Performance model for parallel mathematical libraries based on historical knowledgebase

I. Salawdeh; E. César; A. Morajko; T. Margalef; E. Luque

doi:10.1007/978-3-540-85451-7_13

Performance model for parallel mathematical libraries based on historical knowledgebase

I. Salawdeh^*, E. César, A. Morajko, T. Margalef, E. Luque

^*Corresponding author for this work

Department of Computer Architecture and Operating Systems

Research output: Contribution to journal › Article › Research › peer-review

1 Citation (Scopus)

Abstract

Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, it is still difficult to select the proper parameters and algorithms to maximize the application performance. This work proposes a performance model for dynamically adjusting applications written with the PETSc library. This model is based on historical performance information and data mining techniques. Finally, we demonstrate the validity of the proposed model through real experimentations.

Original language	American English
Pages (from-to)	110-119
Number of pages	10
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOIs	https://doi.org/10.1007/978-3-540-85451-7_13
Publication status	Published - 2008

Keywords

Dynamic mathematical model
Mathematical Performance
Performance Model
PETSc Performance

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10.1007/978-3-540-85451-7_13

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abstract = "Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, it is still difficult to select the proper parameters and algorithms to maximize the application performance. This work proposes a performance model for dynamically adjusting applications written with the PETSc library. This model is based on historical performance information and data mining techniques. Finally, we demonstrate the validity of the proposed model through real experimentations.",

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author = "I. Salawdeh and E. C{\'e}sar and A. Morajko and T. Margalef and E. Luque",

year = "2008",

doi = "10.1007/978-3-540-85451-7_13",

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Performance model for parallel mathematical libraries based on historical knowledgebase. / Salawdeh, I.; César, E.; Morajko, A.; Margalef, T.; Luque, E.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, p. 110-119.

Research output: Contribution to journal › Article › Research › peer-review

TY - JOUR

T1 - Performance model for parallel mathematical libraries based on historical knowledgebase

AU - Salawdeh, I.

AU - César, E.

AU - Morajko, A.

AU - Margalef, T.

AU - Luque, E.

PY - 2008

Y1 - 2008

N2 - Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, it is still difficult to select the proper parameters and algorithms to maximize the application performance. This work proposes a performance model for dynamically adjusting applications written with the PETSc library. This model is based on historical performance information and data mining techniques. Finally, we demonstrate the validity of the proposed model through real experimentations.

AB - Scientific and mathematical parallel libraries offer a high level of abstraction to programmers. However, it is still difficult to select the proper parameters and algorithms to maximize the application performance. This work proposes a performance model for dynamically adjusting applications written with the PETSc library. This model is based on historical performance information and data mining techniques. Finally, we demonstrate the validity of the proposed model through real experimentations.

KW - Dynamic mathematical model

KW - Mathematical Performance

KW - Performance Model

KW - PETSc Performance

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DO - 10.1007/978-3-540-85451-7_13

M3 - Artículo

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SP - 110

EP - 119

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

Performance model for parallel mathematical libraries based on historical knowledgebase

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Keywords

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