[Phase 9] Vectorized Execution - SIMD Optimizations

[ravituringworks]

Phase 9: Query Optimization & Performance

Overview

Implement SIMD (Single Instruction, Multiple Data) optimizations for vector operations and bulk data processing.

Features to Implement

• SIMD-optimized vector distance calculations (<->, <#>, <=>)
• Vectorized batch processing for large datasets
• Parallel column scanning and filtering
• SIMD-optimized aggregation functions (SUM, AVG, COUNT)
• Bulk load and insert optimizations
• Memory-efficient vector processing

Technical Requirements

• Rust SIMD libraries integration (portable_simd, wide)
• Platform-specific optimizations (AVX2, AVX-512, NEON)
• Memory alignment for optimal SIMD performance
• Integration with existing vector types and operations
• Fallback implementations for non-SIMD platforms

Success Criteria

• 2-5x performance improvement for vector operations
• Significantly faster bulk data processing
• Optimized aggregation functions with SIMD
• Efficient memory usage for large datasets
• Cross-platform compatibility with performance gains

Dependencies

• Requires existing vector operations and pgvector support
• Integration with query executor
• Performance benchmarking framework

Estimated Effort

4-5 weeks