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C. Alexander Nelson

dblp:00/5616 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

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

Systems, architecture and hardware · 1

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
Parallel and multicore computing · 67% Memory systems · 33%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › non-uniform memory access
NUMA data placement
0.012000
Extending OpenMP for NUMA Machines · SC 2000
Parallel and multicore computing › parallel programming models › directive-based programming
OpenMP extensions
0.012000
Extending OpenMP for NUMA Machines · SC 2000
Parallel and multicore computing
parallel programming models
0.012000
Extending OpenMP for NUMA Machines · SC 2000
Compilers and program optimization
code generation
0.012000
Extending OpenMP for NUMA Machines · SC 2000

Methods — techniques the papers use, named apart from their topics

high performance fortran directives · 0.1
YearPublicationVenuePosition
2000 Extending OpenMP for NUMA Machines
abstract
This paper describes extensions to OpenMP that implemen data placemen features needed for NUMA architectures. OpenMP is a collection of compiler directives and library routines used to write portable parallel programs for shared-memory architectures. Writing efficient parallel programs for NUMA architectures, which have characteristics of both shared-memory and distributed-memory architectures, requires that a programmer control the placement of data in memory and the placement of computations that operate on that data. Optimal performance is obtained when computations occur on processors that have fast access to the data needed by those computations. OpenMP-designed for shared-memory architectures-does not by itself address these issues. The extensions to OpenMP Fortran presented here have been mainly taken from High Performance Fortran. The paper describes some of the techniques that the Compaq Fortran compiler uses to generate efficient code based on these extensions. I also describes some additional compiler optimizations, and concludes with some preliminary results.
John Bircsak, Peter Craig, RaeLyn Crowell, Zarka Cvetanovic, Jonathan Harris, C. Alexander Nelson, Carl D. Offner
SC6