Struct ParallelInference 

pub struct ParallelInference { /* private fields */ }

Main parallel inference interface for WASM.

Provides high-level API for parallel compute operations in the browser. Automatically manages worker pool and shared memory.

Implementations

impl ParallelInference

pub async fn new( num_workers: Option<usize>, ) -> Result<ParallelInference, JsValue>

Create a new ParallelInference instance.

Arguments

• num_workers - Number of workers to spawn. If None, uses optimal count.

Returns

A Promise that resolves to ParallelInference instance.

Example (JavaScript)

const inference = await ParallelInference.new(4);

pub async fn matmul( &mut self, a: &[f32], b: &[f32], m: usize, n: usize, k: usize, ) -> Result<Vec<f32>, JsValue>

Perform parallel matrix multiplication.

Computes C = A * B where:

• A is m x k
• B is k x n
• C is m x n

Arguments

• a - Matrix A as flat array (row-major)
• b - Matrix B as flat array (row-major)
• m - Number of rows in A
• n - Number of columns in B
• k - Number of columns in A / rows in B

Returns

Result matrix C as Float32Array

pub async fn parallel_attention( &mut self, q: &[f32], k: &[f32], v: &[f32], num_heads: usize, head_dim: usize, seq_len: usize, ) -> Result<Vec<f32>, JsValue>

Perform parallel multi-head attention.

Computes softmax(Q * K^T / sqrt(d_k)) * V for each attention head.

Arguments

• q - Query tensor (batch_size, num_heads, seq_len, head_dim)
• k - Key tensor (batch_size, num_heads, seq_len, head_dim)
• v - Value tensor (batch_size, num_heads, seq_len, head_dim)
• num_heads - Number of attention heads
• head_dim - Dimension of each head
• seq_len - Sequence length

Returns

Output tensor (batch_size, num_heads, seq_len, head_dim)

pub async fn layer_norm( &mut self, input: &[f32], gamma: &[f32], beta: &[f32], epsilon: f32, ) -> Result<Vec<f32>, JsValue>

Perform parallel layer normalization.

Arguments

• input - Input tensor
• gamma - Scale parameter
• beta - Shift parameter
• epsilon - Small constant for numerical stability

Returns

Normalized tensor

pub fn worker_count(&self) -> usize

Get the number of active workers.

pub fn is_shared_memory_available(&self) -> bool

Check if SharedArrayBuffer is available.

pub fn is_cross_origin_isolated(&self) -> bool

Check if the page is cross-origin isolated.

pub fn is_atomics_available(&self) -> bool

Check if Atomics API is available.

pub fn get_optimal_worker_count() -> usize

Get optimal worker count for the current hardware.

pub fn terminate(&mut self)

Terminate all workers and clean up resources.

pub fn get_stats(&self) -> Result<String, JsValue>

Get statistics about worker pool.

Trait Implementations

impl Drop for ParallelInference

fn drop(&mut self)

Executes the destructor for this type.

fn pin_drop(self: Pin<&mut Self>)

🔬This is a nightly-only experimental API. (pin_ergonomics)

Execute the destructor for this type, but different to Drop::drop, it requires self to be pinned.

impl From<ParallelInference> for JsValue

fn from(value: ParallelInference) -> Self

Converts to this type from the input type.

impl FromWasmAbi for ParallelInference

type Abi = u32

The Wasm ABI type that this converts from when coming back out from the ABI boundary.

unsafe fn from_abi(js: u32) -> Self

Recover a Self from Self::Abi.

impl IntoWasmAbi for ParallelInference

type Abi = u32

The Wasm ABI type that this converts into when crossing the ABI boundary.

fn into_abi(self) -> u32

Convert self into Self::Abi so that it can be sent across the wasm ABI boundary.

impl LongRefFromWasmAbi for ParallelInference

type Abi = u32

Same as RefFromWasmAbi::Abi

type Anchor = RcRef<ParallelInference>

Same as RefFromWasmAbi::Anchor

unsafe fn long_ref_from_abi(js: Self::Abi) -> Self::Anchor

Same as RefFromWasmAbi::ref_from_abi

impl OptionFromWasmAbi for ParallelInference

fn is_none(abi: &Self::Abi) -> bool

Tests whether the argument is a “none” instance. If so it will be deserialized as None, and otherwise it will be passed to FromWasmAbi.

impl OptionIntoWasmAbi for ParallelInference

fn none() -> Self::Abi

Returns an ABI instance indicating “none”, which JS will interpret as the None branch of this option.

impl RefFromWasmAbi for ParallelInference

type Abi = u32

The Wasm ABI type references to Self are recovered from.

type Anchor = RcRef<ParallelInference>

The type that holds the reference to Self for the duration of the invocation of the function that has an &Self parameter. This is required to ensure that the lifetimes don’t persist beyond one function call, and so that they remain anonymous.

unsafe fn ref_from_abi(js: Self::Abi) -> Self::Anchor

Recover a Self::Anchor from Self::Abi.

impl RefMutFromWasmAbi for ParallelInference

type Abi = u32

Same as RefFromWasmAbi::Abi

type Anchor = RcRefMut<ParallelInference>

Same as RefFromWasmAbi::Anchor

unsafe fn ref_mut_from_abi(js: Self::Abi) -> Self::Anchor

Same as RefFromWasmAbi::ref_from_abi

impl SupportsConstructor for ParallelInference

impl SupportsInstanceProperty for ParallelInference

impl SupportsStaticProperty for ParallelInference

impl TryFromJsValue for ParallelInference

fn try_from_js_value(value: JsValue) -> Result<Self, JsValue>

Performs the conversion.

fn try_from_js_value_ref(value: &JsValue) -> Option<Self>

Performs the conversion.

impl VectorFromWasmAbi for ParallelInference

type Abi = <Box<[JsValue]> as FromWasmAbi>::Abi

unsafe fn vector_from_abi(js: Self::Abi) -> Box<[ParallelInference]>

impl VectorIntoWasmAbi for ParallelInference

type Abi = <Box<[JsValue]> as IntoWasmAbi>::Abi

fn vector_into_abi(vector: Box<[ParallelInference]>) -> Self::Abi

impl WasmDescribe for ParallelInference

fn describe()

impl WasmDescribeVector for ParallelInference

fn describe_vector()