Emerging workloads such as artificial intelligence, big data analytics and complex multi-step
workflows alongside future exascale applications are anticipated future HPC workloads …

Different techniques for estimating the execution time of parallel applications have been
studied for the last 25 years. These approaches have proposed different methods for …

Power capping is an important solution to keep the system within a fixed power constraint.
However, for the over-provisioned and power-constrained systems, especially the future …

In an in-situ workflow, multiple components such as simulation and analysis applications are
coupled with streaming data transfers. The multiplicity of possible configurations …

Predicting the performance of highly configurable software systems is the foundation for
performance testing and quality assurance. To that end, recent work has been relying on …

Deep Learning (DL) is moving towards deploying workloads not only in cloud datacenters,
but also to the local devices. Although these are mostly limited to inference tasks, it still …

Workflow tasks are time-sensitive and their task completion utility, ie, value of task
completion, is inversely proportional to their completion time. Existing solutions to the NP …

Deep learning has been one of the trendiest research topics. However, as data quantities
rise exponentially, training large neural networks can become prohibitively expensive with …

The many configuration options of modern applications make it difficult for users to select a
performance-optimal configuration. Performance models help users in understanding …

Modeling and predicting the performance of HPC applications is exceedingly important for
several purposes. These incorporate scheduling and managing tasks, understanding …