We present a comprehensive survey on parallel I/O in the high-performance computing
(HPC) context. This is an important field for HPC because of the historic gap between …

State-of-the-art, yet decades-old, architecture of high-performance computing systems has
its compute and storage resources separated. It thus is limited for modern data-intensive …

The quality of machine learning (ML) and deep learning (DL) models are very sensitive to
many different adjustable parameters that are set before training even begins, commonly …

The state-of-the-art storage architecture of high-performance computing systems was
designed decades ago, and with today's scale and level of concurrency, it is showing …

Traditional retrieval methods have been essential for assessing document similarity but
struggle with capturing semantic nuances. Despite advancements in latent semantic …

Direct I/O allows I/O requests to bypass the Linux page cache and was introduced over 20
years ago as an alternative to the default buffered I/O mode. However, high-performance …

As systems scale toward exactable, many resources will become increasingly constrained.
While some of these resources have historically been explicitly allocated, many--such as …

Big Data is playing important role in scientific, industrial and academic areas. Information is
being generated everyday by millions of computing machines and collected for future use …

Although cloud computing has become one of the most popular paradigms for executing
data-intensive applications (for example, Hadoop), the storage subsystem is not optimized …

Data compression could ameliorate the I/O pressure of scientific applications on high-
performance computing systems. Unfortunately, the conventional wisdom of naively …