Struct SimdGradientComputer 

pub struct SimdGradientComputer { /* private fields */ }

SIMD gradient computer

Implementations

impl SimdGradientComputer

pub fn new() -> Self

Create a new SIMD gradient computer with auto-detected capability

pub fn with_config(config: SimdConfig) -> Self

Create with custom configuration

pub fn config(&self) -> &SimdConfig

Get current configuration

pub fn capability(&self) -> SimdCapability

Get detected SIMD capability

pub fn detect_capability(&self) -> SimdCapability

Detect SIMD capability

pub fn stats(&self) -> &SimdStats

Get statistics

pub fn reset_stats(&mut self)

Reset statistics

pub fn should_use_simd(&self, tensor_size: usize) -> bool

Check if SIMD should be used for a given tensor size

pub fn is_aligned<T>(&self, data: &[T]) -> bool

Check if data is aligned for SIMD operations

pub fn compute_simd<T>(&mut self, data: &[T]) -> AutogradResult<Vec<T>>

where T: Clone + Copy + Send + Sync,

Compute gradients with SIMD acceleration

This uses SciRS2-Core’s SIMD operations with memory alignment for optimal performance.

pub fn simd_element_wise<T, F>( &mut self, data: &[T], scalar_op: F, ) -> AutogradResult<Vec<T>>

where T: Clone + Copy + Send + Sync, F: Fn(T) -> T,

Apply element-wise SIMD operation

This demonstrates memory-aligned SIMD operations for element-wise gradient computations.

pub fn report_performance(&self) -> String

Report current performance statistics

Trait Implementations

impl Default for SimdGradientComputer

fn default() -> Self

Returns the “default value” for a type.