Energy increasingly constrains modern computer hardware, yet protecting computations and
data against errors costs energy. This holds at all scales, but especially for the largest …

We present a fault model designed to bring out the" worst" in iterative solvers based on
mathematical properties. Our model introduces substantially higher overhead, but smaller …

Resilience is a major challenge for large-scale systems. It is particularly important for
iterative linear solvers, since they take much of the time of many scientific applications. We …

Exascale systems will present considerable fault-tolerance challenges to applications and
system software. These systems are expected to suffer several hard and soft errors per day …

Current iterative methods for solving linear equations assume reliability of data and
arithmetic computations. When the computer system violates this assumption, the algorithm …

The increasing size and complexity of massively parallel systems (eg HPC systems) is
making it increasingly likely that individual circuits will produce erroneous results. For this …

Emerging high-performance computing platforms, with large component counts and lower
power margins, are anticipated to be more susceptible to soft errors in both logic circuits and …

Algorithm Based Fault Tolerance (ABFT) is the collective name of a set of techniques used to
determine the correctness of some mathematical calculations. A less well known alternative …

Scientists from many different fields have been developing Bulk‐Synchronous MPI
applications to simulate and study a wide variety of scientific phenomena. Since failure rates …

As the scale of computing platforms becomes increasingly extreme, the requirements for
application fault tolerance are increasing as well. Techniques to address this problem by …