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| The '''SPIKE algorithm''' is a hybrid [[Parallel computing|parallel]] solver for [[Band matrix|banded]] [[System of linear equations|linear systems]] developed by Eric Polizzi and Ahmed Sameh.
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| | |
| ==Overview==
| |
| The SPIKE algorithm deals with a linear system {{math|'''<var>AX</var>''' {{=}} '''<var>F</var>'''}}, where {{math|'''<var>A</var>'''}} is a banded <math>n\times n</math> matrix of [[Sparse matrix#Bandwidth|bandwidth]] much less than <math>n</math>, and {{math|'''<var>F</var>'''}} is an <math>n\times s</math> matrix containing <math>s</math> right-hand sides. It is divided into a preprocessing stage and a postprocessing stage.
| |
| | |
| ===Preprocessing stage===
| |
| In the preprocessing stage, the linear system {{math|'''<var>AX</var>''' {{=}} '''<var>F</var>'''}} is partitioned into a [[Block matrix#Block tridiagonal matrices|block tridiagonal]] form | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{A}_1 & \boldsymbol{B}_1\\
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| \boldsymbol{C}_2 & \boldsymbol{A}_2 & \boldsymbol{B}_2\\
| |
| & \ddots & \ddots & \ddots\\
| |
| & & \boldsymbol{C}_{p-1} & \boldsymbol{A}_{p-1} & \boldsymbol{B}_{p-1}\\
| |
| & & & \boldsymbol{C}_p & \boldsymbol{A}_p
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_1\\
| |
| \boldsymbol{X}_2\\
| |
| \vdots\\
| |
| \boldsymbol{X}_{p-1}\\
| |
| \boldsymbol{X}_p
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
| |
| \boldsymbol{F}_1\\
| |
| \boldsymbol{F}_2\\
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| \vdots\\
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| \boldsymbol{F}_{p-1}\\
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| \boldsymbol{F}_p
| |
| \end{bmatrix}.
| |
| </math>
| |
| | |
| Assume, for the time being, that the diagonal blocks {{math|'''<var>A</var>'''<sub><var>j</var></sub>}} ({{math|<var>j<var> {{=}} 1,…,<var>p</var>}} with {{math|<var>p</var> ≥ 2}}) are [[Invertible matrix|nonsingular]]. Define a [[Block matrix#Block diagonal matrices|block diagonal]] matrix
| |
| :{{math|'''<var>D</var>''' {{=}} diag('''<var>A</var>'''<sub>1</sub>,…,'''<var>A</var>'''<sub><var>p</var></sub>)}},
| |
| then {{math|'''<var>D</var>'''}} is also nonsingular. Left-multiplying {{math|'''<var>D</var>'''<sup>−1</sup>}} to both sides of the system gives
| |
| ::<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I} & \boldsymbol{V}_1\\
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| \boldsymbol{W}_2 & \boldsymbol{I} & \boldsymbol{V}_2\\
| |
| & \ddots & \ddots & \ddots\\
| |
| & & \boldsymbol{W}_{p-1} & \boldsymbol{I} & \boldsymbol{V}_{p-1}\\
| |
| & & & \boldsymbol{W}_p & \boldsymbol{I}
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_1\\
| |
| \boldsymbol{X}_2\\
| |
| \vdots\\
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| \boldsymbol{X}_{p-1}\\
| |
| \boldsymbol{X}_p
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
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| \boldsymbol{G}_1\\
| |
| \boldsymbol{G}_2\\
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| \vdots\\
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| \boldsymbol{G}_{p-1}\\
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| \boldsymbol{G}_p
| |
| \end{bmatrix},
| |
| </math>
| |
| | |
| which is to be solved in the postprocessing stage. Left-multiplication by {{math|'''<var>D</var>'''<sup>−1</sup>}} is equivalent to solving <math>p</math> systems of the form
| |
| :{{math|'''<var>A</var>'''<sub><var>j</var></sub>['''<var>V</var>'''<sub><var>j</var></sub> '''<var>W</var>'''<sub><var>j</var></sub> '''<var>G</var>'''<sub><var>j</var></sub>] {{=}} ['''<var>B</var>'''<sub><var>j</var></sub> '''<var>C</var>'''<sub><var>j</var></sub> '''<var>F</var>'''<sub><var>j</var></sub>]}}
| |
| (omitting {{math|'''<var>W</var>'''<sub>1</sub>}} and {{math|'''<var>C</var>'''<sub>1</sub>}} for <math>j=1</math>, and {{math|'''<var>V</var>'''<sub><var>p</var></sub>}} and {{math|'''<var>B</var>'''<sub><var>p</var></sub>}} for <math>j=p</math>), which can be carried out in parallel.
| |
| | |
| Due to the banded nature of {{math|'''<var>A</var>'''}}, only a few leftmost columns of each {{math|'''<var>V</var>'''<sub><var>j</var></sub>}} and a few rightmost columns of each {{math|'''<var>W</var>'''<sub><var>j</var></sub>}} can be nonzero. These columns are called the ''spikes''.
| |
| | |
| ===Postprocessing stage===
| |
| [[Without loss of generality]], assume that each spike contains exactly <math>m</math> columns (<math>m</math> is much less than <math>n</math>) (pad the spike with columns of zeroes if necessary). Partition the spikes in all {{math|'''<var>V</var>'''<sub><var>j</var></sub>}} and {{math|'''<var>W</var>'''<sub><var>j</var></sub>}} into
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{V}_j^{(t)}\\
| |
| \boldsymbol{V}_j'\\
| |
| \boldsymbol{V}_j^{(b)}
| |
| \end{bmatrix}
| |
| </math> and <math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{W}_j^{(t)}\\
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| \boldsymbol{W}_j'\\
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| \boldsymbol{W}_j^{(b)}\\
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| \end{bmatrix}
| |
| </math>
| |
| | |
| where {{math|{{SubSup|'''<var>V</var>'''|<var>j</var>|(''t'')}}}}, {{math|{{SubSup|'''<var>V</var>'''|<var>j</var>|(''b'')}}}}, {{math|{{SubSup|'''<var>W</var>'''|<var>j</var>|(''t'')}}}} and {{math|{{SubSup|'''<var>W</var>'''|<var>j</var>|(''b'')}}}} are of dimensions <math>m\times m</math>. Partition similarly all {{math|'''<var>X</var>'''<sub><var>j</var></sub>}} and {{math|'''<var>G</var>'''<sub><var>j</var></sub>}} into
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_j^{(t)}\\
| |
| \boldsymbol{X}_j'\\
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| \boldsymbol{X}_j^{(b)}
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| \end{bmatrix}
| |
| </math> and <math>
| |
| \begin{bmatrix}
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| \boldsymbol{G}_j^{(t)}\\
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| \boldsymbol{G}_j'\\
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| \boldsymbol{G}_j^{(b)}\\
| |
| \end{bmatrix}.
| |
| </math>
| |
| | |
| Notice that the system produced by the preprocessing stage can be reduced to a block [[Pentadiagonal matrix|pentadiagonal]] system of much smaller size (recall that <math>m</math> is much less than <math>n</math>)
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_1^{(t)}\\
| |
| \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)} & \boldsymbol{0}\\
| |
| \boldsymbol{0} & \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_2^{(t)}\\
| |
| & \boldsymbol{W}_2^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_2^{(b)} & \boldsymbol{0} \\
| |
| & & \ddots & \ddots & \ddots & \ddots & \ddots\\
| |
| & & & \boldsymbol{0} & \boldsymbol{W}_{p-1}^{(t)} & \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_{p-1}^{(t)}\\
| |
| & & & & \boldsymbol{W}_{p-1}^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_{p-1}^{(b)} & \boldsymbol{0}\\
| |
| & & & & & \boldsymbol{0} & \boldsymbol{W}_p^{(t)} & \boldsymbol{I}_m & \boldsymbol{0}\\
| |
| & & & & & & \boldsymbol{W}_p^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_1^{(t)}\\
| |
| \boldsymbol{X}_1^{(b)}\\
| |
| \boldsymbol{X}_2^{(t)}\\
| |
| \boldsymbol{X}_2^{(b)}\\
| |
| \vdots\\
| |
| \boldsymbol{X}_{p-1}^{(t)}\\
| |
| \boldsymbol{X}_{p-1}^{(b)}\\
| |
| \boldsymbol{X}_p^{(t)}\\
| |
| \boldsymbol{X}_p^{(b)}
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
| |
| \boldsymbol{G}_1^{(t)}\\
| |
| \boldsymbol{G}_1^{(b)}\\
| |
| \boldsymbol{G}_2^{(t)}\\
| |
| \boldsymbol{G}_2^{(b)}\\
| |
| \vdots\\
| |
| \boldsymbol{G}_{p-1}^{(t)}\\
| |
| \boldsymbol{G}_{p-1}^{(b)}\\
| |
| \boldsymbol{G}_p^{(t)}\\
| |
| \boldsymbol{G}_p^{(b)}
| |
| \end{bmatrix}\text{,}
| |
| </math>
| |
| | |
| which we call the ''reduced system'' and denote by {{math|'''<var>S̃X̃</var>''' {{=}} '''<var>G̃</var>'''}}.
| |
| | |
| Once all {{math|{{SubSup|'''<var>X</var>'''|<var>j</var>|(''t'')}}}} and {{math|{{SubSup|'''<var>X</var>'''|<var>j</var>|(''b'')}}}} are found, all {{math|'''<var>X</var>'''′<sub><var>j</var></sub>}} can be recovered with perfect parallelism via
| |
| | |
| :<math>
| |
| \begin{cases}
| |
| \boldsymbol{X}_1'=\boldsymbol{G}_1'-\boldsymbol{V}_1'\boldsymbol{X}_2^{(t)}\text{,}\\
| |
| \boldsymbol{X}_j'=\boldsymbol{G}_j'-\boldsymbol{V}_j'\boldsymbol{X}_{j+1}^{(t)}-\boldsymbol{W}_j'\boldsymbol{X}_{j-1}^{(b)}\text{,} & j=2,\ldots,p-1\text{,}\\
| |
| \boldsymbol{X}_p'=\boldsymbol{G}_p'-\boldsymbol{W}_p\boldsymbol{X}_{p-1}^{(b)}\text{.}
| |
| \end{cases}
| |
| </math>
| |
| | |
| ==SPIKE as a polyalgorithmic banded linear system solver==
| |
| Despite being logically divided into two stages, computationally, the SPIKE algorithm comprises three stages:
| |
| # [[Matrix decomposition|factorizing]] the diagonal blocks,
| |
| # computing the spikes,
| |
| # solving the reduced system.
| |
| Each of these stages can be accomplished in several ways, allowing a multitude of variants. Two notable variants are the ''recursive SPIKE'' algorithm for non-[[Diagonally dominant matrix|diagonally-dominant]] cases and the ''truncated SPIKE'' algorithm for diagonally-dominant cases. Depending on the variant, a system can be solved either exactly or approximately. In the latter case, SPIKE is used as a preconditioner for iterative schemes like [[Iterative method|Krylov subspace method]]s and [[iterative refinement]].
| |
| | |
| ===Recursive SPIKE===
| |
| ====Preprocessing stage====
| |
| The first step of the preprocessing stage is to factorize the diagonal blocks {{math|'''<var>A</var>'''<sub><var>j</var></sub>}}. For numerical stability, one can use [[LAPACK]]'s <code>XGBTRF</code> routines to [[LU decomposition|LU factorize]] them with partial pivoting. Alternatively, one can also factorize them without partial pivoting but with a "diagonal boosting" strategy. The latter method tackles the issue of singular diagonal blocks.
| |
| | |
| In concrete terms, the diagonal boosting strategy is as follows. Let {{math|0<sub><var>ε</var></sub>}} denote a configurable "machine zero". In each step of LU factorization, we require that the pivot satisfy the condition
| |
| | |
| :{{math||pivot| > 0<sub><var>ε</var></sub>‖'''<var>A</var>'''‖<sub>1</sub>}}.
| |
| | |
| If the pivot does not satisfy the condition, it is then boosted by
| |
| | |
| :<math>
| |
| \mathrm{pivot}=
| |
| \begin{cases}
| |
| \mathrm{pivot}+\epsilon\lVert\boldsymbol{A}_j\rVert_1 & \text{if }\mathrm{pivot}\geq 0\text{,}\\
| |
| \mathrm{pivot}-\epsilon\lVert\boldsymbol{A}_j\rVert_1 & \text{if }\mathrm{pivot}<0
| |
| \end{cases}
| |
| </math>
| |
| | |
| where {{math|<var>ε</var>}} is a positive parameter depending on the machine's [[Machine epsilon|unit roundoff]], and the factorization continues with the boosted pivot. This can be achieved by modified versions of [[ScaLAPACK]]'s <code>XDBTRF</code> routines. After the diagonal blocks are factorized, the spikes are computed and passed on to the postprocessing stage.
| |
| | |
| ====Postprocessing stage====
| |
| =====The two-partition case=====
| |
| In the two-partition case, i.e., when {{math|<var>p</var> {{=}} 2}}, the reduced system {{math|'''<var>S̃X̃</var>''' {{=}} '''<var>G̃</var>'''}} has the form
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_1^{(t)}\\
| |
| \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)} & \boldsymbol{0}\\
| |
| \boldsymbol{0} & \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m & \boldsymbol{0}\\
| |
| & \boldsymbol{W}_2^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_1^{(t)}\\
| |
| \boldsymbol{X}_1^{(b)}\\
| |
| \boldsymbol{X}_2^{(t)}\\
| |
| \boldsymbol{X}_2^{(b)}
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
| |
| \boldsymbol{G}_1^{(t)}\\
| |
| \boldsymbol{G}_1^{(b)}\\
| |
| \boldsymbol{G}_2^{(t)}\\
| |
| \boldsymbol{G}_2^{(b)}
| |
| \end{bmatrix}\text{.}
| |
| </math>
| |
| | |
| An even smaller system can be extracted from the center:
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)}\\
| |
| \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{X}_1^{(b)}\\
| |
| \boldsymbol{X}_2^{(t)}
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
| |
| \boldsymbol{G}_1^{(b)}\\
| |
| \boldsymbol{G}_2^{(t)}
| |
| \end{bmatrix}\text{,}
| |
| </math>
| |
| | |
| which can be solved using the [[Block LU decomposition|block LU factorization]]
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)}\\
| |
| \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| =
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m\\
| |
| \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)}\\
| |
| & \boldsymbol{I}_m-\boldsymbol{W}_2^{(t)}\boldsymbol{V}_1^{(b)}
| |
| \end{bmatrix}\text{.}
| |
| </math> | |
| | |
| Once {{math|{{SubSup|'''<var>X</var>'''|1|(''b'')}}}} and {{math|{{SubSup|'''<var>X</var>'''|2|(''t'')}}}} are found, {{math|{{SubSup|'''<var>X</var>'''|1|(''t'')}}}} and {{math|{{SubSup|'''<var>X</var>'''|2|(''b'')}}}} can be computed via
| |
| | |
| :{{math|{{SubSup|'''<var>X</var>'''|1|(''t'')}} {{=}} {{SubSup|'''<var>G</var>'''|1|(''t'')}} − {{SubSup|'''<var>V</var>'''|1|(''t'')}}{{SubSup|'''<var>X</var>'''|2|(''t'')}}}},
| |
| :{{math|{{SubSup|'''<var>X</var>'''|2|(''b'')}} {{=}} {{SubSup|'''<var>G</var>'''|2|(''b'')}} − {{SubSup|'''<var>W</var>'''|2|(''b'')}}{{SubSup|'''<var>X</var>'''|1|(''b'')}}}}. | |
| | |
| =====The multiple-partition case=====
| |
| Assume that {{math|<var>p</var>}} is a power of two, i.e., {{math|<var>p</var> {{=}} 2<sup><var>d</var></sup>}}. Consider a block diagonal matrix
| |
| | |
| :{{math|'''<var>D̃</var>'''<sub>1</sub> {{=}} diag({{SubSup|'''<var>D̃</var>'''|1|[1]}},…,{{SubSup|'''<var>D̃</var>'''|<var>p</var>/2|[1]}})}}
| |
| | |
| where
| |
| | |
| :<math>
| |
| \boldsymbol{\tilde{D}}_k^{[1]}=
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_{2k-1}^{(t)}\\
| |
| \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_{2k-1}^{(b)} & \boldsymbol{0}\\
| |
| \boldsymbol{0} & \boldsymbol{W}_{2k}^{(t)} & \boldsymbol{I}_m & \boldsymbol{0}\\
| |
| & \boldsymbol{W}_{2k}^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m
| |
| \end{bmatrix}
| |
| </math>
| |
| | |
| for {{math|<var>k</var> {{=}} 1,…,<var>p</var>/2}}. Notice that {{math|'''<var>D̃</var>'''<sub>1</sub>}} essentially consists of diagonal blocks of order {{math|4<var>m</var>}} extracted from {{math|'''<var>S̃</var>'''}}. Now we factorize {{math|'''<var>S̃</var>'''}} as | |
| | |
| :{{math|'''<var>S̃</var>''' {{=}} '''<var>D̃</var>'''<sub>1</sub>'''<var>S̃</var>'''<sub>2</sub>}}.
| |
| | |
| The new matrix {{math|'''<var>S̃</var>'''<sub>2</sub>}} has the form
| |
| | |
| :<math>
| |
| \begin{bmatrix}
| |
| \boldsymbol{I}_{3m} & \boldsymbol{0} & \boldsymbol{V}_1^{[2](t)}\\
| |
| \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_1^{[2](b)} & \boldsymbol{0}\\
| |
| \boldsymbol{0} & \boldsymbol{W}_2^{[2](t)} & \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_2^{[2](t)}\\
| |
| & \boldsymbol{W}_2^{[2](b)} & \boldsymbol{0} & \boldsymbol{I}_{3m} & \boldsymbol{V}_2^{[2](b)} & \boldsymbol{0} \\
| |
| & & \ddots & \ddots & \ddots & \ddots & \ddots\\
| |
| & & & \boldsymbol{0} & \boldsymbol{W}_{p/2-1}^{[2](t)} & \boldsymbol{I}_{3m} & \boldsymbol{0} & \boldsymbol{V}_{p/2-1}^{[2](t)}\\
| |
| & & & & \boldsymbol{W}_{p/2-1}^{[2](b)} & \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_{p/2-1}^{[2](b)} & \boldsymbol{0}\\
| |
| & & & & & \boldsymbol{0} & \boldsymbol{W}_{p/2}^{[2](t)} & \boldsymbol{I}_m & \boldsymbol{0}\\
| |
| & & & & & & \boldsymbol{W}_{p/2}^{[2](b)} & \boldsymbol{0} & \boldsymbol{I}_{3m}
| |
| \end{bmatrix}\text{.}
| |
| </math> | |
| | |
| Its structure is very similar to that of {{math|'''<var>S̃</var>'''<sub>2</sub>}}, only differing in the number of spikes and their height (their width stays the same at {{math|<var>m</var>}}). Thus, a similar factorization step can be performed on {{math|'''<var>S̃</var>'''<sub>2</sub>}} to produce
| |
| | |
| :{{math|'''<var>S̃</var>'''<sub>2</sub> {{=}} '''<var>D̃</var>'''<sub>2</sub>'''<var>S̃</var>'''<sub>3</sub>}} | |
| | |
| and
| |
| | |
| :{{math|'''<var>S̃</var>''' {{=}} '''<var>D̃</var>'''<sub>1</sub>'''<var>D̃</var>'''<sub>2</sub>'''<var>S̃</var>'''<sub>3</sub>}}.
| |
| | |
| Such factorization steps can be performed recursively. After {{math|<var>d</var> − 1}} steps, we obtain the factorization
| |
| | |
| :{{math|'''<var>S̃</var>''' {{=}} '''<var>D̃</var>'''<sub>1</sub>'''⋯<var>D̃</var>'''<sub><var>d</var>−1</sub>'''<var>S̃</var>'''<sub><var>d</var></sub>}},
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| where {{math|'''<var>S̃</var>'''<sub><var>d</var></sub>}} has only two spikes. The reduced system will then be solved via
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| :{{math|'''<var>X̃</var>''' {{=}} {{SubSup|'''<var>S̃</var>'''|<var>d</var>|−1}}{{SubSup|'''<var>D̃</var>'''|<var>d</var>−1|−1}}⋯{{SubSup|'''<var>D̃</var>'''|1|−1}}'''<var>G̃</var>'''}}.
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| The block LU factorization technique in the two-partition case can be used to handle the solving steps involving {{math|'''<var>D̃</var>'''<sub>1</sub>}}, …, {{math|'''<var>D̃</var>'''<sub><var>d</var>−1</sub>}} and {{math|'''<var>S̃</var>'''<sub><var>d</var></sub>}} for they essentially solve multiple independent systems of generalized two-partition forms.
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| Generalization to cases where {{math|<var>p</var>}} is not a power of two is almost trivial.
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| ===Truncated SPIKE===
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| When {{math|'''<var>A</var>'''}} is diagonally-dominant, in the reduced system
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| :<math>
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| \begin{bmatrix}
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| \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_1^{(t)}\\
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| \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)} & \boldsymbol{0}\\
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| \boldsymbol{0} & \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_2^{(t)}\\
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| & \boldsymbol{W}_2^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_2^{(b)} & \boldsymbol{0} \\
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| & & \ddots & \ddots & \ddots & \ddots & \ddots\\
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| & & & \boldsymbol{0} & \boldsymbol{W}_{p-1}^{(t)} & \boldsymbol{I}_m & \boldsymbol{0} & \boldsymbol{V}_{p-1}^{(t)}\\
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| & & & & \boldsymbol{W}_{p-1}^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m & \boldsymbol{V}_{p-1}^{(b)} & \boldsymbol{0}\\
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| & & & & & \boldsymbol{0} & \boldsymbol{W}_p^{(t)} & \boldsymbol{I}_m & \boldsymbol{0}\\
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| & & & & & & \boldsymbol{W}_p^{(b)} & \boldsymbol{0} & \boldsymbol{I}_m
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| \end{bmatrix}
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| \begin{bmatrix}
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| \boldsymbol{X}_1^{(t)}\\
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| \boldsymbol{X}_1^{(b)}\\
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| \boldsymbol{X}_2^{(t)}\\
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| \boldsymbol{X}_2^{(b)}\\
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| \vdots\\
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| \boldsymbol{X}_{p-1}^{(t)}\\
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| \boldsymbol{X}_{p-1}^{(b)}\\
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| \boldsymbol{X}_p^{(t)}\\
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| \boldsymbol{X}_p^{(b)}
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| \end{bmatrix}
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| =
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| \begin{bmatrix}
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| \boldsymbol{G}_1^{(t)}\\
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| \boldsymbol{G}_1^{(b)}\\
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| \boldsymbol{G}_2^{(t)}\\
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| \boldsymbol{G}_2^{(b)}\\
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| \vdots\\
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| \boldsymbol{G}_{p-1}^{(t)}\\
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| \boldsymbol{G}_{p-1}^{(b)}\\
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| \boldsymbol{G}_p^{(t)}\\
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| \boldsymbol{G}_p^{(b)}
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| \end{bmatrix}\text{,}
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| </math>
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| the blocks {{math|{{SubSup|'''<var>V</var>'''|<var>j</var>|(''t'')}}}} and {{math|{{SubSup|'''<var>W</var>'''|<var>j</var>|(''b'')}}}} are often negligible. With them omitted, the reduced system becomes block diagonal | |
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| :<math>
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| \begin{bmatrix}
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| \boldsymbol{I}_m\\
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| & \boldsymbol{I}_m & \boldsymbol{V}_1^{(b)}\\ | |
| & \boldsymbol{W}_2^{(t)} & \boldsymbol{I}_m\\
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| & & & \boldsymbol{I}_m & \boldsymbol{V}_2^{(b)}\\
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| & & & \ddots & \ddots & \ddots\\
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| & & & & \boldsymbol{W}_{p-1}^{(t)} & \boldsymbol{I}_m\\
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| & & & & & & \boldsymbol{I}_m & \boldsymbol{V}_{p-1}^{(b)}\\
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| & & & & & & \boldsymbol{W}_p^{(t)} & \boldsymbol{I}_m\\
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| & & & & & & & & \boldsymbol{I}_m
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| \end{bmatrix}
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| \begin{bmatrix}
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| \boldsymbol{X}_1^{(t)}\\
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| \boldsymbol{X}_1^{(b)}\\
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| \boldsymbol{X}_2^{(t)}\\
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| \boldsymbol{X}_2^{(b)}\\
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| \vdots\\
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| \boldsymbol{X}_{p-1}^{(t)}\\
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| \boldsymbol{X}_{p-1}^{(b)}\\
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| \boldsymbol{X}_p^{(t)}\\
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| \boldsymbol{X}_p^{(b)}
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| \end{bmatrix}
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| =
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| \begin{bmatrix}
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| \boldsymbol{G}_1^{(t)}\\
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| \boldsymbol{G}_1^{(b)}\\
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| \boldsymbol{G}_2^{(t)}\\
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| \boldsymbol{G}_2^{(b)}\\
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| \vdots\\
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| \boldsymbol{G}_{p-1}^{(t)}\\
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| \boldsymbol{G}_{p-1}^{(b)}\\
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| \boldsymbol{G}_p^{(t)}\\
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| \boldsymbol{G}_p^{(b)}
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| \end{bmatrix}
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| </math>
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| and can be easily solved in parallel.
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| The truncated SPIKE algorithm can be wrapped inside some outer iterative scheme (e.g., [[Biconjugate gradient stabilized method|BiCGSTAB]] or [[iterative refinement]]) to improve the accuracy of the solution.
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| ==SPIKE as a preconditioner==
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| The SPIKE algorithm can also function as a preconditioner for iterative methods for solving linear systems. To solve a linear system {{math|'''<var>Ax</var>''' {{=}} '''<var>b</var>'''}} using a SPIKE-preconditioned iterative solver, one extracts center bands from {{math|'''<var>A</var>'''}} to form a banded preconditioner {{math|'''<var>M</var>'''}} and solves linear systems involving {{math|'''<var>M</var>'''}} in each iteration with the SPIKE algorithm.
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| In order for the preconditioner to be effective, row and/or column permutation is usually necessary to move “heavy” elements of {{math|'''<var>A</var>'''}} close to the diagonal so that they are covered by the preconditioner. This can be accomplished by computing the [[Algebraic connectivity#The Fiedler vector|weighted spectral reordering]] of {{math|'''<var>A</var>'''}}.
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| The SPIKE algorithm can be generalized by not restricting the preconditioner to be strictly banded. In particular, the diagonal block in each partition can be a general matrix and thus handled by a direct general linear system solver rather than a banded solver. This enhances the preconditioner, and hence allows better chance of convergence and reduces the number of iterations.
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| ==Implementations==
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| [[Intel]] offers an implementation of the SPIKE algorithm under the name ''Intel Adaptive Spike-Based Solver''.{{ref|1}} | |
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| ==References==
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| # {{cite doi|10.1016/j.parco.2005.07.005}}
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| # {{cite doi|10.1016/j.compfluid.2005.07.005}}
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| # {{cite doi|10.1137/080719571}}
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| # {{cite doi|10.1007/978-3-642-03869-3_74}}
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| # {{note|1}}{{cite web|url=http://software.intel.com/en-us/articles/intel-adaptive-spike-based-solver/|title=Intel Adaptive Spike-Based Solver - Intel Software Network|accessdate=2009-03-23}}
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| # {{cite doi|10.1145/322047.322054}}
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| {{Numerical linear algebra}}
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| {{DEFAULTSORT:Spike Algorithm}}
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| [[Category:Numerical linear algebra]]
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