## mgl-mat

2023-02-15

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# MAT Manual
###### \[in package MGL-MAT\]
## MGL-MAT ASDF System
- Version: 0.1.0
- Description: MAT is library for working with multi-dimensional
arrays which supports efficient interfacing to foreign and CUDA
code. BLAS and CUBLAS bindings are available.
- Licence: MIT, see COPYING.
- Author: GĂˇbor Melis <mega@retes.hu>
- Mailto: [mega@retes.hu](mailto:mega@retes.hu)
- Homepage: [http://melisgl.github.io/mgl-mat](http://melisgl.github.io/mgl-mat)
- Bug tracker: [https://github.com/melisgl/mgl-mat/issues](https://github.com/melisgl/mgl-mat/issues)
- Source control: [GIT](https://github.com/melisgl/mgl-mat.git)
## Links
Here is the [official
repository](https://github.com/melisgl/mgl-mat) and the [HTML
documentation](http://melisgl.github.io/mgl-mat/mat-manual.html)
for the latest version.
## Introduction
### What's MGL-MAT?
MGL-MAT is library for working with multi-dimensional arrays
which supports efficient interfacing to foreign and CUDA code with
automatic translations between cuda, foreign and lisp storage. BLAS
and CUBLAS bindings are available.
### What kind of matrices are supported?
Currently only row-major single and double float matrices are
supported, but it would be easy to add single and double precision
complex types too. Other numeric types, such as byte and native
integer, can be added too, but they are not supported by CUBLAS.
There are no restrictions on the number of dimensions, and reshaping
is possible. All functions operate on the visible portion of the
matrix (which is subject to displacement and shaping), invisible
elements are not affected.
### Where to Get it?
All dependencies are in quicklisp except for
[CL-CUDA](https://github.com/takagi/cl-cuda) that needs to be
fetched from github. Just clone CL-CUDA and MGL-MAT into
`quicklisp/local-projects/` and you are set. MGL-MAT itself lives
[at github](https://github.com/melisgl/mgl-mat), too.
Prettier-than-markdown HTML documentation cross-linked with other
libraries is
[available](http://melisgl.github.io/mgl-pax-world/mat-manual.html)
as part of [PAX World](http://melisgl.github.io/mgl-pax-world/).
## Tutorial
We are going to see how to create matrices, access their contents.
Creating matrices is just like creating lisp arrays:
```commonlisp
(make-mat '6)
==> #<MAT 6 A #(0.0d0 0.0d0 0.0d0 0.0d0 0.0d0 0.0d0)>
(make-mat '(2 3) :ctype :float :initial-contents '((1 2 3) (4 5 6)))
==> #<MAT 2x3 AB #2A((1.0 2.0 3.0) (4.0 5.0 6.0))>
(make-mat '(2 3 4) :initial-element 1)
==> #<MAT 2x3x4 A #3A(((1.0d0 1.0d0 1.0d0 1.0d0)
--> (1.0d0 1.0d0 1.0d0 1.0d0)
--> (1.0d0 1.0d0 1.0d0 1.0d0))
--> ((1.0d0 1.0d0 1.0d0 1.0d0)
--> (1.0d0 1.0d0 1.0d0 1.0d0)
--> (1.0d0 1.0d0 1.0d0 1.0d0)))>
```
The most prominent difference from lisp arrays is that `MAT`s are
always numeric and their element type (called CTYPE here) defaults
to :DOUBLE.
Individual elements can be accessed or set with MREF:
```commonlisp
(let ((m (make-mat '(2 3))))
(setf (mref m 0 0) 1)
(setf (mref m 0 1) (* 2 (mref m 0 0)))
(incf (mref m 0 2) 4)
m)
==> #<MAT 2x3 AB #2A((1.0d0 2.0d0 4.0d0) (0.0d0 0.0d0 0.0d0))>
```
Compared to AREF MREF is a very expensive operation and it's best
used sparingly. Instead, typical code relies much more on matrix
level operations:
```commonlisp
(princ (scal! 2 (fill! 3 (make-mat 4))))
.. #<MAT 4 BF #(6.0d0 6.0d0 6.0d0 6.0d0)>
==> #<MAT 4 ABF #(6.0d0 6.0d0 6.0d0 6.0d0)>
```
The content of a matrix can be accessed in various representations
called *facets*. MGL-MAT takes care of synchronizing these facets
by copying data around lazily, but doing its best to share storage
for facets that allow it.
Notice the `ABF` in the printed results. It illustrates that behind
the scenes FILL! worked on the [BACKING-ARRAY][facet-name]
facet (hence the `B`) that's basically a 1d lisp array. SCAL! on the
other hand made a foreign call to the BLAS `dscal` function for
which it needed the [FOREIGN-ARRAY][facet-name] facet (`F`).
Finally, the `A` stands for the [ARRAY][facet-name] facet that was
created when the array was printed. All facets are up-to-date (else
some of the characters would be lowercase). This is possible because
these three facets actually share storage which is never the case
for the [CUDA-ARRAY][facet-name] facet. Now if we have a
CUDA-capable GPU, CUDA can be enabled with WITH-CUDA\*:
```commonlisp
(with-cuda* ()
(princ (scal! 2 (fill! 3 (make-mat 4)))))
.. #<MAT 4 C #(6.0d0 6.0d0 6.0d0 6.0d0)>
==> #<MAT 4 A #(6.0d0 6.0d0 6.0d0 6.0d0)>
```
Note the lonely `C` showing that only the [CUDA-ARRAY][facet-name]
facet was used for both FILL! and SCAL!. When WITH-CUDA\* exits and
destroys the CUDA context, it destroys all CUDA facets, moving their
data to the [ARRAY][facet-name] facet, so the returned MAT only has
that facet.
When there is no high-level operation that does what we want, we may
need to add new operations. This is usually best accomplished by
accessing one of the facets directly, as in the following example:
```commonlisp
(defun logdet (mat)
"Logarithm of the determinant of MAT. Return -1, 1 or 0 (or
equivalent) to correct for the sign, as the second value."
(with-facets ((array (mat 'array :direction :input)))
(lla:logdet array)))
```
Notice that LOGDET doesn't know about CUDA at all. WITH-FACETS
gives it the content of the matrix as a normal multidimensional lisp
array, copying the data from the GPU or elsewhere if necessary. This
allows new representations (`FACET`s) to be added easily and it also
avoids copying if the facet is already up-to-date. Of course, adding
CUDA support to LOGDET could make it more efficient.
Adding support for matrices that, for instance, live on a remote
machine is thus possible with a new facet type and existing code
would continue to work (albeit possibly slowly). Then one could
optimize the bottleneck operations by sending commands over the
network instead of copying data.
It is a bad idea to conflate resource management policy and
algorithms. MGL-MAT does its best to keep them separate.
## Basics
- [class] MAT CUBE
A MAT is a data CUBE that is much like a lisp
array, it supports DISPLACEMENT, arbitrary DIMENSIONS and
INITIAL-ELEMENT with the usual semantics. However, a MAT supports
different representations of the same data. See @MAT-TUTORIAL for
an introduction.
- [reader] MAT-CTYPE MAT (:CTYPE = \*DEFAULT-MAT-CTYPE\*)
One of *SUPPORTED-CTYPES*. The matrix can hold
only values of this type.
- [reader] MAT-DISPLACEMENT MAT (:DISPLACEMENT = 0)
A value in the `[0,MAX-SIZE]` interval. This is
like the DISPLACED-INDEX-OFFSET of a lisp array, but displacement
is relative to the start of the underlying storage vector.
- [reader] MAT-DIMENSIONS MAT (:DIMENSIONS)
Like ARRAY-DIMENSIONS. It holds a list of
dimensions, but it is allowed to pass in scalars too.
- [function] MAT-DIMENSION MAT AXIS-NUMBER
Return the dimension along AXIS-NUMBER. Similar to
ARRAY-DIMENSION.
- [reader] MAT-INITIAL-ELEMENT MAT (:INITIAL-ELEMENT = 0)
If non-nil, then when a facet is created, it is
filled with INITIAL-ELEMENT coerced to the appropriate numeric
type. If NIL, then no initialization is performed.
- [reader] MAT-SIZE MAT
The number of elements in the visible portion of
the array. This is always the product of the elements
MAT-DIMENSIONS and is similar to ARRAY-TOTAL-SIZE.
- [reader] MAT-MAX-SIZE MAT (:MAX-SIZE)
The number of elements for which storage may be
allocated. This is DISPLACEMENT + MAT-SIZE + `SLACK` where `SLACK`
is the number of trailing invisible elements.
- [function] MAKE-MAT DIMENSIONS &REST ARGS &KEY (CTYPE \*DEFAULT-MAT-CTYPE\*) (DISPLACEMENT 0) MAX-SIZE INITIAL-ELEMENT INITIAL-CONTENTS (SYNCHRONIZATION \*DEFAULT-SYNCHRONIZATION\*) DISPLACED-TO (CUDA-ENABLED \*DEFAULT-MAT-CUDA-ENABLED\*)
Return a new MAT object. If INITIAL-CONTENTS is given then the
matrix contents are initialized with REPLACE!. See class MAT for the
description of the rest of the parameters. This is exactly
what (MAKE-INSTANCE 'MAT ...) does except DIMENSIONS is not a
keyword argument so that MAKE-MAT looks more like MAKE-ARRAY. The
semantics of SYNCHRONIZATION are desribed in the
@CUBE-SYNCHRONIZATION section.
If specified, DISPLACED-TO must be a MAT object large enough (in the
sense of its MAT-SIZE), to hold DISPLACEMENT plus `(REDUCE #'*
DIMENSIONS)` elements. Just like with MAKE-ARRAY, INITIAL-ELEMENT
and INITIAL-CONTENTS must not be supplied together with
DISPLACED-TO. See @MAT-SHAPING for more.
- [function] ARRAY-TO-MAT ARRAY &KEY CTYPE (SYNCHRONIZATION \*DEFAULT-SYNCHRONIZATION\*)
Create a MAT that's equivalent to ARRAY. Displacement of the
created array will be 0 and the size will be equal to
ARRAY-TOTAL-SIZE. If CTYPE is non-nil, then it will be the ctype of
the new matrix. Else ARRAY's type is converted to a ctype. If there
is no corresponding ctype, then *DEFAULT-MAT-CTYPE* is used.
Elements of ARRAY are coerced to CTYPE.
Also see @CUBE-SYNCHRONIZATION.
- [function] MAT-TO-ARRAY MAT
- [function] REPLACE! MAT SEQ-OF-SEQS
Replace the contents of MAT with the elements of SEQ-OF-SEQS.
SEQ-OF-SEQS is a nested sequence of sequences similar to the
INITIAL-CONTENTS argument of MAKE-ARRAY. The total number of
elements must match the size of MAT. Returns MAT.
SEQ-OF-SEQS may contain multi-dimensional arrays as *leafs*, so the
following is legal:
```common-lisp
(replace! (make-mat '(1 2 3)) '(#2A((1 2 3) (4 5 6))))
==> #<MAT 1x2x3 AB #3A(((1.0d0 2.0d0 3.0d0) (4.0d0 5.0d0 6.0d0)))>
```
- [function] MREF MAT &REST INDICES
Like AREF for arrays. Don't use this if you care about performance
at all. SETFable. When set, the value is coerced to the ctype of MAT
with COERCE-TO-CTYPE. Note that currently MREF always operates on
the BACKING-ARRAY facet so it can trigger copying of facets. When
it's SETF'ed, however, it will update the CUDA-ARRAY if cuda is
enabled and it is up-to-date or there are no facets at all.
- [function] ROW-MAJOR-MREF MAT INDEX
Like ROW-MAJOR-AREF for arrays. Don't use this if you care about
performance at all. SETFable. When set, the value is coerced to the
ctype of MAT with COERCE-TO-CTYPE. Note that currently
ROW-MAJOR-MREF always operates on the BACKING-ARRAY facet so it can
trigger copying of facets. When it's SETF'ed, however, it will
update the CUDA-ARRAY if cuda is enabled and it is up-to-date or
there are no facets at all.
- [function] MAT-ROW-MAJOR-INDEX MAT &REST SUBSCRIPTS
Like ARRAY-ROW-MAJOR-INDEX for arrays.
## Element types
- [variable] *SUPPORTED-CTYPES* (:FLOAT :DOUBLE)
- [type] CTYPE
This is basically `(MEMBER :FLOAT :DOUBLE)`.
- [variable] *DEFAULT-MAT-CTYPE* :DOUBLE
By default MATs are created with this ctype. One of :FLOAT
or :DOUBLE.
- [function] COERCE-TO-CTYPE X &KEY (CTYPE \*DEFAULT-MAT-CTYPE\*)
Coerce the scalar X to the lisp type corresponding to CTYPE.
## Printing
- [variable] *PRINT-MAT* T
Controls whether the contents of a MAT object are printed as an
array (subject to the standard printer control variables).
- [variable] *PRINT-MAT-FACETS* T
Controls whether a summary of existing and up-to-date facets is
printed when a MAT object is printed. The summary that looks like
`ABcfh` indicates that all five facets ([ARRAY][facet-name],
[BACKING-ARRAY][facet-name], [CUDA-ARRAY][facet-name],
[FOREIGN-ARRAY][facet-name], [CUDA-HOST-ARRAY][facet-name]) are
present and the first two are up-to-date. A summary of a single #-
indicates that there are no facets.
## Shaping
We are going to discuss various ways to change the visible portion
and dimensions of matrices. Conceptually a matrix has an *underlying
non-displaced storage vector*. For `(MAKE-MAT 10 :DISPLACEMENT
7 :MAX-SIZE 21)` this underlying vector looks like this:
displacement | visible elements | slack
. . . . . . . 0 0 0 0 0 0 0 0 0 0 . . . .
Whenever a matrix is reshaped (or *displaced to* in lisp
terminology), its displacement and dimensions change but the
underlying vector does not.
The rules for accessing displaced matrices is the same as always:
multiple readers can run in parallel, but attempts to write will
result in an error if there are either readers or writers on any of
the matrices that share the same underlying vector.
### Comparison to Lisp Arrays
One way to reshape and displace MAT objects is with MAKE-MAT and
its DISPLACED-TO argument whose semantics are similar to that of
MAKE-ARRAY in that the displacement is *relative* to the
displacement of DISPLACED-TO.
```commonlisp
(let* ((base (make-mat 10 :initial-element 5 :displacement 1))
(mat (make-mat 6 :displaced-to base :displacement 2)))
(fill! 1 mat)
(values base mat))
==> #<MAT 1+10+0 A #(5.0d0 5.0d0 1.0d0 1.0d0 1.0d0 1.0d0 1.0d0 1.0d0 5.0d0
--> 5.0d0)>
==> #<MAT 3+6+2 AB #(1.0d0 1.0d0 1.0d0 1.0d0 1.0d0 1.0d0)>
```
There are important semantic differences compared to lisp arrays all
which follow from the fact that displacement operates on the
underlying conceptual non-displaced vector.
- Matrices can be displaced and have slack even without DISPLACED-TO
just like `BASE` in the above example.
- It's legal to alias invisible elements of DISPLACED-TO as long as
the new matrix fits into the underlying storage.
- Negative displacements are allowed with DISPLACED-TO as long as
the adjusted displacement is non-negative.
- Further shaping operations can make invisible portions of the
DISPLACED-TO matrix visible by changing the displacement.
- In contrast to ARRAY-DISPLACEMENT, MAT-DISPLACEMENT only returns
an offset into the underlying storage vector.
### Functional Shaping
The following functions are collectively called the functional
shaping operations, since they don't alter their arguments in any
way. Still, since storage is aliased modification to the returned
matrix will affect the original.
- [function] RESHAPE-AND-DISPLACE MAT DIMENSIONS DISPLACEMENT
Return a new matrix of DIMENSIONS that aliases MAT's storage at
offset DISPLACEMENT. DISPLACEMENT 0 is equivalent to the start of
the storage of MAT regardless of MAT's displacement.
- [function] RESHAPE MAT DIMENSIONS
Return a new matrix of DIMENSIONS whose displacement is the same as
the displacement of MAT.
- [function] DISPLACE MAT DISPLACEMENT
Return a new matrix that aliases MAT's storage at offset
DISPLACEMENT. DISPLACEMENT 0 is equivalent to the start of the
storage of MAT regardless of MAT's displacement. The returned matrix
has the same dimensions as MAT.
### Destructive Shaping
The following destructive operations don't alter the contents of
the matrix, but change what is visible. ADJUST! is the odd one out,
it may create a new MAT.
- [function] RESHAPE-AND-DISPLACE! MAT DIMENSIONS DISPLACEMENT
Change the visible (or active) portion of MAT by altering its
displacement offset and dimensions. Future operations will only
affect this visible portion as if the rest of the elements were not
there. Return MAT.
DISPLACEMENT + the new size must not exceed MAT-MAX-SIZE.
Furthermore, there must be no facets being viewed (with WITH-FACETS)
when calling this function as the identity of the facets is not
stable.
- [function] RESHAPE! MAT DIMENSIONS
Like RESHAPE-AND-DISPLACE! but only alters the dimensions.
- [function] DISPLACE! MAT DISPLACEMENT
Like RESHAPE-AND-DISPLACE! but only alters the displacement.
- [function] RESHAPE-TO-ROW-MATRIX! MAT ROW
Reshape the 2d MAT to make only a single ROW visible. This is made
possible by the row-major layout, hence no column counterpart.
Return MAT.
- [macro] WITH-SHAPE-AND-DISPLACEMENT (MAT &OPTIONAL (DIMENSIONS NIL) (DISPLACEMENT NIL)) &BODY BODY
Reshape and displace MAT if DIMENSIONS and/or DISPLACEMENT is given
and restore the original shape and displacement after BODY is
executed. If neither is specificed, then nothing will be changed,
but BODY is still allowed to alter the shape and displacement.
- [function] ADJUST! MAT DIMENSIONS DISPLACEMENT &KEY (DESTROY-OLD-P T)
Like RESHAPE-AND-DISPLACE! but creates a new matrix if MAT isn't
large enough. If a new matrix is created, the contents are not
copied over and the old matrix is destroyed with DESTROY-CUBE if
DESTROY-OLD-P.
## Assembling
The functions here assemble a single MAT from a number of
[MAT][]s.
- [function] STACK! AXIS MATS MAT
Stack MATS along AXIS into MAT and return MAT. If AXIS is 0, place
MATS into MAT below each other starting from the top. If AXIS is 1,
place MATS side by side starting from the left. Higher AXIS are also
supported. All dimensions except for AXIS must be the same for all
MATS.
- [function] STACK AXIS MATS &KEY (CTYPE \*DEFAULT-MAT-CTYPE\*)
Like STACK! but return a new MAT of CTYPE.
```commonlisp
(stack 1 (list (make-mat '(3 2) :initial-element 0)
(make-mat '(3 1) :initial-element 1)))
==> #<MAT 3x3 B #2A((0.0d0 0.0d0 1.0d0)
--> (0.0d0 0.0d0 1.0d0)
--> (0.0d0 0.0d0 1.0d0))>
```
## Caching
Allocating and initializing a MAT object and its necessary facets
can be expensive. The following macros remember the previous value
of a binding in the same thread and /place/. Only weak references
are constructed so the cached objects can be garbage collected.
While the cache is global, thread safety is guaranteed by having
separate subcaches per thread. Each subcache is keyed by a /place/
object that's either explicitly specified or else is unique to each
invocation of the caching macro, so different occurrences of caching
macros in the source never share data. Still, recursion could lead
to data sharing between different invocations of the same function.
To prevent this, the cached object is removed from the cache while
it is used so other invocations will create a fresh one which isn't
particularly efficient but at least it's safe.
- [macro] WITH-THREAD-CACHED-MAT (VAR DIMENSIONS &REST ARGS &KEY (PLACE :SCRATCH) (CTYPE '\*DEFAULT-MAT-CTYPE\*) (DISPLACEMENT 0) MAX-SIZE (INITIAL-ELEMENT 0) INITIAL-CONTENTS) &BODY BODY
Bind VAR to a matrix of DIMENSIONS, CTYPE, etc. Cache this matrix,
and possibly reuse it later by reshaping it. When BODY exits the
cached object is updated with the binding of VAR which BODY may
change.
There is a separate cache for each thread and each `PLACE` (under
EQ). Since every cache holds exactly one MAT per CTYPE, nested
WITH-THREAD-CACHED-MAT often want to use different `PLACE`s. By
convention, these places are called `:SCRATCH-1`, `:SCRATCH-2`,
etc.
- [macro] WITH-THREAD-CACHED-MATS SPECS &BODY BODY
A shorthand for writing nested WITH-THREAD-CACHED-MAT calls.
```
(with-thread-cached-mat (a ...)
(with-thread-cached-mat (b ...)
...))
```
is equivalent to:
```
(with-thread-cached-mat ((a ...)
(b ...))
...)
```
- [macro] WITH-ONES (VAR DIMENSIONS &KEY (CTYPE '\*DEFAULT-MAT-CTYPE\*) (PLACE :ONES)) &BODY BODY
Bind VAR to a matrix of DIMENSIONS whose every element is 1. The
matrix is cached for efficiency.
## BLAS Operations
Only some BLAS functions are implemented, but it should be easy to
add more as needed. All of them default to using CUDA, if it is
initialized and enabled (see USE-CUDA-P).
Level 1 BLAS operations
- [function] ASUM X &KEY (N (MAT-SIZE X)) (INCX 1)
Return the l1 norm of X, that is, sum of the absolute values of its
elements.
- [function] AXPY! ALPHA X Y &KEY (N (MAT-SIZE X)) (INCX 1) (INCY 1)
Set Y to ALPHA \* X + Y. Return Y.
- [function] COPY! X Y &KEY (N (MAT-SIZE X)) (INCX 1) (INCY 1)
Copy X into Y. Return Y.
- [function] DOT X Y &KEY (N (MAT-SIZE X)) (INCX 1) (INCY 1)
Return the dot product of X and Y.
- [function] NRM2 X &KEY (N (MAT-SIZE X)) (INCX 1)
Return the l2 norm of X, which is the square root of the sum of the
squares of its elements.
- [function] SCAL! ALPHA X &KEY (N (MAT-SIZE X)) (INCX 1)
Set X to ALPHA \* X. Return X.
Level 3 BLAS operations
- [function] GEMM! ALPHA A B BETA C &KEY TRANSPOSE-A? TRANSPOSE-B? M N K LDA LDB LDC
Basically C = ALPHA \* A' \* B' + BETA \* C. A' is A or its transpose
depending on TRANSPOSE-A?. B' is B or its transpose depending on
TRANSPOSE-B?. Returns C.
A' is an MxK matrix. B' is a KxN matrix. C is an MxN matrix.
LDA is the width of the matrix A (not of A'). If A is not transposed,
then K <= LDA, if it's transposed then M <= LDA.
LDB is the width of the matrix B (not of B'). If B is not transposed,
then N <= LDB, if it's transposed then K <= LDB.
In the example below M=3, N=2, K=5, LDA=6, LDB=3, LDC=4. The cells
marked with + do not feature in the calculation.
N
--+
--+
K -B+
--+
--+
+++
K
-----+ --++
M --A--+ -C++
-----+ --++
++++++ ++++
## Destructive API
- [function] .SQUARE! X &KEY (N (MAT-SIZE X))
Set X to its elementwise square. Return X.
- [function] .SQRT! X &KEY (N (MAT-SIZE X))
Set X to its elementwise square root. Return X.
- [function] .LOG! X &KEY (N (MAT-SIZE X))
Set X to its elementwise natural logarithm. Return X.
- [function] .EXP! X &KEY (N (MAT-SIZE X))
Apply EXP elementwise to X in a destructive manner. Return X.
- [function] .EXPT! X POWER
Raise matrix X to POWER in an elementwise manner. Return X. Note
that CUDA and non-CUDA implementations may disagree on the treatment
of NaNs, infinities and complex results. In particular, the lisp
implementation always computes the REALPART of the results while
CUDA's pow() returns NaNs instead of complex numbers.
- [function] .INV! X &KEY (N (MAT-SIZE X))
Set X to its elementwise inverse `(/ 1 X)`. Return X.
- [function] .LOGISTIC! X &KEY (N (MAT-SIZE X))
Destructively apply the logistic function to X in an elementwise
manner. Return X.
- [function] .+! ALPHA X
Add the scalar ALPHA to each element of X destructively modifying
X. Return X.
- [function] .*! X Y
- [function] GEEM! ALPHA A B BETA C
Like GEMM!, but multiplication is elementwise. This is not a
standard BLAS routine.
- [function] GEERV! ALPHA A X BETA B
GEneric Elementwise Row - Vector multiplication. `B = beta * B +
alpha a .* X*` where `X*` is a matrix of the same shape as A whose
every row is X. Perform elementwise multiplication on each row of A
with the vector X and add the scaled result to the corresponding row
of B. Return B. This is not a standard BLAS routine.
- [function] .<! X Y
For each element of X and Y set Y to 1 if the element in Y is
greater than the element in X, and to 0 otherwise. Return Y.
- [function] .MIN! ALPHA X
Set each element of X to ALPHA if it's greater than ALPHA. Return
X.
- [function] .MAX! ALPHA X
Set each element of X to ALPHA if it's less than ALPHA. Return X.
- [function] ADD-SIGN! ALPHA A BETA B
Add the elementwise sign (-1, 0 or 1 for negative, zero and
positive numbers respectively) of A times ALPHA to BETA \* B. Return
B.
- [function] FILL! ALPHA X &KEY (N (MAT-SIZE X))
Fill matrix X with ALPHA. Return X.
- [function] SUM! X Y &KEY AXIS (ALPHA 1) (BETA 0)
Sum matrix X along AXIS and add ALPHA \* SUMS to BETA \* Y
destructively modifying Y. Return Y. On a 2d matrix (nothing else is
supported currently), if AXIS is 0, then columns are summed, if AXIS
is 1 then rows are summed.
- [function] SCALE-ROWS! SCALES A &KEY (RESULT A)
Set RESULT to `DIAG(SCALES)*A` and return it. `A` is an `MxN`
matrix, SCALES is treated as a length `M` vector.
- [function] SCALE-COLUMNS! SCALES A &KEY (RESULT A)
Set RESULT to `A*DIAG(SCALES)` and return it. `A` is an `MxN`
matrix, SCALES is treated as a length `N` vector.
- [function] .SIN! X &KEY (N (MAT-SIZE X))
Apply SIN elementwise to X in a destructive manner. Return X.
- [function] .COS! X &KEY (N (MAT-SIZE X))
Apply COS elementwise to X in a destructive manner. Return X.
- [function] .TAN! X &KEY (N (MAT-SIZE X))
Apply TAN elementwise to X in a destructive manner. Return X.
- [function] .SINH! X &KEY (N (MAT-SIZE X))
Apply SINH elementwise to X in a destructive manner. Return X.
- [function] .COSH! X &KEY (N (MAT-SIZE X))
Apply COSH elementwise to X in a destructive manner. Return X.
- [function] .TANH! X &KEY (N (MAT-SIZE X))
Apply TANH elementwise to X in a destructive manner. Return X.
Finally, some neural network operations.
- [function] CONVOLVE! X W Y &KEY START STRIDE ANCHOR BATCHED
Y = Y + conv(X, W) and return Y. If BATCHED, then the first
dimension of X and Y is the number of elements in the batch (B),
else B is assumed to be 1. The rest of the dimensions encode the
input (X) and output (Y} N dimensional feature maps. START, STRIDE
and ANCHOR are lists of length N. START is the multi-dimensional
index of the first element of the input feature map (for each
element in the batch) for which the convolution must be computed.
Then (ELT STRIDE (- N 1)) is added to the last element of START and
so on until (ARRAY-DIMENSION X 1) is reached. Then the last element
of START is reset, (ELT STRIDE (- N 2)) is added to the first but
last element of START and we scan the last dimension again. Take a
2d example, START is (0 0), STRIDE is (1 2), and X is a B\*2x7
matrix.
W is:
1 2 1
2 4 2
1 2 1
and ANCHOR is (1 1) which refers to the element of W whose value is
4. This anchor point of W is placed over elements of X whose multi
dimensional index is in numbers in this figure (only one element in
the batch is shown):
0,0 . 0,2 . 0,4 . 0,6
1,0 . 1,2 . 1,4 . 1,6
When applying W at position P of X, the convolution is the sum of
the products of overlapping elements of X and W when W's ANCHOR is
placed at P. Elements of W over the edges of X are multiplied with 0
so are effectively ignored. The order of application of W to
positions defined by START, STRIDE and ANCHOR is undefined.
Y must be a B\*2x4 (or 2x4 if not BATCHED) matrix in this example,
just large enough to hold the results of the convolutions.
- [function] DERIVE-CONVOLVE! X XD W WD YD &KEY START STRIDE ANCHOR BATCHED
Add the dF/dX to XD and and dF/dW to WD where YD is dF/dY for some
function F where Y is the result of convolution with the same
arguments.
- [function] MAX-POOL! X Y &KEY START STRIDE ANCHOR BATCHED POOL-DIMENSIONS
- [function] DERIVE-MAX-POOL! X XD Y YD &KEY START STRIDE ANCHOR BATCHED POOL-DIMENSIONS
Add the dF/dX to XD and and dF/dW to WD where YD is dF/dY for some
function F where Y is the result of MAX-POOL! with the same
arguments.
## Non-destructive API
- [function] COPY-MAT A
Return a copy of the active portion with regards to displacement
and shape of A.
- [function] COPY-ROW A ROW
Return ROW of A as a new 1d matrix.
- [function] COPY-COLUMN A COLUMN
Return COLUMN of A as a new 1d matrix.
- [function] MAT-AS-SCALAR A
Return the first element of A. A must be of size 1.
- [function] SCALAR-AS-MAT X &KEY (CTYPE (LISP-\>CTYPE (TYPE-OF X)))
Return a matrix of one dimension and one element: X. CTYPE, the
type of the matrix, defaults to the ctype corresponding to the type
of X.
- [function] M= A B
Check whether A and B, which must be matrices of the same size, are
elementwise equal.
- [function] TRANSPOSE A
Return the transpose of A.
- [function] M* A B &KEY TRANSPOSE-A? TRANSPOSE-B?
Compute op(A) \* op(B). Where op is either the identity or the
transpose operation depending on TRANSPOSE-A? and TRANSPOSE-B?.
- [function] MM* M &REST ARGS
Convenience function to multiply several matrices.
(mm\* a b c) => a \* b \* c
- [function] M- A B
Return A - B.
- [function] M+ A B
Return A + B.
- [function] INVERT A
Return the inverse of A.
- [function] LOGDET MAT
Logarithm of the determinant of MAT. Return -1, 1 or 0 (or
equivalent) to correct for the sign, as the second value.
## Mappings
- [function] MAP-CONCAT FN MATS MAT &KEY KEY PASS-RAW-P
Call FN with each element of MATS and MAT temporarily reshaped to
the dimensions of the current element of MATS and return MAT. For
the next element the displacement is increased so that there is no
overlap.
MATS is keyed by KEY just like the CL sequence functions. Normally,
FN is called with the matrix returned by KEY. However, if
PASS-RAW-P, then the matrix returned by KEY is only used to
calculate dimensions and the element of MATS that was passed to KEY
is passed to FN, too.
```
(map-concat #'copy! (list (make-mat 2) (make-mat 4 :initial-element 1))
(make-mat '(2 3)))
==> #<MAT 2x3 AB #2A((0.0d0 0.0d0 1.0d0) (1.0d0 1.0d0 1.0d0))>
```
- [function] MAP-DISPLACEMENTS FN MAT DIMENSIONS &KEY (DISPLACEMENT-START 0) DISPLACEMENT-STEP
Call FN with MAT reshaped to DIMENSIONS, first displaced by
DISPLACEMENT-START that's incremented by DISPLACEMENT-STEP each
iteration while there are enough elements left for DIMENSIONS at the
current displacement. Returns MAT.
```commonlisp
(let ((mat (make-mat 14 :initial-contents '(-1 0 1 2 3
4 5 6 7
8 9 10 11 12))))
(reshape-and-displace! mat '(4 3) 1)
(map-displacements #'print mat 4))
..
.. #<MAT 1+4+9 B #(0.0d0 1.0d0 2.0d0 3.0d0)>
.. #<MAT 5+4+5 B #(4.0d0 5.0d0 6.0d0 7.0d0)>
.. #<MAT 9+4+1 B #(8.0d0 9.0d0 10.0d0 11.0d0)>
```
- [function] MAP-MATS-INTO RESULT-MAT FN &REST MATS
Like CL:MAP-INTO but for MAT objects. Destructively modifies
RESULT-MAT to contain the results of applying FN to each element in
the argument MATS in turn.
## Random numbers
Unless noted these work efficiently with CUDA.
- [generic-function] COPY-RANDOM-STATE STATE
Return a copy of STATE be it a lisp or cuda random
state.
- [function] UNIFORM-RANDOM! MAT &KEY (LIMIT 1)
Fill MAT with random numbers sampled uniformly from the \[0,LIMIT)
interval of MAT's type.
- [function] GAUSSIAN-RANDOM! MAT &KEY (MEAN 0) (STDDEV 1)
Fill MAT with independent normally distributed random numbers with
MEAN and STDDEV.
- [function] MV-GAUSSIAN-RANDOM &KEY MEANS COVARIANCES
Return a column vector of samples from the multivariate normal
distribution defined by MEANS (Nx1) and COVARIANCES (NxN). No CUDA
implementation.
- [function] ORTHOGONAL-RANDOM! M &KEY (SCALE 1)
Fill the matrix M with random values in such a way that `M^T * M`
is the identity matrix (or something close if M is wide). Return M.
## I/O
- [variable] *MAT-HEADERS* T
If true, a header with MAT-CTYPE and MAT-SIZE is written by
WRITE-MAT before the contents and READ-MAT checks that these match
the matrix into which it is reading.
- [generic-function] WRITE-MAT MAT STREAM
Write MAT to binary STREAM in portable binary
format. Return MAT. Displacement and size are taken into account,
only visible elements are written. Also see *MAT-HEADERS*.
- [generic-function] READ-MAT MAT STREAM
Destructively modify the visible portion (with
regards to displacement and shape) of MAT by reading MAT-SIZE number
of elements from binary STREAM. Return MAT. Also see
*MAT-HEADERS*.
## Debugging
The largest class of bugs has to do with synchronization of facets
being broken. This is almost always caused by an operation that
mispecifies the DIRECTION argument of WITH-FACET. For example, the
matrix argument of SCAL! should be accessed with direciton :IO. But
if it's :INPUT instead, then subsequent access to the ARRAY facet
will not see the changes made by AXPY!, and if it's :OUTPUT, then
any changes made to the ARRAY facet since the last update of the
CUDA-ARRAY facet will not be copied and from the wrong input SCAL!
will compute the wrong result.
Using the SLIME inspector or trying to access the
[CUDA-ARRAY][facet-name] facet from threads other than the one in
which the corresponding CUDA context was initialized will fail. For
now, the easy way out is to debug the code with CUDA disabled (see
*CUDA-ENABLED*).
Another thing that tends to come up is figuring out where memory is
used.
- [function] MAT-ROOM &KEY (STREAM \*STANDARD-OUTPUT\*) (VERBOSE T)
Calls FOREIGN-ROOM and CUDA-ROOM.
- [macro] WITH-MAT-COUNTERS (&KEY COUNT N-BYTES) &BODY BODY
Count all MAT allocations and also the number of bytes they may
require. *May require* here really means an upper bound,
because `(MAKE-MAT (EXPT 2 60))` doesn't actually uses memory until
one of its facets is accessed (don't simply evaluate it though,
printing the result will access the [ARRAY][facet-name] facet if
*PRINT-MAT*). Also, while facets today all require the same number
of bytes, this may change in the future. This is a debugging tool,
don't use it in production.
```common-lisp
(with-mat-counters (:count count :n-bytes n-bytes)
(assert (= count 0))
(assert (= n-bytes 0))
(make-mat '(2 3) :ctype :double)
(assert (= count 1))
(assert (= n-bytes (* 2 3 8)))
(with-mat-counters (:n-bytes n-bytes-1 :count count-1)
(make-mat '7 :ctype :float)
(assert (= count-1 1))
(assert (= n-bytes-1 (* 7 4))))
(assert (= n-bytes (+ (* 2 3 8) (* 7 4))))
(assert (= count 2)))
```
## Facet API
### Facets
A MAT is a CUBE (see @CUBE-MANUAL) whose facets are different
representations of numeric arrays. These facets can be accessed with
WITH-FACETS with one of the following [FACET-NAME][locative]
locatives:
- [facet-name] BACKING-ARRAY
The corresponding facet's value is a one dimensional lisp array or
a static vector that also looks exactly like a lisp array but is
allocated in foreign memory. See *FOREIGN-ARRAY-STRATEGY*.
- [facet-name] ARRAY
Same as BACKING-ARRAY if the matrix is one-dimensional, all
elements are visible (see @MAT-SHAPING), else it's a lisp array
displaced to the backing array.
- [facet-name] FOREIGN-ARRAY
The facet's value is a [FOREIGN-ARRAY][class] which is an
OFFSET-POINTER wrapping a CFFI pointer. See
*FOREIGN-ARRAY-STRATEGY*.
- [facet-name] CUDA-HOST-ARRAY
This facet's value is a basically the same as that of
[FOREIGN-ARRAY][facet-name]. In fact, they share storage. The
difference is that accessing [CUDA-HOST-ARRAY][facet-name] ensures
that the foreign memory region is page-locked and registered with
the CUDA Driver API function cuMemHostRegister(). Copying between
GPU memory ([CUDA-ARRAY][facet-name]) and registered memory is
significantly faster than with non-registered memory and also allows
overlapping copying with computation. See
WITH-SYNCING-CUDA-FACETS.
- [facet-name] CUDA-ARRAY
The facet's value is a CUDA-ARRAY, which is an OFFSET-POINTER
wrapping a CL-CUDA.DRIVER-API:CU-DEVICE-PTR, allocated with
CL-CUDA.DRIVER-API:CU-MEM-ALLOC and freed automatically.
Facets bound by with WITH-FACETS are to be treated as dynamic
extent: it is not allowed to keep a reference to them beyond the
dynamic scope of WITH-FACETS.
For example, to implement the FILL! operation using only the
BACKING-ARRAY, one could do this:
```commonlisp
(let ((displacement (mat-displacement x))
(size (mat-size x)))
(with-facets ((x* (x 'backing-array :direction :output)))
(fill x* 1 :start displacement :end (+ displacement size))))
```
DIRECTION is :OUTPUT because we clobber all values in `X`. Armed
with this knowledge about the direction, WITH-FACETS will not copy
data from another facet if the backing array is not up-to-date.
To transpose a 2d matrix with the [ARRAY][facet-name] facet:
```commonlisp
(destructuring-bind (n-rows n-columns) (mat-dimensions x)
(with-facets ((x* (x 'array :direction :io)))
(dotimes (row n-rows)
(dotimes (column n-columns)
(setf (aref x* row column) (aref x* column row))))))
```
Note that DIRECTION is :IO, because we need the data in this facet
to be up-to-date (that's the input part) and we are invalidating all
other facets by changing values (that's the output part).
To sum the values of a matrix using the [FOREIGN-ARRAY][facet-name]
facet:
```commonlisp
(let ((sum 0))
(with-facets ((x* (x 'foreign-array :direction :input)))
(let ((pointer (offset-pointer x*)))
(loop for index below (mat-size x)
do (incf sum (cffi:mem-aref pointer (mat-ctype x) index)))))
sum)
```
See DIRECTION for a complete description of :INPUT, :OUTPUT and :IO.
For MAT objects, that needs to be refined. If a MAT is reshaped
and/or displaced in a way that not all elements are visible then
those elements are always kept intact and copied around. This is
accomplished by turning :OUTPUT into :IO automatically on such MATs.
We have finished our introduction to the various facets. It must be
said though that one can do anything without ever accessing a facet
directly or even being aware of them as most operations on `MAT`s
take care of choosing the most appropriate facet behind the scenes.
In particular, most operations automatically use CUDA, if available
and initialized. See WITH-CUDA\* for detail.
### Foreign arrays
One facet of MAT objects is [FOREIGN-ARRAY][facet-name] which is
backed by a memory area that can be a pinned lisp array or is
allocated in foreign memory depending on *FOREIGN-ARRAY-STRATEGY*.
- [class] FOREIGN-ARRAY
[FOREIGN-ARRAY][class] wraps a foreign pointer (in
the sense of CFFI:POINTERP). That is, both OFFSET-POINTER and
BASE-POINTER return a foreign pointer. There are no other public
operations that work with [FOREIGN-ARRAY][class] objects, their sole
purpose is represent facets of MAT objects.
- [variable] *FOREIGN-ARRAY-STRATEGY* "-see below-"
One of :PINNED, :STATIC and :CUDA-HOST (see type
FOREIGN-ARRAY-STRATEGY). This variable controls how foreign arrays
are handled and it can be changed at any time.
If it's :PINNED (only supported if (PINNING-SUPPORTED-P), then no
separate storage is allocated for the foreign array. Instead, it
aliases the lisp array (via the BACKING-ARRAY facet).
If it's :STATIC, then the lisp backing arrays are allocated
statically via the static-vectors library. On some implementations,
explicit freeing of static vectors is necessary, this is taken care
of by finalizers or can be controlled with WITH-FACET-BARRIER.
DESTROY-CUBE and DESTROY-FACET may also be of help.
:CUDA-HOST is the same as :STATIC, but any copies to/from the
GPU (i.e. the [CUDA-ARRAY][facet-name] facet) will be done via the
[CUDA-HOST-ARRAY][facet-name] facet whose memory pages will also be
locked and registered with `cuMemHostRegister` which allows quicker
and asynchronous copying to and from CUDA land.
The default is :PINNED if available, because it's the most
efficient. If pinning is not available, then it's :STATIC.
- [type] FOREIGN-ARRAY-STRATEGY
One of :PINNED, :STATIC and :CUDA-HOST. See
*FOREIGN-ARRAY-STRATEGY* for their semantics.
- [function] PINNING-SUPPORTED-P
Return true iff the lisp implementation efficiently supports
pinning lisp arrays. Pinning ensures that the garbage collector
doesn't move the array in memory. Currently this is only supported on
SBCL gencgc platforms.
- [function] FOREIGN-ROOM &KEY (STREAM \*STANDARD-OUTPUT\*) (VERBOSE T)
Print a summary of foreign memory usage to STREAM. If VERBOSE, make
the output human easily readable, else try to present it in a very
concise way. Sample output with VERBOSE:
```
Foreign memory usage:
foreign arrays: 450 (used bytes: 3,386,295,808)
```
The same data presented with VERBOSE false:
```
f: 450 (3,386,295,808)
```
### CUDA
- [function] CUDA-AVAILABLE-P &KEY (DEVICE-ID 0)
Check that a cuda context is already in initialized in the current
thread or a device with DEVICE-ID is available.
- [macro] WITH-CUDA* (&KEY (ENABLED '\*CUDA-ENABLED\*) (DEVICE-ID '\*CUDA-DEFAULT-DEVICE-ID\*) (RANDOM-SEED '\*CUDA-DEFAULT-RANDOM-SEED\*) (N-RANDOM-STATES '\*CUDA-DEFAULT-N-RANDOM-STATES\*) N-POOL-BYTES) &BODY BODY
Initializes CUDA with with all bells and whistles before BODY and
deinitializes it after. Simply wrapping WITH-CUDA\* around a piece
code is enough to make use of the first available CUDA device or
fall back on blas and lisp kernels if there is none.
If CUDA is already initialized, then it sets up a facet barrier
which destroys CUDA-ARRAY and CUDA-HOST-ARRAY facets after ensuring
that the [ARRAY][facet-name] facet is up-to-date.
Else, if CUDA is available and ENABLED, then in addition to the
facet barrier, a CUDA context is set up, *N-MEMCPY-HOST-TO-DEVICE*,
*N-MEMCPY-DEVICE-TO-HOST* are bound to zero, a cublas handle
created, and *CURAND-STATE* is bound to a CURAND-XORWOW-STATE with
N-RANDOM-STATES, seeded with RANDOM-SEED, and allocation of device
memory is limited to N-POOL-BYTES (NIL means no limit, see
@MAT-CUDA-MEMORY-MANAGEMENT).
Else - that is, if CUDA is not available, BODY is simply executed.
- [function] CALL-WITH-CUDA FN &KEY ((:ENABLED \*CUDA-ENABLED\*) \*CUDA-ENABLED\*) (DEVICE-ID \*CUDA-DEFAULT-DEVICE-ID\*) (RANDOM-SEED \*CUDA-DEFAULT-RANDOM-SEED\*) (N-RANDOM-STATES \*CUDA-DEFAULT-N-RANDOM-STATES\*) N-POOL-BYTES
Like WITH-CUDA\*, but takes a no argument function instead of the
macro's BODY.
- [variable] *CUDA-ENABLED* T
Set or bind this to false to disable all use of cuda. If this is
done from within WITH-CUDA*, then cuda becomes temporarily disabled.
If this is done from outside WITH-CUDA*, then it changes the default
values of the ENABLED argument of any future [WITH-CUDA\*][]s which
turns off cuda initialization entirely.
- [accessor] CUDA-ENABLED MAT (:CUDA-ENABLED = \*DEFAULT-MAT-CUDA-ENABLED\*)
The control provided by *CUDA-ENABLED* can be too
coarse. This flag provides a per-object mechanism to turn cuda
off. If it is set to NIL, then any operation that pays attention
to this flag will not create or access the CUDA-ARRAY facet.
Implementationally speaking, this is easily accomplished by using
USE-CUDA-P.
- [variable] *DEFAULT-MAT-CUDA-ENABLED* T
The default for [CUDA-ENABLED][(accessor mat)].
- [variable] *N-MEMCPY-HOST-TO-DEVICE* 0
Incremented each time a host to device copy is performed. Bound to
0 by WITH-CUDA\*. Useful for tracking down performance problems.
- [variable] *N-MEMCPY-DEVICE-TO-HOST* 0
Incremented each time a device to host copy is performed. Bound to
0 by WITH-CUDA\*. Useful for tracking down performance problems.
- [variable] *CUDA-DEFAULT-DEVICE-ID* 0
The default value of WITH-CUDA\*'s :DEVICE-ID argument.
- [variable] *CUDA-DEFAULT-RANDOM-SEED* 1234
The default value of WITH-CUDA\*'s :RANDOM-SEED argument.
- [variable] *CUDA-DEFAULT-N-RANDOM-STATES* 4096
The default value of WITH-CUDA\*'s :N-RANDOM-STATES argument.
#### CUDA Memory Management
The GPU (called *device* in CUDA terminology) has its own memory
and it can only perform computation on data in this *device memory*
so there is some copying involved to and from main memory. Efficient
algorithms often allocate device memory up front and minimize the
amount of copying that has to be done by computing as much as
possible on the GPU.
MGL-MAT reduces the cost of device of memory allocations by
maintaining a cache of currently unused allocations from which it
first tries to satisfy allocation requests. The total size of all
the allocated device memory regions (be they in use or currently
unused but cached) is never more than N-POOL-BYTES as specified in
WITH-CUDA\*. N-POOL-BYTES being NIL means no limit.
- [condition] CUDA-OUT-OF-MEMORY STORAGE-CONDITION
If an allocation request cannot be
satisfied (either because of N-POOL-BYTES or physical device memory
limits being reached), then CUDA-OUT-OF-MEMORY is signalled.
- [function] CUDA-ROOM &KEY (STREAM \*STANDARD-OUTPUT\*) (VERBOSE T)
When CUDA is in use (see USE-CUDA-P), print a summary of memory
usage in the current CUDA context to STREAM. If VERBOSE, make the
output human easily readable, else try to present it in a very
concise way. Sample output with VERBOSE:
```
CUDA memory usage:
device arrays: 450 (used bytes: 3,386,295,808, pooled bytes: 1,816,657,920)
host arrays: 14640 (used bytes: 17,380,147,200)
host->device copies: 154,102,488, device->host copies: 117,136,434
```
The same data presented with VERBOSE false:
```
d: 450 (3,386,295,808 + 1,816,657,920), h: 14640 (17,380,147,200)
h->d: 154,102,488, d->h: 117,136,434
```
That's it about reducing the cost allocations. The other important
performance consideration, minimizing the amount copying done, is
very hard to do if the data doesn't fit in device memory which is
often a very limited resource. In this case the next best thing is
to do the copying concurrently with computation.
- [macro] WITH-SYNCING-CUDA-FACETS (MATS-TO-CUDA MATS-TO-CUDA-HOST &KEY (SAFEP '\*SYNCING-CUDA-FACETS-SAFE-P\*)) &BODY BODY
Update CUDA facets in a possibly asynchronous way while BODY
executes. Behind the scenes, a separate CUDA stream is used to copy
between registered host memory and device memory. When
WITH-SYNCING-CUDA-FACETS finishes either by returning normally or by
a performing a non-local-exit the following are true:
- All `MAT`s in MATS-TO-CUDA have an up-to-date
[CUDA-ARRAY][facet-name] facet.
- All `MAT`s in MATS-TO-CUDA-HOST have an up-to-date
[CUDA-HOST-ARRAY][facet-name] facet and no
[CUDA-ARRAY][facet-name].
It is an error if the same matrix appears in both MATS-TO-CUDA and
MATS-TO-CUDA-HOST, but the same matrix may appear any number of
times in one of them.
If SAFEP is true, then the all matrices in either of the two lists
are effectively locked for output until WITH-SYNCING-CUDA-FACETS
finishes. With SAFE NIL, unsafe accesses to facets of these matrices
are not detected, but the whole operation has a bit less overhead.
- [variable] *SYNCING-CUDA-FACETS-SAFE-P* T
The default value of the SAFEP argument of
WITH-SYNCING-CUDA-FACETS.
Also note that often the easiest thing to do is to prevent the use
of CUDA (and consequently the creation of [CUDA-ARRAY][facet-name]
facets, and allocations). This can be done either by binding
*CUDA-ENABLED* to NIL or by setting CUDA-ENABLED to NIL on specific
matrices.
## Writing Extensions
New operations are usually implemented in lisp, CUDA, or by calling
a foreign function in, for instance, BLAS, CUBLAS, CURAND.
### Lisp Extensions
- [macro] DEFINE-LISP-KERNEL (NAME &KEY (CTYPES '(:FLOAT :DOUBLE))) (&REST PARAMS) &BODY BODY
This is very much like DEFINE-CUDA-KERNEL but for normal lisp code.
It knows how to deal with MAT objects and can define the same
function for multiple CTYPES. Example:
```commonlisp
(define-lisp-kernel (lisp-.+!)
((alpha single-float) (x :mat :input) (start-x index) (n index))
(loop for xi of-type index upfrom start-x
below (the! index (+ start-x n))
do (incf (aref x xi) alpha)))
```
Parameters are either of the form `(<NAME> <LISP-TYPE)`
or `(<NAME> :MAT <DIRECTION>)`. In the latter case, the appropriate
CFFI pointer is passed to the kernel. `<DIRECTION>` is passed on to
the WITH-FACET that's used to acquire the foreign array. Note that
the return type is not declared.
Both the signature and the body are written as if for single floats,
but one function is defined for each ctype in CTYPES by transforming
types, constants and code by substituting them with their ctype
equivalents. Currently this means that one needs to write only one
kernel for SINGLE-FLOAT and DOUBLE-FLOAT. All such functions get the
declaration from *DEFAULT-LISP-KERNEL-DECLARATIONS*.
Finally, a dispatcher function with NAME is defined which determines
the ctype of the MAT objects passed for :MAT typed parameters. It's
an error if they are not of the same type. Scalars declared
SINGLE-FLOAT are coerced to that type and the appropriate kernel is
called.
- [variable] *DEFAULT-LISP-KERNEL-DECLARATIONS* ((OPTIMIZE SPEED (SB-C::INSERT-ARRAY-BOUNDS-CHECKS 0)))
These declarations are added automatically to kernel functions.
### CUDA Extensions
- [function] USE-CUDA-P &REST MATS
Return true if cuda is enabled (*CUDA-ENABLED*), it's initialized
and all MATS have [CUDA-ENABLED][(accessor mat)]. Operations of
matrices use this to decide whether to go for the CUDA
implementation or BLAS/Lisp. It's provided for implementing new
operations.
- [function] CHOOSE-1D-BLOCK-AND-GRID N MAX-N-WARPS-PER-BLOCK
Return two values, one suitable as the :BLOCK-DIM, the other as
the :GRID-DIM argument for a cuda kernel call where both are
one-dimensional (only the first element may be different from 1).
The number of threads in a block is a multiple of *CUDA-WARP-SIZE*.
The number of blocks is between 1 and and *CUDA-MAX-N-BLOCKS*. This
means that the kernel must be able handle any number of elements in
each thread. For example, a strided kernel that adds a constant to
each element of a length N vector looks like this:
```
(let ((stride (* block-dim-x grid-dim-x)))
(do ((i (+ (* block-dim-x block-idx-x) thread-idx-x)
(+ i stride)))
((>= i n))
(set (aref x i) (+ (aref x i) alpha))))
```
It is often the most efficient to have MAX-N-WARPS-PER-BLOCK around
4. Note that the maximum number of threads per block is limited by
hardware (512 for compute capability < 2.0, 1024 for later
versions), so *CUDA-MAX-N-BLOCKS* times MAX-N-WARPS-PER-BLOCK must
not exceed that limit.
- [function] CHOOSE-2D-BLOCK-AND-GRID DIMENSIONS MAX-N-WARPS-PER-BLOCK
Return two values, one suitable as the :BLOCK-DIM, the other as
the :GRID-DIM argument for a cuda kernel call where both are
two-dimensional (only the first two elements may be different from
1).
The number of threads in a block is a multiple of *CUDA-WARP-SIZE*.
The number of blocks is between 1 and and *CUDA-MAX-N-BLOCKS*.
Currently - but this may change - the BLOCK-DIM-X is always
*CUDA-WARP-SIZE* and GRID-DIM-X is always 1.
This means that the kernel must be able handle any number of
elements in each thread. For example, a strided kernel that adds a
constant to each element of a HEIGHT\*WIDTH matrix looks like this:
```
(let ((id-x (+ (* block-dim-x block-idx-x) thread-idx-x))
(id-y (+ (* block-dim-y block-idx-y) thread-idx-y))
(stride-x (* block-dim-x grid-dim-x))
(stride-y (* block-dim-y grid-dim-y)))
(do ((row id-y (+ row stride-y)))
((>= row height))
(let ((i (* row width)))
(do ((column id-x (+ column stride-x)))
((>= column width))
(set (aref x i) (+ (aref x i) alpha))
(incf i stride-x)))))
```
- [function] CHOOSE-3D-BLOCK-AND-GRID DIMENSIONS MAX-N-WARPS-PER-BLOCK
Return two values, one suitable as the :BLOCK-DIM, the other as
the :GRID-DIM argument for a cuda kernel call where both are
two-dimensional (only the first two elements may be different from
1).
The number of threads in a block is a multiple of *CUDA-WARP-SIZE*.
The number of blocks is between 1 and and *CUDA-MAX-N-BLOCKS*.
Currently - but this may change - the BLOCK-DIM-X is always
*CUDA-WARP-SIZE* and GRID-DIM-X is always 1.
This means that the kernel must be able handle any number of
elements in each thread. For example, a strided kernel that adds a
constant to each element of a THICKNESS \* HEIGHT \* WIDTH 3d array
looks like this:
```
(let ((id-x (+ (* block-dim-x block-idx-x) thread-idx-x))
(id-y (+ (* block-dim-y block-idx-y) thread-idx-y))
(id-z (+ (* block-dim-z block-idx-z) thread-idx-z))
(stride-x (* block-dim-x grid-dim-x))
(stride-y (* block-dim-y grid-dim-y))
(stride-z (* block-dim-z grid-dim-z)))
(do ((plane id-z (+ plane stride-z)))
((>= plane thickness))
(do ((row id-y (+ row stride-y)))
((>= row height))
(let ((i (* (+ (* plane height) row)
width)))
(do ((column id-x (+ column stride-x)))
((>= column width))
(set (aref x i) (+ (aref x i) alpha))
(incf i stride-x))))))
```
- [macro] DEFINE-CUDA-KERNEL (NAME &KEY (CTYPES '(:FLOAT :DOUBLE))) (RETURN-TYPE PARAMS) &BODY BODY
This is an extended CL-CUDA:DEFKERNEL macro. It knows how to deal
with MAT objects and can define the same function for multiple
CTYPES. Example:
```commonlisp
(define-cuda-kernel (cuda-.+!)
(void ((alpha float) (x :mat :input) (n int)))
(let ((stride (* block-dim-x grid-dim-x)))
(do ((i (+ (* block-dim-x block-idx-x) thread-idx-x)
(+ i stride)))
((>= i n))
(set (aref x i) (+ (aref x i) alpha)))))
```
The signature looks pretty much like in CL-CUDA:DEFKERNEL, but
parameters can take the form of `(<NAME> :MAT <DIRECTION>)` too, in
which case the appropriate CL-CUDA.DRIVER-API:CU-DEVICE-PTR is
passed to the kernel. `<DIRECTION>` is passed on to the WITH-FACET
that's used to acquire the cuda array.
Both the signature and the body are written as if for single floats,
but one function is defined for each ctype in CTYPES by transforming
types, constants and code by substituting them with their ctype
equivalents. Currently this means that one needs to write only one
kernel for FLOAT and DOUBLE.
Finally, a dispatcher function with NAME is defined which determines
the ctype of the MAT objects passed for :MAT typed parameters. It's
an error if they are not of the same type. Scalars declared
FLOAT are coerced to that type and the appropriate
kernel is called.
#### CUBLAS
In a WITH-CUDA\* @MAT-BLAS will automatically use CUBLAS. No need to
use these at all.
- [condition] CUBLAS-ERROR ERROR
- [reader] CUBLAS-ERROR-FUNCTION-NAME CUBLAS-ERROR (:FUNCTION-NAME)
- [reader] CUBLAS-ERROR-STATUS CUBLAS-ERROR (:STATUS)
- [variable] *CUBLAS-HANDLE*
- [function] CUBLAS-CREATE HANDLE
- [function] CUBLAS-DESTROY &KEY (HANDLE \*CUBLAS-HANDLE\*)
- [macro] WITH-CUBLAS-HANDLE NIL &BODY BODY
- [function] CUBLAS-GET-VERSION VERSION &KEY (HANDLE \*CUBLAS-HANDLE\*)
#### CURAND
This the low level CURAND API. You probably want @MAT-RANDOM
instead.
- [macro] WITH-CURAND-STATE (STATE) &BODY BODY
- [variable] *CURAND-STATE*
- [class] CURAND-XORWOW-STATE
- [reader] N-STATES CURAND-XORWOW-STATE (:N-STATES)
- [reader] STATES CURAND-XORWOW-STATE (:STATES)
# Cube Manual
###### \[in package MGL-CUBE\]
## Links
Here is the [official
repository](https://github.com/melisgl/mgl-mat) and the [HTML
documentation](http://melisgl.github.io/mgl-mat/cube-manual.html)
for the latest version.
## Introduction
This is the library on which MGL-MAT (see MGL-MAT:@MAT-MANUAL) is
built. The idea of automatically translating between various
representations may be useful for other applications, so this got
its own package and all ties to MGL-MAT has been severed.
This package defines CUBE, an abstract base class that provides a
framework for automatic conversion between various representations
of the same data. To define a cube, CUBE needs to be subclassed and
the @CUBE-FACET-EXTENSION-API be implemented.
If you are only interested in how to use cubes in general, read
@CUBE-BASICS, @CUBE-LIFETIME and @CUBE-FACET-BARRIER.
If you want to implement a new cube datatype, then see @CUBE-FACETS,
@CUBE-FACET-EXTENSION-API, and @CUBE-DEFAULT-CALL-WITH-FACET\*.
## Basics
Here we learn what a CUBE is and how to access the data in it with
WITH-FACET.
- [class] CUBE
A datacube that has various representations of the
same stuff. These representations go by the name \`facet'. Clients
must use WITH-FACET to acquire a dynamic extent reference to a
facet. With the information provided in the DIRECTION argument of
WITH-FACET, the cube keeps track of which facets are up-to-date and
copies data between them as necessary.
The cube is an abstract class, it does not provide useful behavior
in itself. One must subclass it and implement the
@CUBE-FACET-EXTENSION-API.
Also see @CUBE-LIFETIME and @CUBE-FACET-BARRIER.
- [macro] WITH-FACET (VAR (CUBE FACET-NAME &KEY (DIRECTION :IO) TYPE)) &BODY BODY
Find or create the facet with FACET-NAME in CUBE and bind VAR to
the representation of CUBE's data provided by that facet. This
representation is called the facet's *value*. The value is to be
treated as dynamic extent: it is not allowed to keep a reference to
it. For the description of the DIRECTION parameter, see the type
DIRECTION.
If TYPE is specified, then VAR is declared to be of that type.
- [type] DIRECTION
Used by WITH-FACET, DIRECTION can be :INPUT, :OUTPUT or :IO.
- :INPUT promises that the facet will only be read and never
written. Other up-to-date facets of the same cube remain
up-to-date. If the facet in question is not up-to-date then data
is copied to it from one of the up-to-date facets (see
SELECT-COPY-SOURCE-FOR-FACET\*).
- :OUTPUT promises that *all* data will be overwritten without
reading any data. All up-to-date facets become non-up-to-date,
while this facet is marked as up-to-date. No copying of data takes
place.
- :IO promises nothing about the type of access. All up-to-date
facets become non-up-to-date, while this facet is marked as
up-to-date. If the facet in question is not up-to-date then data
is copied to it from one of the up-to-date facets (see
SELECT-COPY-SOURCE-FOR-FACET\*).
Any number of `WITH-FACET`s with direction :INPUT may be active at
the same time, but :IO and :OUTPUT cannot coexists with another
WITH-FACET regardless of the direction. The exception for this rule
is that an inner WITH-FACET does not conflict with an enclosing
WITH-FACET if they are for the same facet (but inner `WITH-FACET`s
for another facet or for the same facet from another thread do).
See CHECK-NO-WRITERS and CHECK-NO-WATCHERS called by
@CUBE-DEFAULT-CALL-WITH-FACET\*.
- [macro] WITH-FACETS (&REST FACET-BINDING-SPECS) &BODY BODY
A shorthand for writing nested WITH-FACET calls.
```
(with-facet (f1 (c1 'name1 :direction :input))
(with-facet (f2 (c2 'name2 :direction :output))
...))
```
is equivalent to:
```
(with-facets ((f1 (c1 'name1 :direction :input))
(f2 (c2 'name2 :direction :output)))
...)
```
## Synchronization
Cubes keep track of which facets are used, which are up-to-date to
be able to perform automatic translation between facets. WITH-FACET
and other operations access and make changes to this metadata so
thread safety is a concern. In this section, we detail how to relax
the default thread safety guarantees.
A related concern is async signal safety which arises most often
when C-c'ing or killing a thread or when the extremely nasty
WITH-TIMEOUT macro is used. In a nutshell, changes to cube metadata
are always made with interrupts disabled so things should be async
signal safe.
- [accessor] SYNCHRONIZATION CUBE (:SYNCHRONIZATION = \*DEFAULT-SYNCHRONIZATION\*)
By default, setup and teardown of facets by
WITH-FACET is performed in a thread safe way. Corrupting internal
data structures of cubes is not fun, but in the name of
performance, synchronization can be turned off either dynamically
or on a per instance basis.
If T, then access to cube metadata is always synchronized. If NIL,
then never. If :MAYBE, then whether access is synchronized is
determined by *MAYBE-SYNCHRONIZE-CUBE* that's true by default.
The default is the value of *DEFAULT-SYNCHRONIZATION*
that's :MAYBE by default.
Note that the body of a WITH-FACET is never synchronized with
anyone, apart from the implicit reader/writer conflict (see
DIRECTION).
- [variable] *DEFAULT-SYNCHRONIZATION* :MAYBE
The default value for SYNCHRONIZATION of new cubes.
- [variable] *MAYBE-SYNCHRONIZE-CUBE* T
Determines whether access the cube metadata is synchronized for
cubes with SYNCHRONIZATION :MAYBE.
## Facets
The basic currency for implementing new cube types is the FACET.
Simply using a cube only involves facet names and values, never
facets themselves.
- [function] FACETS CUBE
Return the facets of CUBE.
- [function] FIND-FACET CUBE FACET-NAME
Return the facet of CUBE for the facet with FACET-NAME or NIL if no
such facet exists.
- [class] FACET STRUCTURE-OBJECT
A cube has facets, as we discussed in @CUBE-BASICS. Facets holds
the data in a particular representation, this is called the *value*
of the facet. A facet holds one such value and some metadata
pertaining to it: its FACET-NAME, whether it's
up-to-date (FACET-UP-TO-DATE-P), etc. FACET objects are never seen
when simply using a cube, they are for implementing the
@CUBE-FACET-EXTENSION-API.
- [structure-accessor] FACET-NAME
A symbol that uniquely identifies the facet within a cube.
- [structure-accessor] FACET-VALUE
This is what's normally exposed by WITH-FACET.
- [structure-accessor] FACET-DESCRIPTION
Returned by MAKE-FACET\* as its second value, this is an
arbitrary object in which additional information can be
stored.
- [structure-accessor] FACET-UP-TO-DATE-P
Whether the cube has changed since this facet has been last
updated. See FACET-UP-TO-DATE-P\*.
- [structure-accessor] FACET-N-WATCHERS
The number of active `WITH-FACET`s. Updated by WATCH-FACET and
UNWATCH-FACET.
- [structure-accessor] FACET-WATCHER-THREADS
The threads (one for each watcher) that have active
`WITH-FACET`s.
- [structure-accessor] FACET-DIRECTION
The direction of the last WITH-FACET on this facet.
## Facet Extension API
Many of the generic functions in this section take FACET arguments.
FACET is a structure and is not intended to be subclassed. To be
able to add specialized methods, the name of the
facet ([FACET-NAME][structure-accessor]) is also passed as the
argument right in front of the corresponding facet argument.
In summary, define EQL specializers on facet name arguments, and use
FACET-DESCRIPTION to associate arbitrary information with facets.
- [generic-function] MAKE-FACET* CUBE FACET-NAME
Called by WITH-FACET (or more directly WATCH-FACET)
when there is no facet with FACET-NAME. As the first value, return a
new object capable of storing CUBE's data in the facet with
FACET-NAME. As the second value, return a facet description which
will be available as FACET-DESCRIPTION. As the third value, return a
generalized boolean indicating whether this facet must be explicitly
destroyed (in which case a finalizer will be added to CUBE).
- [generic-function] DESTROY-FACET* FACET-NAME FACET
Free the resources associated with FACET with
FACET-NAME. The cube this facet belongs to is not among the
parameters because this method can be called from a finalizer on the
cube (so we can't have a reference to the cube portably) which also
means that it may run in an unpredictable thread.
- [generic-function] COPY-FACET* CUBE FROM-FACET-NAME FROM-FACET TO-FACET-NAME TO-FACET
Copy the CUBE's data from FROM-FACET with
FROM-FACET-NAME to TO-FACET with TO-FACET-NAME. Called by
WITH-FACET (or more directly WATCH-FACET) when necessary. FROM-FACET
is what SELECT-COPY-SOURCE-FOR-FACET\* returned.
- [generic-function] CALL-WITH-FACET* CUBE FACET-NAME DIRECTION FN
Call FN with an up-to-date FACET-VALUE that belongs
to FACET-NAME of CUBE. WITH-FACET is directly implemented in terms
of this function. See @CUBE-DEFAULT-CALL-WITH-FACET\* for the gory
details.
Specializations will most likely want to call the default
implementation (with CALL-NEXT-METHOD) but with a lambda that
transforms FACET-VALUE before passing it on to FN.
- [generic-function] FACET-UP-TO-DATE-P* CUBE FACET-NAME FACET
Check if FACET with FACET-NAME has been updated
since the latest change to CUBE (that is, since the access to other
facets with DIRECTION of :IO or :OUTPUT). The default method simply
calls FACET-UP-TO-DATE-P on FACET.
One reason to specialize this is when some facets actually share
common storage, so updating one make the other up-to-date as well.
- [generic-function] SELECT-COPY-SOURCE-FOR-FACET* CUBE TO-NAME TO-FACET
Called when TO-FACET with TO-NAME is about to be
updated by copying data from an up-to-date facet. Return the
facet (or its name) from which data shall be copied. Note that if
the returned facet is not FACET-UP-TO-DATE-P*, then it will be
updated first and another SELECT-COPY-SOURCE-FOR-FACET* will take
place, so be careful not to get into endless recursion. The default
method simply returns the first up-to-date facet.
PAX integration follows, don't worry about it if you don't use PAX,
but you really should (see MGL-PAX::@MGL-PAX-MANUAL).
- [locative] FACET-NAME
The FACET-NAME locative is to refer to stuff defined with
DEFINE-FACET-NAME.
- [macro] DEFINE-FACET-NAME SYMBOL LAMBDA-LIST &BODY DOCSTRING
Just a macro to document that SYMBOL refers to a facet name (as in
the [FACET-NAME][locative]). This is totally confusing, so here is
an example of how MGL-MAT (see MGL-MAT:@MAT-MANUAL) documents the
MGL-MAT:BACKING-ARRAY facet:
```commonlisp
(define-facet-name backing-array ()
"The corresponding facet is a one dimensional lisp array.")
```
Which makes it possible to refer to this definition (refer as in
link and `M-.` to) MGL-MAT:BACKING-ARRAY facet-name. See
MGL-PAX:@MGL-PAX-MANUAL for more.
Also see @CUBE-DEFAULT-CALL-WITH-FACET\*.
## The Default Implementation of CALL-WITH-FACET\*
- [method] CALL-WITH-FACET* (CUBE CUBE) FACET-NAME DIRECTION FN
The default implementation of CALL-WITH-FACET\* is defined in terms
of the WATCH-FACET and the UNWATCH-FACET generic functions. These
can be considered part of the @CUBE-FACET-EXTENSION-API.
- [generic-function] WATCH-FACET CUBE FACET-NAME DIRECTION
This is what the default CALL-WITH-FACET\* method,
in terms of which WITH-FACET is implemented, calls first. The
default method takes care of creating facets, copying and tracking
up-to-dateness.
Calls CHECK-NO-WRITERS (unless *LET-INPUT-THROUGH-P*) and
CHECK-NO-WATCHERS (unless *LET-OUTPUT-THROUGH-P*) depending on
DIRECTION to detect situations with a writer being concurrent to
readers/writers because that would screw up the tracking of
up-to-dateness.
The default implementation should suffice most of the time.
MGL-MAT specializes it to override the DIRECTION arg, if
it's :OUTPUT but not all elements are visible due to reshaping, so
that invisible elements are still copied over.
- [generic-function] UNWATCH-FACET CUBE FACET-NAME
This is what the default CALL-WITH-FACET\* method,
in terms of which WITH-FACET is implemented, calls last. The default
method takes care of taking down facets. External resource managers
may want to hook into this to handle unused facets.
- [variable] *LET-INPUT-THROUGH-P* NIL
If true, WITH-FACETS (more precisely, the default implementation of
CALL-WITH-FACET\*) with :DIRECTION :INPUT does not call
CHECK-NO-WRITERS. This knob is intended to be bound locally for
debugging purposes.
- [variable] *LET-OUTPUT-THROUGH-P* NIL
If true, WITH-FACETS (more precisely, the default implementation of
CALL-WITH-FACET\*) with :DIRECTION :IO or :OUTPUT does not call
CHECK-NO-WATCHERS. This knob is intended to be bound locally for
debugging purposes.
- [function] CHECK-NO-WRITERS CUBE FACET-NAME MESSAGE-FORMAT &REST MESSAGE-ARGS
Signal an error if CUBE has facets (with names other than
FACET-NAME) being written (i.e. direction is :IO or :OUTPUT).
- [function] CHECK-NO-WATCHERS CUBE FACET-NAME MESSAGE-FORMAT &REST MESSAGE-ARGS
Signal an error if CUBE has facets (with names other than
FACET-NAME) being regardless of the direction.
## Lifetime
Lifetime management of facets is manual (but facets of garbage
cubes are freed automatically by a finalizer, see MAKE-FACET\*). One
may destroy a single facet or all facets of a cube with
DESTROY-FACET and DESTROY-CUBE, respectively. Also see
@CUBE-FACET-BARRIER.
- [function] DESTROY-FACET CUBE FACET-NAME
Free resources associated with the facet with FACET-NAME and remove
it from FACETS of CUBE.
- [function] DESTROY-CUBE CUBE
Destroy all facets of CUBE with DESTROY-FACET.
In some cases it is useful to declare the intent to use a facet in
the future to prevent its destruction. Hence, every facet has
reference count which starts from 0. The reference count is
incremented and decremented by ADD-FACET-REFERENCE-BY-NAME and
REMOVE-FACET-REFERENCE-BY-NAME, respectively. If it is positive,
then the facet will not be destroyed by explicit DESTROY-FACET and
DESTROY-CUBE calls, but it will still be destroyed by the finalizer
to prevent resource leaks caused by stray references.
- [function] ADD-FACET-REFERENCE-BY-NAME CUBE FACET-NAME
Make sure FACET-NAME exists on CUBE and increment its reference
count. Return the FACET behind FACET-NAME.
- [function] REMOVE-FACET-REFERENCE-BY-NAME CUBE FACET-NAME
Decrement the reference count of the facet with FACET-NAME of CUBE.
It is an error if the facet does not exists or if the reference
count becomes negative.
- [function] REMOVE-FACET-REFERENCE FACET
Decrement the reference count of FACET. It is an error if the facet
is already destroyed or if the reference count becomes negative.
This function has the same purpose as
REMOVE-FACET-REFERENCE-BY-NAME, but by having a single FACET
argument, it's more suited for use in finalizers because it does not
keep the whole CUBE alive.
### Facet Barriers
A facility to control lifetime of facets tied to a dynamic extent.
Also see @CUBE-LIFETIME.
- [macro] WITH-FACET-BARRIER (CUBE-TYPE ENSURES DESTROYS) &BODY BODY
When BODY exits, destroy facets which:
- are of cubes with CUBE-TYPE
- have a facet name among DESTROYS
- were created in the dynamic extent of BODY
Before destroying the facets, it is ensured that facets with names
among ENSURES are up-to-date. WITH-FACET-BARRIERs can be nested, in
case of multiple barriers matching the cube's type and the created
facet's name, the innermost one takes precedence.
The purpose of this macro is twofold. First, it makes it easy to
temporarily work with a certain facet of many cubes without leaving
newly created facets around. Second, it can be used to make sure
that facets whose extent is tied to some dynamic boundary (such as
the thread in which they were created) are destroyed.
- [function] COUNT-BARRED-FACETS FACET-NAME &KEY (TYPE 'CUBE)
Count facets with FACET-NAME of cubes of TYPE which will be
destroyed by a facet barrier.
* * *
###### \[generated by [MGL-PAX](https://github.com/melisgl/mgl-pax)\]