SIMD Vectorization
Guide for CPU-based SIMD vectorization using AVX2, AVX512, and ARM NEON instructions.
🚧 Documentation In Progress - SIMD vectorization guide is being developed.
Overview
DotCompute's SIMD backend provides:
- Automatic vectorization with AVX2, AVX512, and NEON
- 3.7x speedup on CPU operations (measured 2.14ms → 0.58ms)
- Hardware detection and fallback paths
- Intrinsic-based optimizations
SIMD Instruction Sets
AVX2 (Advanced Vector Extensions 2)
TODO: Document AVX2:
- 256-bit vector width
- Supported operations
- Hardware compatibility
AVX512 (Advanced Vector Extensions 512)
TODO: Explain AVX512:
- 512-bit vector width
- Supported operations
- Hardware requirements
ARM NEON
TODO: Document ARM NEON:
- Vector extensions for ARM
- Supported operations
- Mobile GPU usage
Vectorization Techniques
Auto-Vectorization
TODO: Explain automatic vectorization:
- Compiler directives
- Loop patterns
- Memory access patterns
Intrinsic-Based Vectorization
TODO: Document SIMD intrinsics:
- Vector types
- Intrinsic functions
- Performance considerations
Vector Operations
Arithmetic Operations
TODO: Document vectorized arithmetic:
- Addition, subtraction, multiplication
- Division and modulo
- Floating-point operations
Logical Operations
TODO: Explain bitwise and logical operations
Memory Operations
TODO: Cover vectorized memory operations:
- Load operations
- Store operations
- Permutation operations
Performance Optimization
Cache Utilization
TODO: Document cache-friendly vectorization
Register Usage
TODO: Explain register pressure management
Branch Minimization
TODO: Cover branch prediction and SIMD
Hardware Detection
Feature Detection
TODO: Explain SIMD feature detection:
- CPUID usage
- Runtime capability checking
- Fallback strategies
Multi-Code Path Support
TODO: Document runtime selection of SIMD paths
Benchmarking
SIMD Performance Measurement
TODO: Provide benchmarking techniques
Examples
TODO: Provide SIMD vectorization examples