Laurence S. Kaplan

dblp:16/6459 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none

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

Software engineering, systems software and programming languages · 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.

Software engineering, system software, and programming languages
1 paper
Operating systems · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 100%

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

TopicWeightPapersLastEvidence papers
Operating systems › resource management
memory management
0.011991
The Robustness of NUMA Memory Management · SOSP 1991
Operating systems › resource management › memory management
NUMA memory management
0.011991
The Robustness of NUMA Memory Management · SOSP 1991
Memory systems
non-uniform memory access
0.011991
The Robustness of NUMA Memory Management · SOSP 1991

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

policy tuning · 0.0
YearPublicationVenuePosition
1991 The Robustness of NUMA Memory Management
abstract
The study of operating systems level memory management policies for nonuniform memory access time (NUMA) shared memory multiprocessors is an area of active research. Previous results have suggested that the best policy choice often depends on the application under consideration, while others have reported that the best policy depends on the particular architecture. Since both observations have merit, we explore the concept of policy tuning on an application/architecture basis.We introduce a highly tunable dynamic page placement policy for NUMA multiprocessors, and address issues related to the tuning of that policy to different architectures and applications. Experimental data acquired from our DUnX operating system running on two different NUMA multiprocessors are used to evaluate the usefulness, importance, and ease of policy tuning.Our results indicate that while varying some of the parameters can have dramatic effects on performance, it is easy to select a set of default parameter settings that result in good performance for each of our test applications on both architectures. This apparent robustness of our parameterized policy raises the possibility of machine-independent memory management for NUMA-class machines.
Richard P. LaRowe Jr., Carla Schlatter Ellis, Laurence S. Kaplan
SOSP3