The high-performance computing I/O stack has been complex due to multiple software
layers, the inter-dependencies among these layers, and the different performance tuning …

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

Modern HPC workflows involve intricate coupling of simulation, data analytics, and artificial
intelligence (AI) applications to improve time to scientific insight. These workflows require a …

Modern HPC systems are collecting large amounts of I/O performance data. The massive
volume and heterogeneity of this data, however, have made timely performance of in-depth …

Machine Learning applications on HPC systems have been gaining popularity in recent
years. The upcoming large scale systems will offer tremendous parallelism for training …

On the way to the exascale era, millions of parallel processing elements are required.
Accordingly, one major chal-lenge is the ever-widening gap between computational power …

Scientific discovery increasingly depends on complex workflows consisting of multiple
phases and sometimes millions of parallelizable tasks or pipelines. These workflows access …

Large-scale parallel applications are highly data-intensive and perform terabytes of I/O
routinely. Unfortunately, on a large-scale system where multiple applications run …

Using parallel file systems efficiently is a tricky problem due to inter-dependencies among
multiple layers of I/O software, including high-level I/O libraries (HDF5, netCDF, etc.), MPI-IO …

Computational provenance has many important applications, especially to reproducibility.
System-level provenance collectors can track provenance data without requiring the user to …