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 …

The exponential growth of digital information is imposing increasing scale and efficiency
demands on modern storage infrastructures. As infrastructure complexity increases, so does …

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

Python has become the prime language for application development in the data science and
machine learning domains. However, data scientists are not necessarily experienced …

Parallel file systems are at the core of HPC I/O infrastructures. Those systems minimize the
I/O time of applications by separating files into fixed-size chunks and distributing them …

Fault-tolerant systems rely on recovery techniques to enhance system resilience. In this
regard, checkpointing procedures periodically take snapshots of the system state during …

In high-performance computing (HPC), data and metadata are stored on special server
nodes and client applications access the servers' data and metadata through a network …

The complex software and hardware I/O stack of HPC platforms makes it challenging for end-
users to obtain superior I/O performance and to understand the root causes of I/O …

With advances in the scope of computational modeling methodologies, an increased focus
is being placed on the application of data-driven techniques to increasingly complex …

An in-depth overview of an emerging field that brings together high-performance computing,
big data processing, and deep lLearning. Over the last decade, the exponential explosion of …