tinyti.js

This is a fork of AmesingFlank's taichi.js which is, in turn, a Javascript version of the Python library Taichi.

This fork:

• Implements mipmap generation and LOD sampling. (mipmaps generated w/ webgpu-spd)
• Removes the "engine" components of taichi.js. (rendering has been moved to tinymarch)
• Adds and fixes various builtin operations.
• Optimizes GPU→CPU readback performance (see below).
• Adds toFloat32Array() and toTypedInt32Array() field methods for zero-copy typed array readbacks.

See the forked repo and site for documentation.

Readback Performance

GPU→CPU field readback (toArray, toArray1D, get, etc.) has been optimized in three ways:

1. Removed redundant GPU sync. The original code called onSubmittedWorkDone() before mapAsync() — both wait for the GPU, so the first was redundant. Removing it eliminates an extra full GPU round-trip on every readback.

2. Eliminated heap-heavy Array.from() conversions. Mapped GPU data is now copied into a single ArrayBuffer with two typed array views (Int32Array + Float32Array) sharing it, instead of creating two separate JS number[] arrays via Array.from().

3. Index-based element extraction. groupElements no longer splits arrays into O(N) sub-arrays via groupByN. For scalar fields (the common case), each element is read with a single typed array index — zero intermediate allocations.

New Methods

For callers who want maximum readback throughput, two new Field methods skip the final Array.from conversion entirely:

Method	Returns	Use case
field.toFloat32Array()	Promise<Float32Array>	Fast readback of f32 scalar/vector/matrix fields
field.toTypedInt32Array()	Promise<Int32Array>	Fast readback of i32 fields

All existing methods (toArray, toArray1D, toInt32Array, get, set, fromArray, etc.) remain backward-compatible.

Batch Readbacks

For applications reading back multiple fields per frame (e.g., position, velocity, and density), you can batch the readbacks to avoid multiple individual GPU submissions and sequential sync waits:

// Batch reads multiple fields concurrently
let [pos, vel, density] = await ti.toArrays([posField, velField, densityField]);

This merges the buffer copy commands into a single GPU command queue submission and executes the mapAsync calls concurrently using Promise.all.