Sparse Matrices and Vectors
Enhancements have been made in Maple 17 for the performance of stacking and augmenting sparse floatingpoint, hardware datatype Matrices and Vectors. Computing linear combinations of pairs of sparse floatingpoint, hardware datatype Matrices using the LinearAlgebra:MatrixAdd command has also been improved.
In the examples below, the Matrix and Vector constructors are used for stacking or augmentation (concatenation).
Benchmarks computed in 64bit Maple 17 for Linux on an Intel Core i5 CPU 760 @ 2.80GHz.

Sparse Vector concatenation


In Maple 16, the concatenation of Vector V with itself three times would take approximately 28 seconds and would result in an output Vector with full rectangular storage. The allocated memory would increase by 1.27GiB.
In Maple 17, it takes less than 0.2 seconds and produces a Vector with sparse storage. Memory allocation increases by 30.5MiB.
Example

>


>


>


 (1.1) 
>


 (1.2) 
>


memory used=76.37MiB, alloc change=30.54MiB, cpu time=100.00ms, real time=121.00ms
 
 (1.3) 
>


 (1.4) 





Sparse Matrix concatenation


In Maple 16, the concatenation of Matrix M with itself as performed below would take approximately 20 seconds and would result in an output Matrix with full rectangular storage.
In Maple 17, it takes less than 0.1 seconds and produces a Matrix with sparse storage. Memory allocation increases by 48MiB.
Example

 (2.1) 
>


 (2.2) 
>


memory used=0.76MiB, alloc change=48.00MiB, cpu time=30.00ms, real time=55.00ms
 
 (2.3) 
>


 (2.4) 





Simple linear combinations of sparse Matrices


In Maple 16, the following command would take 3 minutes and allocated memory would increase by 400MiB.
In Maple 17, it takes less than 1 second and allocated memory increases by 76MiB.
Example

memory used=76.67MiB, alloc change=76.35MiB, cpu time=360.00ms, real time=417.00ms
 
 (3.1) 





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