The Role of Benchmarking in Symbolic Computation: (Position Paper)

James Davenport

doi:10.1109/SYNASC.2018.00050

The Role of Benchmarking in Symbolic Computation: (Position Paper)

James Davenport

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a published conference proceeding

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Abstract

There is little doubt that, in the minds of most symbolic computation researchers, the ideal paper consists of a problem statement, a new algorithm, a complexity analysis and preferably a few validating examples. There are many such great papers. This paradigm has served computer algebra well for many years, and indeed continues to do so where it is applicable. However, it is much less applicable to sparse problems, where there are many NP-hardness results, or to many problems coming from algebraic geometry, where the worst-case complexity seems to be rare.

We argue that, in these cases, the field should take a leaf out of the practices of the SAT-solving community, and adopt systematic benchmarking, and benchmarking contests, as a way measuring (and stimulating) progress. This would involve a change of culture.

Original language	English
Title of host publication	Proceedings of the 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2018
Publisher	IEEE
ISBN (Electronic)	9781728106250
ISBN (Print)	9781728106267
DOIs	https://doi.org/10.1109/SYNASC.2018.00050
Publication status	Published - 2018

Keywords

Computer algebra
Benchmarking
Performance

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10.1109/SYNASC.2018.00050

SYNASC2018-JHDpositionV7Accepted author manuscript, 266 KBLicence: CC BY-NC-ND

1 Finished

SC-square - Satisfiability Checking and Symbolic Computation: uniting two communities to solve real problems
Davenport, J.
EU - Horizon 2020
1/07/16 → 31/08/18
Project: EU Commission

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title = "The Role of Benchmarking in Symbolic Computation: (Position Paper)",

abstract = "There is little doubt that, in the minds of most symbolic computation researchers, the ideal paper consists of a problem statement, a new algorithm, a complexity analysis and preferably a few validating examples. There are many such great papers. This paradigm has served computer algebra well for many years, and indeed continues to do so where it is applicable. However, it is much less applicable to sparse problems, where there are many NP-hardness results, or to many problems coming from algebraic geometry, where the worst-case complexity seems to be rare.We argue that, in these cases, the field should take a leaf out of the practices of the SAT-solving community, and adopt systematic benchmarking, and benchmarking contests, as a way measuring (and stimulating) progress. This would involve a change of culture.",

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N2 - There is little doubt that, in the minds of most symbolic computation researchers, the ideal paper consists of a problem statement, a new algorithm, a complexity analysis and preferably a few validating examples. There are many such great papers. This paradigm has served computer algebra well for many years, and indeed continues to do so where it is applicable. However, it is much less applicable to sparse problems, where there are many NP-hardness results, or to many problems coming from algebraic geometry, where the worst-case complexity seems to be rare.We argue that, in these cases, the field should take a leaf out of the practices of the SAT-solving community, and adopt systematic benchmarking, and benchmarking contests, as a way measuring (and stimulating) progress. This would involve a change of culture.

AB - There is little doubt that, in the minds of most symbolic computation researchers, the ideal paper consists of a problem statement, a new algorithm, a complexity analysis and preferably a few validating examples. There are many such great papers. This paradigm has served computer algebra well for many years, and indeed continues to do so where it is applicable. However, it is much less applicable to sparse problems, where there are many NP-hardness results, or to many problems coming from algebraic geometry, where the worst-case complexity seems to be rare.We argue that, in these cases, the field should take a leaf out of the practices of the SAT-solving community, and adopt systematic benchmarking, and benchmarking contests, as a way measuring (and stimulating) progress. This would involve a change of culture.

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KW - Benchmarking

KW - Performance

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M3 - Chapter in a published conference proceeding

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BT - Proceedings of the 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2018

PB - IEEE

ER -

The Role of Benchmarking in Symbolic Computation: (Position Paper)

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Keywords

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SC-square - Satisfiability Checking and Symbolic Computation: uniting two communities to solve real problems

Cite this