Difference between revisions of "Dense multidimensional arrays"
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==Multidimensional arrays and ranges== | ==Multidimensional arrays and ranges== | ||
− | + | [https://github.com/libmir/mir-algorithm mir-algorithm] standard library provides multidimensional shell over arrays and ranges. | |
− | It is located in [http://dlang. | + | It is located in [mir.ndslice package http://docs.algorithm.dlang.io/latest/mir_ndslice.html] |
<syntaxhighlight lang=D> | <syntaxhighlight lang=D> | ||
− | import | + | import mir.ndslice; |
− | auto slice = | + | auto slice = slice!int(5, 6, 7); |
assert(slice.length == 5); | assert(slice.length == 5); | ||
assert(slice.elementsCount == 5 * 6 * 7); | assert(slice.elementsCount == 5 * 6 * 7); | ||
− | static assert(is(typeof(slice) == Slice!(3, int*))); | + | static assert(is(typeof(slice) == Slice!(Contiguous, [3], int*))); |
+ | |||
+ | slice[1, 3, 4] = 5; | ||
+ | |||
+ | auto matrix = slice[1]; | ||
+ | matrix = slice.front; // Random Access Range API | ||
+ | |||
+ | auto matrix2 = slice.front!1; // Multidimensional Random Access Range API | ||
+ | |||
</syntaxhighlight> | </syntaxhighlight> | ||
Revision as of 17:48, 13 April 2017
There are several ways of declaring multidimensional arrays in D.
Contents
Multidimensional arrays and ranges
mir-algorithm standard library provides multidimensional shell over arrays and ranges. It is located in [mir.ndslice package http://docs.algorithm.dlang.io/latest/mir_ndslice.html]
import mir.ndslice;
auto slice = slice!int(5, 6, 7);
assert(slice.length == 5);
assert(slice.elementsCount == 5 * 6 * 7);
static assert(is(typeof(slice) == Slice!(Contiguous, [3], int*)));
slice[1, 3, 4] = 5;
auto matrix = slice[1];
matrix = slice.front; // Random Access Range API
auto matrix2 = slice.front!1; // Multidimensional Random Access Range API
Jagged arrays
The simplest way is to use an array of arrays:
int[][] matrix = [
[ 1, 2, 3 ],
[ 4, 5, 6 ],
[ 7, 8, 9 ]
];
assert(matrix[0][0] == 1);
assert(matrix[1][1] == 5);
This creates a so-called jagged array, because each element of the outer array can have different lengths:
int[][] matrix = [
[ 1, 2, 3 ],
[ 4, 5, 6, 7, 8 ], // this is valid
[ 9, 10, 11 ]
];
However, this approach is not so memory-efficient, because the outer array is a separate block of memory containing references to the inner arrays. Array lookups require multiple indirections, so there is a slight performance hit.
Note that with the "jagged" array scheme, the "2nd dimensions" arrays may either all be allocated individually, or simply be slices of a single very big 1D array. Both schemes are valid.
A dynamic rectangular jagged array may be dynamically allocated at once using the multi-dim allocation syntax:
//Allocates a dynamic array containing
// 2 dynamic arrays containing
// 5 ints
int[][] matrix = new int[][](5, 2);
Note that in this example, the dimensions don't need to be known at compile time. Also note that this works for any amount of dimensions.
Static arrays
D recognizes the inefficiency of jagged arrays, so when all the dimensions of the array are known at compile-time, the array is automatically implemented as a dense array: the elements are packed together into a single memory block, and array access requires only a single indexed lookup:
// This is a dense array
int[3][3] matrix = [
[ 1, 2, 3 ],
[ 4, 5, 6 ],
[ 7, 8, 9 ]
];
Dense arrays are fast and memory-efficient. But it requires that all array dimensions be known at compile-time, that is, it must be a static array. But what about dynamic arrays?
Dense dynamic arrays
There is a way to make multidimensional dynamic arrays dense, if only the last dimension needs to be variable, or if the array is just too big to fit on stack:
enum columns = 100;
int rows = 100;
double[columns][] gridInfo = new double[columns][](rows);
This creates a multidimensional dynamic array with dense storage: all the array elements are contiguous in memory.
Credits
The idiom for creating dense multidimensional dynamic arrays was first posted to the D newsgroup by User:Monarchdodra.