Get started with Taichi Lang

2025-12-09

2 min read time

Applies to Linux

When writing compute-intensive tasks, you can make use of the two decorators @ti.func and @ti.kernel. Functions decorated with @ti.kernel are kernels that serve as the entry points where Taichi’s runtime takes over the tasks, and they must be directly invoked by Python code. Functions decorated with @ti.func are building blocks of kernels and can only be invoked by another Taichi Lang function or a kernel. These decorators instruct Taichi Lang to take over the computation tasks and compile the decorated functions to machine code using just-in-time (JIT) compiler. As a result, calls to these functions are executed on multi-core CPUs or GPUs.

Below you can see how simple it is to use the Taichi Lang @ti.func and @ti.kernel decorators to accelerate Python code. First, you can see the Python code without using Taichi Lang. To enact Taichi Lang in this example, the function inv_square serves as a building block function for the kernel partial_sum.

The example Python code without Taichi Lang:

def inv_square(x):  # A function
    return 1.0 / (x * x)

def partial_sum(n: int) -> float:  # A kernel
    total = 0.0
    for i in range(1, n + 1):
        total += inv_square(n)
    return total

partial_sum(1000)

1. To write this code in Taichi Lang, import and initialize Taichi Lang for code acceleration:

import taichi as ti
ti.init(arch=ti.gpu)

2. Then, you can decorate the building block function and kernel with the @ti.func and @ti.kernel decorators, respectively:

   @ti.func def inv_square(x): # A Taichi function

   return 1.0 / (x * x)

   @ti.kernel def partial_sum(n: int) -> float: # A kernel

   total = 0.0 for i in range(1, n + 1):

   total += inv_square(n)

   return total