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 …

Large-scale applications typically spend a large fraction of their execution time performing
I/O to a parallel storage system. However, with rapid progress in compute and storage …

This paper offers a solution to overcome the complexities of production system I/O
performance monitoring. We present Beacon, an end-to-end I/O resource monitoring and …

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 …

Most widely-deployed parallel file systems (PFSs) implement POSIX semantics, which
implies sequential consistency for reads and writes. Strict adherence to POSIX semantics is …

Large-scale high-performance computing (HPC) applications running on supercomputers
produce large amounts of data routinely and store it in files on multi-PB shared parallel …

Many HPC applications perform their I/O in bursts that follow a periodic pattern. This allows
for making predictions as to when a burst occurs. System providers can take advantage of …

Scientific computing workloads at HPC facilities have been shifting from traditional
numerical simulations to AI/ML applications for training and inference while processing and …

Large-scale high performance computing (HPC) systems typically consist of many
thousands of CPUs and storage units, while used by hundreds to thousands of users at the …