This paper aims to create a transition path from file-based IO to streaming-based workflows
for scientific applications in an HPC environment. By using the openPMP-api, traditional …

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning
(DL) models in material science to predict material properties from the graph representation …

Computer modeling is essential to research on Advanced Accelerator Concepts (AAC), as
well as to their design and operation. This paper summarizes the current status and future …

Efficient data compression is becoming increasingly critical for storing scientific data
because many scientific applications produce vast amounts of data. This paper presents an …

After a summary of relevant comments and recommendations from various reports over the
last ten years, this paper examines the modeling needs in accelerator physics, from the …

With the adoption of new storage technologies like NVMs, tiered storage has gained
popularity in large-scale, hyper-converged clusters. The storage back-end of hyper …

Acquiring data from scientific simulations for analytical purposes is inherently challenging
due to the complex and irregularly shaped regions within which the data resides, particularly …

Accurately modeling real-world systems requires scientific applications at exascale to
generate massive amounts of data and manage data storage efficiently. However, parallel …

Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per
run, create massive IO and storage challenges for analysis. Deep learning-based …

I/O bandwidth is a critical resource in an HPC cluster. As with all shared resources, its
availability is impacted significantly by the users and the applications they execute. Without …