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//! Stub implementation for SIMD optimization when target architecture doesn't support x86 SIMD
//!
//! This provides fallback implementations for non-x86 architectures.
use crate::diagnostics::{Error, Result};
use crate::eval::value::Value;
/// Stub SIMD operation types for compatibility
#[derive(Debug, Clone, PartialEq)]
pub enum SimdOperationType {
/// Dense uniform data operations
DenseUniform,
/// Sparse data operations
Sparse,
/// Mixed data type operations
MixedTypes,
/// Streaming data operations
Streaming,
/// Small data operations
Small,
}
/// Stub CPU features for compatibility
#[derive(Debug, Clone)]
pub struct CpuFeatures {
/// AVX-512 Foundation support
pub avx512f: bool,
/// AVX2 support
pub avx2: bool,
/// SSE2 support
pub sse2: bool,
/// Fused multiply-add support
pub fma: bool,
/// ARM NEON support
pub neon: bool,
}
/// Stub aligned buffer for compatibility
#[derive(Debug)]
pub struct AlignedBuffer<T> {
/// Internal data storage
data: Vec<T>,
}
impl<T> AlignedBuffer<T> {
/// Creates a new aligned buffer with the specified capacity
pub fn new(capacity: usize, _alignment: usize) -> Result<Self> {
Ok(AlignedBuffer {
data: Vec::with_capacity(capacity),
})
}
/// Returns the buffer data as a slice
pub fn as_slice(&self) -> &[T] {
&self.data
}
/// Returns the buffer data as a mutable slice
pub fn as_mut_slice(&mut self) -> &mut [T] {
&mut self.data
}
/// Resizes the buffer to the specified length
pub fn resize(&mut self, new_len: usize) -> Result<()>
where
T: Default + Clone,
{
self.data.resize(new_len, T::default());
Ok(())
}
/// Extends the buffer by copying from a source slice
pub fn extend_from_slice(&mut self, source: &[T]) -> Result<()>
where
T: Copy,
{
self.data.extend_from_slice(source);
Ok(())
}
}
unsafe impl<T: Send> Send for AlignedBuffer<T> {}
unsafe impl<T: Sync> Sync for AlignedBuffer<T> {}
/// Stub SIMD numeric operations - all operations fall back to scalar
pub struct SimdNumericOps {
cpu_features: CpuFeatures,
}
impl SimdNumericOps {
/// Creates a new SIMD numeric operations instance
pub fn new() -> Self {
SimdNumericOps {
cpu_features: CpuFeatures {
avx512f: false,
avx2: false,
sse2: false,
fma: false,
neon: cfg!(target_arch = "aarch64"),
},
}
}
/// Creates a new SIMD numeric operations instance with default settings
pub fn with_default() -> Self {
Self::new()
}
/// Analyzes the data to determine the best SIMD operation type
pub fn analyze_operation_type(&self, data: &[f64]) -> SimdOperationType {
if data.len() < 8 {
SimdOperationType::Small
} else {
SimdOperationType::DenseUniform
}
}
/// Adds two f64 arrays element-wise (stub implementation)
pub fn add_f64_arrays(&mut self, a: &[f64], b: &[f64], result: &mut [f64]) -> Result<()> {
if a.len() != b.len() || a.len() != result.len() {
return Err(Box::new(Error::runtime_error(
"Array length mismatch".to_string(),
None,
)));
}
for i in 0..a.len() {
result[i] = a[i] + b[i];
}
Ok(())
}
/// Multiplies two f64 arrays element-wise (stub implementation)
pub fn multiply_f64_arrays(&mut self, a: &[f64], b: &[f64], result: &mut [f64]) -> Result<()> {
if a.len() != b.len() || a.len() != result.len() {
return Err(Box::new(Error::runtime_error(
"Array length mismatch".to_string(),
None,
)));
}
for i in 0..a.len() {
result[i] = a[i] * b[i];
}
Ok(())
}
/// Computes dot product of two f64 arrays (stub implementation)
pub fn dot_product_f64(&mut self, a: &[f64], b: &[f64]) -> Result<f64> {
if a.len() != b.len() {
return Err(Box::new(Error::runtime_error(
"Array length mismatch".to_string(),
None,
)));
}
let result = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
Ok(result)
}
/// Attempts to optimize a Scheme numeric operation (stub implementation)
pub fn optimize_scheme_numeric_operation(&mut self, _op: &str, _args: &[Value]) -> Result<Option<Value>> {
Ok(None) // No optimization available
}
/// Returns performance statistics (stub implementation)
pub fn get_performance_stats(&self) -> &() {
&()
}
/// Resets performance statistics (stub implementation)
pub fn reset_performance_stats(&mut self) {
// No-op
}
/// Optimized addition of two numeric arrays (stub implementation)
pub fn add_numeric_arrays_optimized(&mut self, a: &[f64], b: &[f64]) -> Result<Vec<f64>> {
if a.len() != b.len() {
return Err(Box::new(Error::runtime_error(
"Array length mismatch".to_string(),
None,
)));
}
let result: Vec<f64> = a.iter().zip(b.iter()).map(|(x, y)| x + y).collect();
Ok(result)
}
#[cfg(feature = "simd-benchmarks")]
/// Benchmarks SIMD performance (stub implementation for non-SIMD architectures)
pub fn benchmark_simd_performance(&mut self, size: usize) -> crate::numeric::simd_benchmarks::SimdBenchmarkResults {
// Simple stub implementation
crate::numeric::simd_benchmarks::SimdBenchmarkResults {
operation: "benchmark".to_string(),
array_size: size,
simd_time_ns: 1000,
scalar_time_ns: 1000,
speedup: 1.0,
bandwidth_utilization: 0.5,
}
}
}
impl Default for SimdNumericOps {
fn default() -> Self {
Self::new()
}
}