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Lu Xi-Cheng

dblp:91/2779 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1986
—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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 77% Performance modeling and evaluation · 23%

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

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
task allocation
0.011986
Optimal Partitioning of Randomly Generated Distributed Programs · IEEE Trans. Software Eng. 1986
Performance modeling and evaluation
distributed system performance
0.011986
Optimal Partitioning of Randomly Generated Distributed Programs · IEEE Trans. Software Eng. 1986

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

random graph model · 0.0optimization analysis · 0.0
YearPublicationVenuePosition
1986 Optimal Partitioning of Randomly Generated Distributed Programs
abstract
An investigation is made of an optimal task-assignment policy for a random-graph model of a distributed program. The model of the distributed computer system assumes that communications overhead adds to total run time and that total run time decreases as the number of processors running the program is increased. When the processors are homogeneous, the optimal task-assignments are external in the sense that tasks are totally distributed among all processors as evenly as possible or not distributed at all. The point at which the policy shows a sharp change of behavior depends upon the ratio of run times to communication times. The authors derive two important properties of the optimal task-assignments for heterogeneous processors. The first property is that an optimal policy distributes the cost of processing among the processors as evenly as possible so that a processor with higher speed gets more tasks and vice versa. The second property determines the number of processors among which to distribute the tasks evenly. In the special case when there is a uniform degradation of processing speed, it is shown that the optimal policy again exhibits an extremal characteristic.
Bipin Indurkhya, Harold S. Stone, Lu Xi-Cheng
IEEE Trans. Software Eng.3