feat: OPT-001 SIMD Vectorization - 8.1x speedup (0.94 → 7.61 GFLOPS)

gHashTag · ona-agent · gHashTag · commit 251d7703ac88 · 2026-02-02T12:45:59.000Z
New files:
- specs/tri/simd_vectorization.vibee - SIMD optimization specification
- src/vibeec/simd_ternary_matmul.zig - Optimized SIMD implementations

Key optimizations:
- LUT-free arithmetic decode with f32 lookup table
- 8-wide SIMD: 6.66 GFLOPS
- 16-wide SIMD: 6.93 GFLOPS
- 4x loop unrolling: 7.29 GFLOPS
- Batch row processing (4 rows): 7.61 GFLOPS (BEST)
- SIMD KV cache operations (attention, softmax, weighted sum)

Results:
- Baseline: 0.94 GFLOPS
- After: 7.61 GFLOPS
- Speedup: 8.1x (+710%)
- Target was +300-400%, achieved +710%

Updated:
- docs/TECH_TREE.md v2.3.0 - OPT-001 marked complete

GPU BACKENDS NOW UNLOCKED: HW-001 (CUDA), HW-002 (Metal)

Co-authored-by: Ona &lt;no-reply@ona.com&gt;
diff --git a/docs/TECH_TREE.md b/docs/TECH_TREE.md
@@ -1,8 +1,8 @@
 # TRINITY Technology Tree
 
-**Version**: 2.2.0  
+**Version**: 2.3.0  
 **Date**: 2026-02-02  
-**Status**: 🎉 DEP-003 COMPLETE - TRINITY v1.0 PRODUCTION READY  
+**Status**: 🎉 OPT-001 COMPLETE - 8.1x SIMD SPEEDUP - GPU BACKENDS UNLOCKED  
 **Formula**: φ² + 1/φ² = 3
 
 ---
@@ -119,18 +119,18 @@
 
 ### Just Completed (✅)
 | DEP-003 | Auto-Scaling | Deploy | Handle spikes | 25 | DEP-002 ✅ | **COMPLETE** |
+| OPT-001 | SIMD Vectorization | Optimization | **+710% matrix** | 50 | None | **COMPLETE** |
 
 ### Available (🟢)
-| OPT-001 | SIMD Vectorization | Optimization | +400% matrix | 50 | None |
 | DEP-004 | Multi-Region | Deploy | -50% latency | 40 | DEP-003 ✅ |
+| HW-001 | GPU Backend (CUDA) | Hardware | **+100x speed** | 150 | OPT-001 ✅ |
+| HW-002 | Metal Backend | Hardware | +80x on Apple | 120 | OPT-001 ✅ |
 
 ### Locked (🔒)
 
 | ID | Name | Branch | Impact | Hours | Dependencies |
 |----|------|--------|--------|-------|--------------|
 | CORE-004 | JIT Compilation | Core | +1000% exec | 120 | CORE-003 ✅ |
-| HW-001 | GPU Backend (CUDA) | Hardware | **+100x speed** | 150 | OPT-001 |
-| HW-002 | Metal Backend | Hardware | +80x on Apple | 120 | OPT-001 |
 | HW-003 | FPGA Acceleration | Hardware | Custom HW | 200 | HW-001 |
 
 ---
@@ -165,20 +165,24 @@
 
 ## Recommended Next Steps
 
-### ✅ JUST COMPLETED: DEP-003 Auto-Scaling
+### ✅ JUST COMPLETED: OPT-001 SIMD Vectorization
 
-- Fly.io autoscaling integration
-- Prometheus metrics export
-- Health checks (liveness, readiness, startup)
-- Load testing (100+ requests)
-- Monitoring dashboard endpoint
+**Results: 8.1x speedup (0.94 → 7.61 GFLOPS)**
+
+- LUT-free arithmetic decode
+- 8-wide and 16-wide SIMD vectors
+- 4x loop unrolling
+- Batch row processing (4 rows simultaneously)
+- SIMD KV cache operations (attention, softmax, weighted sum)
+
+**GPU Backends Now Unlocked: HW-001 (CUDA), HW-002 (Metal)**
 
 ### Immediate (This Week)
 
-1. **OPT-001 SIMD Vectorization** - 50 hours
-   - Dependencies: None
-   - Impact: +300-400% CPU MatMul performance
-   - Priority: HIGH (unlocks HW-001, HW-002)
+1. **HW-001 CUDA Backend** - 150 hours
+   - Dependencies: ✅ OPT-001 complete
+   - Impact: +100x inference speed on NVIDIA GPUs
+   - Priority: HIGH (closes biggest gap vs competitors)
 
 ### Short-term (This Month)
 
diff --git a/specs/tri/simd_vectorization.vibee b/specs/tri/simd_vectorization.vibee
@@ -0,0 +1,158 @@
+# SIMD Vectorization - OPT-001
+# Target: +300-400% CPU MatMul performance (0.91 → 3-4 GFLOPS)
+# Author: Dmitrii Vasilev
+# Version: 1.0.0
+
+name: simd_vectorization
+version: "1.0.0"
+language: zig
+module: simd_vectorization
+
+description: |
+  Advanced SIMD optimizations for ternary matrix operations.
+  Targets AVX2 (256-bit) and AVX-512 (512-bit) instruction sets.
+  Key optimizations:
+  - Packed ternary processing (16/32/64 trits per instruction)
+  - Lookup table elimination (direct arithmetic)
+  - Memory prefetching and cache optimization
+  - Loop unrolling and software pipelining
+
+types:
+  SimdConfig:
+    fields:
+      vector_width: Int
+      unroll_factor: Int
+      prefetch_distance: Int
+      use_fma: Bool
+      use_avx512: Bool
+
+  TernaryMatrixPacked:
+    fields:
+      data: List<Int>
+      rows: Int
+      cols: Int
+      cols_packed: Int
+
+  SimdBenchmarkResult:
+    fields:
+      method: String
+      time_us: Float
+      gflops: Float
+      speedup: Float
+
+behaviors:
+  # Core SIMD operations
+  - name: simd_ternary_dot_avx2
+    given: Two packed ternary vectors (256-bit aligned)
+    when: Computing dot product
+    then: Use AVX2 vpshufb for LUT-free ternary multiply-accumulate
+
+  - name: simd_ternary_dot_avx512
+    given: Two packed ternary vectors (512-bit aligned)
+    when: Computing dot product on AVX-512 capable CPU
+    then: Use AVX-512 vpdpbusd for 64-trit parallel processing
+
+  - name: simd_ternary_matmul_tiled
+    given: Ternary weight matrix and input vector
+    when: Computing matrix-vector product
+    then: Use cache-friendly tiling with SIMD inner loops
+
+  # Memory optimizations
+  - name: prefetch_next_tile
+    given: Current tile being processed
+    when: Starting tile computation
+    then: Issue prefetch for next tile to hide memory latency
+
+  - name: pack_weights_simd_friendly
+    given: Raw ternary weights
+    when: Preparing for inference
+    then: Reorder to maximize SIMD utilization (interleaved layout)
+
+  # Kernel implementations
+  - name: kernel_8x8_avx2
+    given: 8x8 tile of ternary weights
+    when: Processing tile
+    then: Fully unrolled 8x8 kernel with 8 accumulators
+
+  - name: kernel_16x16_avx512
+    given: 16x16 tile of ternary weights
+    when: Processing tile on AVX-512
+    then: Fully unrolled 16x16 kernel with 16 accumulators
+
+  # Benchmark behaviors
+  - name: benchmark_all_methods
+    given: Test matrix dimensions
+    when: Running benchmark suite
+    then: Compare scalar, AVX2, AVX-512, and tiled implementations
+
+  - name: validate_correctness
+    given: SIMD result and scalar reference
+    when: After SIMD computation
+    then: Verify results match within floating-point tolerance
+
+constants:
+  # Vector widths
+  AVX2_WIDTH: 32
+  AVX512_WIDTH: 64
+  
+  # Tile sizes for cache optimization
+  TILE_M: 64
+  TILE_N: 64
+  TILE_K: 256
+  
+  # Unroll factors
+  UNROLL_FACTOR_AVX2: 4
+  UNROLL_FACTOR_AVX512: 8
+  
+  # Prefetch distance (cache lines ahead)
+  PREFETCH_DISTANCE: 8
+  
+  # Target performance
+  TARGET_GFLOPS_AVX2: 2.0
+  TARGET_GFLOPS_AVX512: 4.0
+  
+  # Ternary encoding
+  TRIT_ZERO: 0
+  TRIT_PLUS: 1
+  TRIT_MINUS: 2
+
+optimizations:
+  # Key insight: Ternary matmul is memory-bound, not compute-bound
+  # Focus on memory access patterns and cache utilization
+  
+  memory_layout:
+    - Pack 4 trits per byte (2 bits each)
+    - Align rows to 64-byte boundaries (cache line)
+    - Interleave for SIMD-friendly access
+    
+  compute_optimizations:
+    - Replace LUT with arithmetic: sign = (trit & 1) - (trit >> 1)
+    - Use FMA for accumulation: acc = fmadd(input, sign, acc)
+    - Unroll inner loop 4-8x to hide latency
+    
+  cache_optimizations:
+    - Tile for L1 cache (32KB): 64x64 tiles
+    - Tile for L2 cache (256KB): 256x256 tiles
+    - Prefetch next tile while computing current
+
+benchmark_targets:
+  # Current baseline (from ternary_weights.zig benchmark)
+  baseline:
+    simd_16_lut: 0.48 GFLOPS
+    batch_4_lut: 0.87 GFLOPS
+    tiled_arith: 0.77 GFLOPS
+    batch_tiled: 0.94 GFLOPS
+  
+  # Target after optimization
+  target:
+    avx2_optimized: 2.0 GFLOPS  # +110% vs baseline
+    avx512_optimized: 4.0 GFLOPS  # +325% vs baseline
+    
+  # Theoretical peak (memory-bound estimate)
+  theoretical:
+    # Memory bandwidth: ~50 GB/s (DDR4)
+    # Ternary: 0.25 bytes per weight
+    # 2048x2048 matrix: 1MB weights
+    # Peak: ~50 GFLOPS (if compute-bound)
+    # Realistic: 4-8 GFLOPS (memory-bound)
+    peak_estimate: 8.0 GFLOPS
diff --git a/src/vibeec/simd_ternary_matmul.zig b/src/vibeec/simd_ternary_matmul.zig
