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1 Introduction
 1.1 Sample timings

1 Introduction

The LinBox C++ library (http://www.linalg.org) performs exact linear algebra and provides a set of routines for the solution of linear algebra problems such as rank, determinant, and the solution of linear systems. It provides representations for both sparse and dense matrices over integers and finite fields. It has a particular emphasis on black-box matrix methods (which are very efficient over sparse matrices), but increasingly also provides elimination-based routines for dense matrices using the industry-standard BLAS numeric routines.

GAP (http://www.gap-system.org) is a system for computational discrete algebra, with particular emphasis on Computational Group Theory. It provides good implementations of exact linear algebra routines on dense matrices over all common fields and the integers. Typically, GAP's versions are faster than LinBox for small finite fields (i.e. order less than 256), but LinBox is much faster for larger finite fields and the integers.

The linboxing (LinBox-in-GAP) package provides an interface to the LinBox C++ library from GAP. It provides alternative versions of GAP linear algebra routines which are mapped through to the equivalent LinBox library routines at the GAP kernel level. The functions provided by the linboxing package are named the same as the GAP equivalents, but are all contained within the LinBox record, and so are prefixed with `LinBox.'. The functions provided are

over the integers and prime fields.

The linear algebra routines provided by the linboxing package are, in the majority of cases, considerably faster than the native GAP versions, and scale better with matrix size. This speed is at the expense of memory, since the GAP matrices and vector must be copied into a memory format that LinBox can use.

1.1 Sample timings

The tables below give typical timings when performing operations on a 500 x 500 matrix. All of the timings given below were performed on a 3.20GHz Intel Core i7 Linux machine using GAP version 4.4.12 and version 0.5.2 of the linboxing package. The tests consider the various equivalent methods in GAP and linboxing, and the various different data representations used internally by GAP

The most dramatic speedups are seen for matrices of integers, where the linboxing routines are significantly faster. It is also faster for large prime fields, but GAP is better for small finite fields due to its very efficient internal representation of those fields. These observations hold for all Rank, Determinant and SolutionMat calculations, but the Trace method is so simple that it appears to be always faster to use GAP

These timings are offered as guidelines. The speedup should be larger with larger matrices, but a gain can also be seen with much smaller matrices (e.g. the Rank of a 15 x 15 integer matrix). The user is encouraged to perform their own timing experiments to assess the likely gain in their own cases.

Table: 500 x 500 matrices of small integers
Operation Standard GAP method GAP IntMat method linboxing method
RankMat / Length(BaseIntMat) 2822.08s 73.21s 0.10s
DeterminantMat / DeterminantIntMat 184.71s 78.84s 7.12s
SolutionMat / SolutionIntMat 6423.93s 144.92s 2.19s
TraceMat 0.00s - 0.52s

 


Table: 500 x 500 matrices of large integers
Operation Standard GAP method GAP IntMat method linboxing method
RankMat / Length(BaseIntMat) 6375.83s 149.78s 0.32s
DeterminantMat / DeterminantIntMat 2998.70s 133.67s 103.74s
SolutionMat / SolutionIntMat 15210.10s 840.73s 34.52s
TraceMat 0.00s - 0.73s

 


Table: 500 x 500 matrices over a large prime field (GF(10007))
Operation Standard GAP method linboxing method
RankMat 1.95s 0.14s
DeterminantMat 1.92s 0.14s
SolutionMat 3.64s 0.33s
TraceMat 0.00s 0.35s

 


Table: 500 x 500 matrices over a small prime field (GF(2))
Operation Standard GAP method linboxing method
RankMat 0.00s 0.06s
DeterminantMat 0.00s 0.06s
SolutionMat 0.00s 0.21s
TraceMat 0.00s 0.29s

 


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