researchers at the recent SC12 conference, held last week in Salt Lake City,
> offered possible solutions to this growing problem.
> Today's high-performance computing (HPC) systems can have 100,000 nodes or
> more -- with each node built from multiple components of memory, processors,
> buses and other circuitry. Statistically speaking, all these components will
> fail at some point, and they halt operations when they do so, said David
> Fiala, a Ph.D student at the North Carolina State University, during a talk
> at SC12.
> The problem is not a new one, of course. When Lawrence Livermore National
> Laboratory's 600-node ASCI (Accelerated Strategic Computing Initiative) White
> supercomputer went online in 2001, it had a mean time between failures (MTBF)
> of only five hours, thanks in part to component failures. Later tuning
> efforts had improved ASCI White's MTBF to 55 hours, Fiala said.
> But as the number of supercomputer nodes grows, so will the problem.
> "Something has to be done about this. It will get worse as we move to
> exascale," Fiala said, referring to how supercomputers of the next decade are
> expected to have 10 times the computational power that today's models do.
> Today's techniques for dealing with system failure may not scale very well,
> Fiala said. He cited checkpointing, in which a running program is temporarily
> halted and its state is saved to disk. Should the program then crash, the
> system is able to restart the job from the last checkpoint.
> The problem with checkpointing, according to Fiala, is that as the number of
> nodes grows, the amount of system overhead needed to do checkpointing grows
> as well -- and grows at an exponential rate. On a 100,000-node supercomputer,
> for example, only about 35 percent of the activity will be involved in
> conducting work. The rest will be taken up by checkpointing and -- should a
> system fail -- recovery operations, Fiala estimated.
> Because of all the additional hardware needed for exascale systems, which
> could be built from a million or more components, system reliability will
> have to be improved by 100 times in order to keep to the same MTBF that
> today's supercomputers enjoy, Fiala said.
> Fiala presented technology that he and fellow researchers developed that may
> help improve reliability. The technology addresses the problem of silent data
> corruption, when systems make undetected errors writing data to disk.
> Basically, the researchers' approach consists of running multiple copies, or
> "clones" of a program, simultaneously and then comparing the answers. The
> software, called RedMPI, is run in conjunction with the Message Passing
> Interface (MPI), a library for splitting running applications across multiple
> servers so the different parts of the program can be executed in parallel.
> RedMPI intercepts and copies every MPI message that an application sends, and
> sends copies of the message to the clone (or clones) of the program. If
> different clones calculate different answers, then the numbers can be
> recalculated on the fly, which will save time and resources from running the
> entire program again.
> "Implementing redundancy is not expensive. It may be high in the number of
> core counts that are needed, but it avoids the need for rewrites with
> checkpoint restarts," Fiala said. "The alternative is, of course, to simply
> rerun jobs until you think you have the right answer."
> Fiala recommended running two backup copies of each program, for triple
> redundancy. Though running multiple copies of a program would initially take
> up more resources, over time it may actually be more efficient, due to the
> fact that programs would not need to be rerun to check answers. Also,
> checkpointing may not be needed when multiple copies are run, which would
> also save on system resources.
> "I think the idea of doing redundancy is actually a great idea. [For] very
> large computations, involving hundreds of thousands of nodes, there certainly
> is a chance that errors will creep in," said Ethan Miller, a computer science
> professor at the University of California Santa Cruz, who attended the
> presentation. But he said the approach may be not be suitable given the
> amount of network traffic that such redundancy might create. He suggested
> running all the applications on the same set of nodes, which could minimize
> internode traffic.
> In another presentation, Ana Gainaru, a Ph.D student from the University of
> Illinois at Urbana-Champaign, presented a technique of analyzing log files to
> predict when system failures would occur.
> The work combines signal analysis with data mining. Signal analysis is used
> to characterize normal behavior, so when a failure occurs, it can be easily
> spotted. Data mining looks for correlations between separate reported
> failures. Other researchers have shown that multiple failures are sometimes
> correlated with each other, because a failure with one technology may affect
> performance in others, according to Gainaru. For instance, when a network
> card fails, it will soon hobble other system processes that rely on network
> communication.
> The researchers found that 70 percent of correlated failures provide a window
> of opportunity of more than 10 seconds. In other words, when the first sign
> of a failure has been detected, the system may have up to 10 seconds to save
> its work, or move the work to another node, before a more critical failure
> occurs. "Failure prediction can be merged with other fault-tolerance
> techniques," Gainaru said.
> Joab Jackson covers enterprise software and general technology breaking news
> for The IDG News Service. Follow Joab on Twitter at @Joab_Jackson. Joab's
> e-mail address is Joab_Jackson at idg.com
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