Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields
such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges …

As the US Department of Energy (DOE) computing facilities began deploying petascale
systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA …

We improved the quality and reduced the time to produce machine learned models for use
in small molecule antiviral design. Our globally asynchronous multi-level parallel training …

In this paper we present PolarFly, a diameter-2 network topology based on the Erdos-Renyi
family of polarity graphs from finite geometry. This is the first known diameter-2 topology that …

A number of features of today's high-performance computers make it challenging to exploit
these machines fully for computational science. These include increasing core counts but …

As HPC systems advance to exascale, communication networks are becoming ever more
complex including, eg, support for in-network processing. While critical in facilitating …

This work explores the performance of single‐and multi‐GPU computing on state‐of‐the‐art
NVIDIA‐and AMD‐based server‐class hardware using various programming interfaces to …

Distributed-memory graph algorithms are fundamental enablers in scientific computing and
analytics workflows. A majority of graph algorithms rely on the graph neighborhood …

We present optimizations to improve communication performance by reducing on-node data
movement for a class of distributed memory applications. The primary concept is to eliminate …

On modern heterogeneous HPC systems, the most popular way to realize distributed
computation is the hybrid programming model of MPI+ X (X being OpenMP/CUDA/etc.), as it …