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cs.DC2022

Characterizing the Performance of Node-Aware Strategies for Irregular Point-to-Point Communication on Heterogeneous Architectures

Shelby Lockhart, Amanda Bienz, William D. Gropp +1

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computat…

cs.DC2022

Performance Analysis and Optimal Node-Aware Communication for Enlarged Conjugate Gradient Methods

Shelby Lockhart, Amanda Bienz, William Gropp +1

Krylov methods are a key way of solving large sparse linear systems of equations, but suffer from poor strong scalabilty on distributed memory machines. This is due to high synchro…

cs.DC2020

Modeling Data Movement Performance on Heterogeneous Architectures

Amanda Bienz, Luke N. Olson, William D. Gropp +1

The cost of data movement on parallel systems varies greatly with machine architecture, job partition, and nearby jobs. Performance models that accurately capture the cost of data…

cs.DC2019

Node-Aware Improvements to Allreduce

Amanda Bienz, Luke N. Olson, William D. Gropp

The \texttt{MPI\_Allreduce} collective operation is a core kernel of many parallel codebases, particularly for reductions over a single value per process. The commonly used allredu…

cs.DC2018

Improving Performance Models for Irregular Point-to-Point Communication

Amanda Bienz, William D. Gropp, Luke N. Olson

Parallel applications are often unable to take full advantage of emerging parallel architectures due to scaling limitations, which arise due to inter-process communication. Perform…