Skip to content

PHASE2_SIMD_SUMMARY.md

Latest commit

 

History

History
139 lines (113 loc) · 4.64 KB

PHASE2_SIMD_SUMMARY.md

File metadata and controls

139 lines (113 loc) · 4.64 KB

Phase 2: SIMD Optimizations for Vision Kernels - Implementation Summary

Overview

Successfully implemented SIMD optimizations (SSE2/AVX2 for x86_64, NEON for aarch64) for the OpenVX vision kernels with parallel processing support via Rayon.

Changes Made

1. Cargo.toml Features

Added feature flags for SIMD support:

[features]
default = []
simd = []
sse2 = ["simd"]
avx2 = ["simd"]
neon = ["simd"]
parallel = ["rayon"]

[dependencies]
rayon = { version = "1.8", optional = true }

2. SIMD Utility Module (src/simd_utils.rs)

  • Platform detection (is_simd_available())
  • SIMD lane constants for 128-bit and 256-bit operations
  • Scalar fallback implementations for all operations

3. x86_64 SIMD Module (src/x86_64_simd.rs)

Implemented SSE2 and AVX2 intrinsics:

  • Arithmetic Operations: add_images_sat, sub_images_sat, weighted_avg
  • Gaussian Filters: gaussian_h3_sse2, gaussian_v3_sse2, gaussian_h3_avx2, gaussian_v3_avx2
  • Box Filter: box_h3_sse2
  • Runtime dispatch: Auto-detects AVX2/SSE2 availability

4. aarch64 NEON Module (src/aarch64_simd.rs)

Implemented NEON intrinsics:

  • Arithmetic Operations: add_images_sat_neon, sub_images_sat_neon, weighted_avg_neon
  • Gaussian Filters: gaussian_h3_neon, gaussian_v3_neon
  • Box Filter: box_h3_neon

5. SIMD-Optimized Filter Module (src/filter_simd.rs)

  • gaussian3x3_simd() - Separable [1,2,1] horizontal + vertical passes
  • gaussian5x5_simd() - Separable [1,4,6,4,1] kernel
  • box3x3_simd() - Moving average optimization
  • sobel3x3_simd() - Gradient computation with SIMD

6. SIMD-Optimized Arithmetic Module (src/arithmetic_simd.rs)

  • add_images_simd() - Saturated addition (16/32 pixels at once)
  • subtract_images_simd() - Saturated subtraction
  • weighted_avg_simd() - Alpha blending with fixed-point arithmetic
  • multiply_images_simd() - Multiplication with scale factor

7. SIMD-Optimized Color Module (src/color_simd.rs)

  • rgb_to_gray_simd() - BT.709 coefficients
  • gray_to_rgb_simd() - Channel replication
  • rgb_to_rgba_simd() / rgba_to_rgb_simd() - Format conversion
  • rgb_to_yuv_simd() - BT.601 YUV conversion

8. Parallel Processing Module (src/parallel.rs)

Rayon-based parallel implementations:

  • gaussian3x3_parallel() - Row-parallel separable convolution
  • gaussian5x5_parallel() - Parallel 5x5 Gaussian
  • box3x3_parallel() - Parallel box filter
  • sobel3x3_parallel() - Parallel gradient computation
  • rgb_to_gray_parallel() - Parallel color conversion
  • add_images_parallel() / subtract_images_parallel() - Parallel arithmetic

9. Benchmark Suite (benches/vision_kernels.rs)

Criterion benchmarks comparing:

  • Scalar vs SIMD implementations
  • SIMD vs Parallel implementations
  • Multiple image sizes: 640x480, 1280x720, 1920x1080
  • Kernels: Gaussian3x3, Box3x3, RGB to Gray, Add, Sobel, Weighted Average

Usage

Build with SIMD

cd ~/.openclaw/workspace/rustVX
cargo build --release --features simd

Build with SIMD + Parallel

cargo build --release --features "simd parallel"

Run Benchmarks

cargo bench --features "simd parallel"

Run Tests

cargo test --features "simd parallel"

Performance Characteristics

SIMD Lane Widths

  • 128-bit (SSE2/NEON): 16 u8, 8 u16/i16, 4 f32 per operation
  • 256-bit (AVX2): 32 u8, 16 u16/i16, 8 f32 per operation

Expected Speedups

  • Arithmetic operations: ~8-16x with SSE2, ~16-32x with AVX2
  • Gaussian 3x3: ~4-8x (separable implementation)
  • Color conversion: ~3-6x (memory-bound)
  • With Rayon: Additional 2-4x on multi-core systems

Separable Filter Optimizations

All separable filters now use:

  1. Horizontal pass first (row-major memory access)
  2. Vertical pass second
  3. Intermediate buffer between passes
  4. SIMD for both passes
  5. Proper edge handling (replicate border)

Module Structure

openvx-vision/src/
├── lib.rs                  # Updated with SIMD modules
├── simd_utils.rs           # SIMD infrastructure & scalar fallbacks
├── x86_64_simd.rs          # x86_64 SSE2/AVX2 implementations
├── aarch64_simd.rs         # ARM NEON implementations
├── filter_simd.rs          # SIMD filter operations
├── arithmetic_simd.rs      # SIMD arithmetic operations
├── color_simd.rs           # SIMD color conversions
├── parallel.rs             # Rayon parallel implementations
└── ... (original modules)

Next Steps (Phase 3)

  • Integrate SIMD into kernel execution paths
  • Add runtime feature detection for automatic dispatch
  • Profile and optimize memory access patterns
  • Consider GPU acceleration via CUDA/OpenCL