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M. Hosseiniyar

dblp:65/4708 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1988
—ORCID · none

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

Systems, architecture and hardware · 1Software 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 · 50% High-performance computing · 44% Electronic design automation · 6%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Parallel and multicore computing
dataflow computing		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988
Parallel and multicore computing › multiprocessor system
dataflow multiprocessor		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988
High-performance computing › scientific computing systems
partial differential equation solver		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988
High-performance computing
scientific computing		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988
Electronic design automation › high-level synthesis › scheduling
dataflow graph scheduling		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988
Parallel and multicore computing
parallel programming models		 0.011988
Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment · ISCA 1988

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

tagged token dataflow · 0.0deterministic simulation · 0.0chaotic relaxation · 0.0
YearPublicationVenuePosition
1988 Solving Partial Differential Equations in a Data-Driven Multiprocessor Environment
abstract
The implementation of some partial differential equation (PDE) solvers (such as the Jacobi method) on a tagged token data-flow graph is demonstrated. Asynchronous methods (e.g. chaotic relaxation) are studied and other scheduling approaches (such as the token no-labeling scheme) are introduced to support the implementation of the asynchronous methods in a data-driven environment. High-level data-flow-language program constructs are introduced in order to handle chaotic operations. The performance of the program graphs is demonstrated by a deterministic simulation of a message-passing data-flow multiprocessor. An analysis of the overhead in the data-flow graphs is undertaken to demonstrate the limits of parallel operations in data-flow PDE program graphs.>
Jean-Luc Gaudiot, C. M. Lin, M. Hosseiniyar
 ISCA3