This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance
methods proposed for cloud computing. The survey classifies fault-tolerance methods into …

The next generation of supercomputers will break the exascale barrier. Soon we will have
systems capable of at least one quintillion (billion billion) floating-point operations per …

Today's HPC applications are producing extremely large amounts of data, thus it is
necessary to use an efficient compression before storing them to parallel file systems. In this …

Training Deep Neural Networks (DNNs) is a resource-hungry and time-consuming task.
During training, the model performs computation at the GPU to learn weights, repeatedly …

Checkpoints play an important role in training long running machine learning (ML) models.
Checkpoints take a snapshot of an ML model and store it in a non-volatile memory so that …

Performance and power constraints come together with Complementary Metal Oxide
Semiconductor technology scaling in future Exascale systems. Technology scaling makes …

Traditionally, on-demand, rigid, and malleable applications have been scheduled and
executed on separate systems. The ever-growing workload demands and rapidly …

The traditional single-level checkpointing method suffers from significant overhead on large-
scale platforms. Hence, multilevel checkpointing protocols have been studied extensively in …

For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous
problems because there is no indication that there are errors during the execution. We …

Iterative methods are commonly used approaches to solve large, sparse linear systems,
which are fundamental operations for many modern scientific simulations. When the large …