The considerable interest in the high performance computing (HPC) community regarding
analyzing and visualization data without first writing to disk, ie, in situ processing, is due to …

Data staging and in-situ/in-transit data processing are emerging as attractive approaches for
supporting extreme scale scientific workflows. These approaches improve end-to-end …

In this position paper, we argue that the loosely coupled in situ processing paradigm will
play an important role in high performance computing for the foreseeable future. Loosely …

The dramatic increase in the scale of current and planned high-end HPC systems is leading
new challenges, such as the growing costs of data movement and IO, and the reduced mean …

MPI intergroup collective communication defines message transfer patterns between two
disjoint groups of MPI processes. Such patterns occur in coupled applications, and in …

This paper presents a methodology and algorithms for automatic communication refinement.
The communication refinement task in system-level synthesis transforms abstract data …

The dramatic increase in the scale of current and planned high-end HPC systems is leading
new challenges, such as the growing costs of data movement and IO, and the reduced mean …

As applications move towards extreme scales, data-related challenges are becoming
significant concerns, and in-situ workflows based on data staging and in-situ/in-transit data …

As scientific workflows increasingly use extreme-scale resources, the imbalance between
higher computational capabilities, generated data volumes, and available I/O bandwidth is …

Extreme scale scientific workflows are composed of multiple applications that exchange data
at runtime. Several data-related challenges are limiting the potential impact of such …