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Fields are global variables provided by Taichi. Currently, it can only be defined before launching any Taichi kernel. Fields can be either sparse or dense. An element of a field can be either a scalar or a vector/matrix. This term is borrowed from mathematics and physics. If you have already known scalar field (e.g., heat field), vector field (e.g., gravitational field) in mathematics and physics, it would be straightforward to understand the fields in Taichi.


Matrices can be used as field elements, so you can have fields with each element being a matrix.

Scalar fields#

A simple example might help you understand scalar fields. Assume you have a rectangular wok on the top of a fire. At each point of the wok, there would be a temperature. The surface of the wok forms a heat field. The width and height of the wok are similar to the shape of the Taichi scalar field. The temperature (0-D scalar) is like the element of the Taichi scalar field. We could use the following field to represent the heat field on the wok:

heat_field = taichi.field(dtype=ti.f32, shape=(width_wok, height_wok))
  • Every global variable is an N-dimensional field.

    • Global scalars are treated as 0-D scalar fields.
  • Fields are always accessed by indices

    • E.g. x[i, j, k] if x is a 3D scalar field.
    • Even when accessing 0-D field x, use x[None] = 0 instead of x = 0. Please always use indexing to access entries in fields. A 0-D field looks like energy = ti.field(dtype=ti.f32, shape=()).
  • Field values are initially zero.

  • Sparse fields are initially inactive.

  • See Scalar fields for more details.

Vector fields#

We are all live in a gravitational field which is a vector field. At each position of the 3D space, there is a gravity force vector. The gravitational field could be represent with:

gravitational_field = taichi.Vector.field(n = 3,dtype=ti.f32,shape=(x,y,z))

x,y,z are the sizes of each dimension of the 3D space respectively. n is the number of elements of the gravity force vector.

Matrix fields#

Field elements can also be matrices. In continuum mechanics, each infinitesimal point in a material exists a strain and a stress tensor. The strain and stress tensor is a 3 by 3 matrix in the 3D space. To represent this tensor field we could use:

strain_tensor_field = taichi.Matrix.field(n = 3,m = 3, dtype=ti.f32, shape=(x,y,z))

x,y,z are the sizes of each dimension of the 3D material respectively. n, m are the dimensions of the strain tensor.

In general case, suppose you have a 128 x 64 field called A, and each element contains a 3 x 2 matrix. To allocate a 128 x 64 matrix field which has a 3 x 2 matrix for each of its entry, use the statement A = ti.Matrix.field(3, 2, dtype=ti.f32, shape=(128, 64)).

  • If you want to get the matrix of grid node i, j, please use mat = A[i, j]. mat is simply a 3 x 2 matrix.
  • To get the element on the first row and second column of that matrix, use mat[0, 1] or A[i, j][0, 1].
  • As you may have noticed, there are two indexing operators [] when you load a matrix element from a global matrix field: the first is for field indexing, the second for matrix indexing.
  • ti.Vector is simply an alias of ti.Matrix.
  • See Matrices for more on matrices.

Matrix size#

For performance reasons matrix operations will be unrolled during the compile stage, therefore we suggest using only small matrices. For example, 2x1, 3x3, 4x4 matrices are fine, yet 32x6 is probably too big as a matrix size.


Due to the unrolling mechanisms, operating on large matrices (e.g. 32x128) can lead to a very long compilation time and low performance.

If you have a dimension that is too large (e.g. 64), it's better to declare a field of size 64. E.g., instead of declaring ti.Matrix.field(64, 32, dtype=ti.f32, shape=(3, 2)), declare ti.Matrix.field(3, 2, dtype=ti.f32, shape=(64, 32)). Try to put large dimensions to fields instead of matrices.