Scalable Parallel Generation of Very Large Sparse Benchmark Matrices

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F14%3A00217676" target="_blank" >RIV/68407700:21240/14:00217676 - isvavai.cz</a>
Result on the web
<a href="http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-642-55224-3_18" target="_blank" >http://80.link.springer.com.dialog.cvut.cz/chapter/10.1007/978-3-642-55224-3_18</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-55224-3_18" target="_blank" >10.1007/978-3-642-55224-3_18</a>

Result language
angličtina
Original language name
Scalable Parallel Generation of Very Large Sparse Benchmark Matrices
Original language description
We present a method and an accompanying algorithm for scalable parallel generation of sparse matrices intended primarily for benchmarking purposes, namely for evaluation of performance and scalability of generic massively parallel algorithms that involvesparse matrices. The proposed method is based on enlargement of small input matrices, which are supposed to be obtained from public sparse matrix collections containing numerous matrices arising in different application domains and thus having differentstructural and numerical properties. The resulting matrices are distributed among processors of a parallel computer system. The enlargement process is designed so its users may easily control structural and numerical properties of resulting matrices aswell as the distribution of their nonzero elements to particular processors.
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F12%2F2011" target="_blank" >GAP202/12/2011: Parallel Input/Output Algorithms for Very Large Sparse Matrices</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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