Resilience 2015 - Workshop on Resiliency in High Performance Computing (Resilience) in Clusters, Clouds, and Grids

Clusters, Clouds, and Grids are three different computational paradigms
with the intent or potential to support High Performance Computing
(HPC). Currently, they consist of hardware, management, and usage models
particular to different computational regimes, e.g., high performance
cluster systems designed to support tightly coupled scientific
simulation codes typically utilize high-speed interconnects and
commercial cloud systems designed to support software as a service (SAS)
do not. However, in order to support HPC, all must at least utilize
large numbers of resources and hence effective HPC in any of these
paradigms must address the issue of resiliency at large-scale.
Recent trends in HPC systems have clearly indicated that future
increases in performance, in excess of those resulting from improvements
in single- processor performance, will be achieved through corresponding
increases in system scale, i.e., using a significantly larger component
count. As the raw computational performance of these HPC systems
increases from today's tera- and peta-scale to next-generation multi
peta-scale capability and beyond, their number of computational,
networking, and storage components will grow from the ten-to-one-hundred
thousand compute nodes of today's systems to several hundreds of
thousands of compute nodes and more in the foreseeable future. This
substantial growth in system scale, and the resulting component count,
poses a challenge for HPC system and application software with respect
to fault tolerance and resilience.
Furthermore, recent experiences on extreme-scale HPC systems with
non-recoverable soft errors, i.e., bit flips in memory, cache,
registers, and logic added another major source of concern. The
probability of such errors not only grows with system size, but also
with increasing architectural vulnerability caused by employing
accelerators, such as FPGAs and GPUs, and by shrinking nanometer
technology. Reactive fault tolerance technologies, such as
checkpoint/restart, are unable to handle high failure rates due to
associated overheads, while proactive resiliency technologies, such as
migration, simply fail as random soft errors can't be predicted.
Moreover, soft errors may even remain undetected resulting in silent
data corruption.