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Annemarie Southwell

dblp:304/6358 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 77% High-performance computing · 23%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Compilers and program optimization › parallelizing compiler
OpenMP compilation
0.512021
Non-recurring engineering (NRE) best practices: a case study with the NERSC/NVIDIA OpenMP contract · SC 2021
GPUs and heterogeneous computing
GPU computing
0.512021
Non-recurring engineering (NRE) best practices: a case study with the NERSC/NVIDIA OpenMP contract · SC 2021
YearPublicationVenuePosition
2021 Non-recurring engineering (NRE) best practices: a case study with the NERSC/NVIDIA OpenMP contract
abstract
The NERSC supercomputer, Perlmutter, consists of AMD CPUs and NVIDIA GPUs. NERSC users expect to be able to use OpenMP to take advantage of the highly capable GPUs. This paper describes how NERSC/NVIDIA constructed a Non-Recurring Engineering (NRE) contract to add OpenMP GPU-offload support to the NVIDIA HPC compilers. The paper describes how the contract incorporated the strengths of both parties and encouraged collaboration to improve the quality of the final deliverable. We include our best practices and how this particular contract took into account emerging OpenMP specifications, NERSC workload requirements, and how to use OpenMP most efficiently on GPU hardware. This paper includes OpenMP application performance results obtained with the NVIDIA compilers distributed in the NVIDIA HPC SDK.
Christopher S. Daley, Annemarie Southwell, Rahulkumar Gayatri, Scott Biersdorfff, Craig Toepfer, Güray Özen, Nicholas J. Wright
SC2