Glossary
Abstract syntax tree (AST)
https://en.wikipedia.org/wiki/Abstract_syntax_tree
Ahead-of-time (AOT)
https://en.wikipedia.org/wiki/Ahead-of-time_compilation
Aliasing
https://en.wikipedia.org/wiki/Aliasing_(computing)
Annotation
https://docs.python.org/3/glossary.html
Array of structures (AOS)
https://en.wikipedia.org/wiki/AoS_and_SoA
See also structure of arrays.
Assert statement
https://docs.python.org/3/reference/simple_stmts.html#the-assert-statement
Atomic operation
https://wiki.osdev.org/Atomic_operation
Augmented assignment
https://en.wikipedia.org/wiki/Augmented_assignment
Automatic differentiation
https://en.wikipedia.org/wiki/Automatic_differentiation
Bitmask
https://en.wikipedia.org/wiki/Mask_(computing)
Column-major order
https://en.wikipedia.org/wiki/Row-_and_column-major_order
See also row-major order.
Compound type
A compound type is a user-defined array-like or struct-like data type which comprises multiple members of primitive types or other compound types. Supported compound types in Taichi include vectors, metrics, and structs.
Compute shader
https://www.khronos.org/opengl/wiki/Compute_Shader
Coordinate offset
A coordinate offset refers to a value added to another base value, which is an element in a Taichi field.
You can use offsets when defining a field to move the field boundaries.
Data-oriented programming (DOP)
https://en.wikipedia.org/wiki/Data-oriented_design
Data race
https://en.wikipedia.org/wiki/Race_condition#Data_race
Differentiable programming
https://en.wikipedia.org/wiki/Differentiable_programming
Domain-specific language (DSL)
https://en.wikipedia.org/wiki/Domain-specific_language
External array
External arrays refer to data containers available in the Python scope.
Taichi supports interaction with the following external arrays - Numpy arrays, PyTorch tensors, and Paddle tensors.
Field
A field is a multi-dimensional array of elements. The elements it accepts can be a scalar, a vector, a matrix, or a struct. It is a global data container provided by Taichi and can be accessed from both the Python scope and the Taichi scope.
Field shape
The shape of a field is the number of elements in each dimension.
Fragment shader
https://en.wikipedia.org/wiki/Shader#Pixel_shaders
Global variable
https://en.wikipedia.org/wiki/Global_variable
Grid-Stride Loop
https://developer.nvidia.com/blog/cuda-pro-tip-write-flexible-kernels-grid-stride-loops/
Imperative programming
https://en.wikipedia.org/wiki/Imperative_programming
Instantiation
https://en.wikipedia.org/wiki/Instance_(computer_science)
Intermediate representation (IR)
https://en.wikipedia.org/wiki/Intermediate_representation
Just-in-time (JIT) compilation
https://en.wikipedia.org/wiki/Just-in-time_compilation
Kernel
A kernel is a function decorated with @ti.kernel. A kernel serves as the entry point where Taichi begins to take over the tasks, and it must be called directly by Python code.
Lexical-scoped
https://en.wikipedia.org/wiki/Scope_(computer_science)#Lexical_scope
Local variable
https://en.wikipedia.org/wiki/Local_variable
Loop unrolling
https://en.wikipedia.org/wiki/Loop_unrolling
Megakernel
A megakernel is a Taichi kernel that can deal with a large amount of computation to achieve high arithmetic intensity.
Metadata
Metadata refers to the two fundamental attributes of a Taichi field, i.e., data type and shape.
Use field.dtype and field.shape to retrieve the metadata.
Metaprogramming
https://en.wikipedia.org/wiki/Metaprogramming
Object-oriented programming (OOP)
https://en.wikipedia.org/wiki/Object-oriented_programming
Plain old data (POD)
https://en.wikipedia.org/wiki/Passive_data_structure
Primitive type
Primitive data types are commonly-used numerical data types from which all other data types are constructed. Supported primitive data types in Taichi include ti.i32 (int32), ti.u8 (uint8), and ti.f64 (float64)
Python scope
Code outside of the Taichi scope is in the Python scope. The code in the Python scope is native Python and executed by Python's virtual machine, not by Taichi's runtime.
The Python scope corresponds to the host side in CUDA.
Row-major order
https://en.wikipedia.org/wiki/Row-_and_column-major_order
See also coloum-major order.
Shader storage buffer object (SSBO)
https://www.khronos.org/opengl/wiki/Shader_Storage_Buffer_Object
Sparse matrix
A matrix is a two-dimensional data object made of m rows and n columns. If a matrix is comprised of mostly zero values, then it is a sparse matrix.
Taichi provides APIs for sparse matrices.
Static scope
A static scope is a scope of the argument of ti.static, which is a hint for the compiler to evaluate the argument at compile time.
Static single assignment (SSA)
https://en.wikipedia.org/wiki/Static_single-assignment_form
Structure of arrays (SOA)
https://en.wikipedia.org/wiki/AoS_and_SoA
See also array of structures.
Taichi function
A Taichi function is a function decorated with @ti.func.
A Taichi function must be called from inside a kernel or from inside another Taichi function.
Taichi scope
The code inside a kernel or a Taichi function is in the Taichi scope. The code in the Taichi scope is compiled by Taichi's runtime and executed in parallel on CPU or GPU devices for high-performance computation.
The Taichi scope corresponds to the device side in CUDA.
Template signature
Template signatures are what distinguish different instantiations of a kernel template.
For example, The signature of add(x, 42) is (x, ti.i32), which is the same as that of add(x, 1). Therefore, the latter can reuse the previously compiled binary. The signature of add(y, 42) is (y, ti.i32), a different value from the previous signature, hence a new kernel will be instantiated and compiled.
Thread local storage (TLS)
https://en.wikipedia.org/wiki/Thread-local_storage
Traceback
https://en.wikipedia.org/wiki/Stack_trace