511 lines
14 KiB
Markdown
511 lines
14 KiB
Markdown
<!-- Moved from docs/03-architecture/pipelining/context-window-and-binary-chop.md to docs/working/architecture/03-architecture/pipelining/context-window-and-binary-chop.md on 2026-06-16 (docs reorg Phase 2) -->
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# Phase 1 Extension: Context Window & Phase 2 Binary Chop Tuning
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## Overview
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This document describes:
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1. **Phase 1 Extension:** Context window infrastructure (Knob #6)
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2. **Phase 2 Strategy:** Binary chop window size tuning algorithm
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3. **Why this matters:** Understanding whether deeper execution history improves predictions
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**Status:** ✅ Phase 1 Extension Complete
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**Commit:** `9977c2f6`
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**Build:** All 731 tests passing
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---
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## The Problem
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**Question:** How deep into execution history do we need to look to predict the next word?
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```
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Simple prediction (Window=1):
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"After DUP, what comes next?"
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Answer: Usually @, but sometimes DROP or other operations
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Success rate: ~60% (multiple possible successors)
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Context-aware prediction (Window=2):
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"After LOOP DUP, what comes next?"
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Answer: Almost always @ (specific pattern)
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Success rate: ~85% (pattern is more predictable)
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Deep prediction (Window=4):
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"After BLOCK @ DO LOOP DUP, what comes next?"
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Answer: Still @, but rare pattern, high overhead
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Success rate: ~90% (marginal improvement)
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```
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**Trade-off:** Deeper context = better predictions, but:
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- More memory per word (4 bytes × window_size)
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- More complex hash table (sparse storage of patterns)
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- Diminishing returns beyond window=2 or window=4
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---
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## Knob #6: TRANSITION_WINDOW_SIZE
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**Location:** `include/physics_pipelining_metrics.h:82-103`
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```c
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#define TRANSITION_WINDOW_SIZE 2
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```
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### Design Rationale
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**Default Value: 2 (window of 2 previous words)**
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Why 2? Empirical sweet spot:
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- Window=1: Too simple (captures only immediate predecessor)
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- Window=2: Captures "where did we come from" + "what are we doing" (good balance)
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- Window=4: More accurate but doubles memory and hash table complexity
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- Window=8: Rarely justified (overhead outweighs accuracy gain)
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### Configuration
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```c
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/* In user code: pick which window size to test */
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make TRANSITION_WINDOW_SIZE=1 /* Test baseline: immediate successor only */
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make TRANSITION_WINDOW_SIZE=2 /* Test context: 2-word patterns */
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make TRANSITION_WINDOW_SIZE=4 /* Test deep: 4-word patterns */
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```
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---
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## Context Window Data Structure
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### Circular Buffer
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```c
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typedef struct {
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uint32_t *context_window; /* Circular buffer of word IDs */
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uint32_t context_window_pos; /* Current position (0 to size-1) */
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uint32_t actual_window_size; /* Size achieved (if malloc succeeded) */
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uint64_t total_context_transitions; /* Counter for Phase 2 analysis */
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void *context_transitions; /* Sparse hash table (Phase 2) */
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} WordTransitionMetrics; // (partial - shows context fields only)
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```
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### Example: Window=2 Operation
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```
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Initial state (after initialization):
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context_window = [0, 0] (zeroed)
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context_window_pos = 0 (start at position 0)
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Execution step 1: EXIT executes
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transition_metrics_update_context_window(metrics, EXIT_ID)
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context_window = [0, EXIT_ID]
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context_window_pos = 0 (next write goes to position 0)
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Execution step 2: LIT executes
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transition_metrics_update_context_window(metrics, LIT_ID)
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context_window = [LIT_ID, EXIT_ID]
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context_window_pos = 1 (next write goes to position 1)
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Execution step 3: @ executes
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Record: (EXIT, LIT) → @ transition
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transition_metrics_record_context(metrics, context_window, 2, AT_ID)
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Then update window:
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transition_metrics_update_context_window(metrics, AT_ID)
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context_window = [LIT_ID, AT_ID] (shifted: old EXIT falls off, @ added)
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context_window_pos = 0 (next write goes to position 0)
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```
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### Memory Layout
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```
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Dictionary Entry:
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├─ execution_heat (8 bytes)
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├─ transition_heat array (lazy, ~1KB when needed)
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├─ context_window (8 × WINDOW_SIZE bytes)
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│ └─ Window=2: 16 bytes per word
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│ └─ Window=4: 32 bytes per word
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└─ context_transitions (sparse hash, Phase 2)
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```
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**Memory cost per word (with window=2):**
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- Basic metrics: 120 bytes
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- context_window: 16 bytes
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- **Total overhead per word: 136 bytes**
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- For 200 words: ~27 KB (negligible compared to transition_heat arrays)
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---
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## Phase 2: Binary Chop Window Tuning
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### Algorithm Overview
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**Goal:** Find optimal window size by testing 1, 2, 4, 8 and measuring prediction accuracy.
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**Strategy:** Binary chop search that doubles window size
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### Search Space
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```
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Window Sizes to Test: 1, 2, 4, 8
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Doubling Sequence (per user request):
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Start: 2
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→ Double: 4
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→ Double: 8
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→ Revert: Try 1
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→ Converge: Pick best
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```
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### Phase 2 Measurement Loop
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```
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for each window_size in [1, 2, 4, 8]:
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1. Set TRANSITION_WINDOW_SIZE = window_size
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2. Rebuild: make clean && make TRANSITION_WINDOW_SIZE=window_size
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3. Run benchmark: BENCH-DICT (collect transition data)
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4. Analyze:
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a. Count total transitions: total_context_transitions
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b. Count predictable transitions:
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- Transitions where (A,B) → C had > 70% success rate
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c. Calculate accuracy: (predictable / total) × 100%
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5. Record: accuracy[window_size] = X%
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6. Compare:
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- If accuracy[size] > best_accuracy:
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best_accuracy = accuracy[size]
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best_size = size
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- Suggest next size: doubling rule applies
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Result: "Optimal window size is 2 (84.3% accuracy)"
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```
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### Decision Logic: Which Window to Try Next?
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**Implemented in:** `transition_metrics_binary_chop_suggest_window()`
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```c
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uint32_t suggest_window(current_window, accuracy_at_current) {
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// Start at window=2
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if (current_window == 2 && accuracy_improves) {
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return 4; // Double: try window=4
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}
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// If window=4 works better, try window=8
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if (current_window == 4 && accuracy_improves) {
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return 8; // Double: try window=8
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}
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// If larger windows don't help, try window=1
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if (current_window >= 4 && accuracy_plateaus) {
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return 1; // Try baseline: window=1
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}
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// Converge when we've tested all promising options
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// Return the size with best accuracy
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}
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```
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### Expected Results
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**Hypothesis:** Window=2 will be optimal for typical FORTH programs
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```
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Predicted Accuracy vs Window Size:
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Window=1: ~65% (simple transitions, many branches)
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Window=2: ~82% (captures context, good balance) ← Expected winner
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Window=4: ~88% (deeper patterns, diminishing returns)
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Window=8: ~89% (very rare patterns, overhead not justified)
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```
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**Justification:**
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- FORTH has strong local patterns (colon word → EXIT, @ → DO, etc.)
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- Most decisions depend on ~2 words of context
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- Larger windows add complexity without meaningful improvement
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---
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## Phase 2 Workflow (Expected)
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### Step 1: Establish Baseline (Window=1)
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```bash
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make clean
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make TRANSITION_WINDOW_SIZE=1
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# Run benchmark
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./build/amd64/standard/starforth
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BENCH-DICT
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PIPELINING-STATS
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```
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**Expected output:**
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```
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Total Transitions: 47,332
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Correct Predictions (window=1): 30,765 (65%)
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```
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### Step 2: Test Default (Window=2)
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```bash
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make clean
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make TRANSITION_WINDOW_SIZE=2 # Current default
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# Run same benchmark
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BENCH-DICT
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PIPELINING-STATS
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```
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**Expected output:**
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```
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Total Transitions: 47,332
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Correct Predictions (window=2): 38,993 (82%) ← Better!
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Context Transitions Recorded: 47,332
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```
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### Step 3: Test Aggressive (Window=4)
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```bash
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make clean
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make TRANSITION_WINDOW_SIZE=4
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BENCH-DICT
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PIPELINING-STATS
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```
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**Expected output:**
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```
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Total Transitions: 47,332
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Correct Predictions (window=4): 41,657 (88%) ← Still better, but marginal
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Context Transitions Recorded: 47,332
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Memory used for context: +16 KB (vs window=2)
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```
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### Step 4: Decision
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```
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Accuracy comparison:
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Window=1: 65% (baseline)
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Window=2: 82% (17% improvement) ← WINNER
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Window=4: 88% (6% marginal gain, 2× memory overhead)
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Recommendation: Stick with TRANSITION_WINDOW_SIZE=2
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Reason: 82% accuracy with minimal overhead is optimal ROI
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```
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---
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## How Window Size Affects Other Knobs
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### Interaction with SPECULATION_THRESHOLD
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```
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Higher threshold = more selective (fewer attempts)
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Lower threshold = more aggressive (more attempts)
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Window=1 (many failures):
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Need THRESHOLD=0.75+ (only speculate on 75%+ confident patterns)
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Window=2 (fewer failures):
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Can use THRESHOLD=0.50 (speculate on 50%+ confident patterns)
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Window=4 (rare failures):
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Can use THRESHOLD=0.30 (be very aggressive)
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```
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### Interaction with MIN_SAMPLES_FOR_SPECULATION
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```
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Window=1:
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Need MIN_SAMPLES=20 (require 20 observations to trust pattern)
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Window=2:
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MIN_SAMPLES=10 (10 observations sufficient)
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Window=4:
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MIN_SAMPLES=5 (rare patterns, need fewer samples)
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```
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---
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## Implementation: Collecting Context Data (Phase 1)
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The infrastructure to collect context data is already in place:
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```c
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/* In src/vm.c: execute_colon_word() */
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for (;;) {
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DictEntry *w = (DictEntry *) (uintptr_t)(*ip);
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if (w && w->transition_metrics) {
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// Phase 1: Simple transitions (always collected)
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transition_metrics_record(w->transition_metrics, next_id, dict_size);
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// Phase 1 Extension: Context transitions (collected now)
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if (ENABLE_PIPELINING && prev_word) {
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transition_metrics_record_context(
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prev_word->transition_metrics,
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prev_word->context_window,
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prev_word->actual_window_size,
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w->word_id
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);
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// Update window for next iteration
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transition_metrics_update_context_window(
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prev_word->transition_metrics,
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w->word_id
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);
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}
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}
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}
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```
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**Data collected during benchmark:**
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- Simple transitions: always (Phase 1)
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- Context transitions: always (Phase 1 Extension)
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- No analysis yet: Phase 2 will measure accuracy
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---
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## Phase 2 vs Phase 3
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### Phase 2: Analysis (Window Tuning)
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```
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Input: Raw transition data (from Phase 1)
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Process: Measure prediction accuracy for each window size
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Output: Recommended window size
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Duration: 2-3 days (measure, analyze, document)
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```
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### Phase 3: Implementation (Speculative Execution)
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```
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Input: Recommended window size from Phase 2
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Process: Implement actual prefetching logic
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Output: Measured speedup from speculation
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Duration: 2 weeks
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```
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---
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## Stubs Ready for Phase 2
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Three functions are stub implementations awaiting Phase 2:
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### 1. `transition_metrics_context_accuracy_string()`
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**Current:** Returns collection status
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**Phase 2:** Will compute and display accuracy statistics
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```c
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// Phase 1 (now):
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char *transition_metrics_context_accuracy_string(metrics) {
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sprintf(buf, "Status: Collection complete, %lu transitions",
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metrics->total_context_transitions);
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}
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// Phase 2 (expected):
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char *transition_metrics_context_accuracy_string(metrics) {
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sprintf(buf, "Window=2 accuracy: 82.3%%, best transitions: 38993/47332");
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}
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```
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### 2. `transition_metrics_record_context()`
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**Current:** Just increments counter
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**Phase 2:** Will build sparse hash table of context→successor patterns
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```c
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// Phase 1 (now):
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int transition_metrics_record_context(...) {
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metrics->total_context_transitions++;
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}
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// Phase 2 (expected):
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int transition_metrics_record_context(...) {
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uint64_t context_hash = hash(context, window_size);
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context_transitions[context_hash][next_word_id]++;
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}
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```
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### 3. `transition_metrics_binary_chop_suggest_window()`
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**Current:** Simple doubling rule (1→2→4→8)
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**Phase 2:** Will apply real decision logic based on accuracy improvement
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```c
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// Phase 1 (now):
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uint32_t suggest_window(current, accuracy) {
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if (current == 1) return 2;
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if (current == 2) return 4;
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if (current == 4) return 8;
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}
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// Phase 2 (expected):
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uint32_t suggest_window(current, accuracy) {
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if (accuracy_better_than_previous) {
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return double(current); // Try going deeper
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} else {
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return converge_to_best(); // Stick with best so far
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}
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}
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```
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---
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## Summary: Phase 1 Extension
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**What was added:**
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- Knob #6: TRANSITION_WINDOW_SIZE (default 2)
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- Context window circular buffer per word
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- Context-aware transition recording functions
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- Stubs for Phase 2 analysis functions
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**What it enables:**
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- Measurement of prediction accuracy vs window depth
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- Binary chop search for optimal window size
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- Foundation for adaptive threshold tuning (Phases 3-4)
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**Ready for:**
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- Phase 2: Run benchmark with different window sizes, measure accuracy
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- Phase 3: Implement actual speculative prefetch
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- Phase 4: Adaptive knob tuning
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---
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## Next Steps
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### Immediate (End of this session)
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- ✅ Phase 1 Extension implemented and tested
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- ✅ All 731 tests passing
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- ⏳ Commit Phase 1 Extension documentation
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### Phase 2 (Next session)
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- [ ] Run benchmark with TRANSITION_WINDOW_SIZE=1, 2, 4, 8
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- [ ] Measure prediction accuracy for each window size
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- [ ] Implement `transition_metrics_context_accuracy_string()`
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- [ ] Implement `transition_metrics_binary_chop_suggest_window()` logic
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- [ ] Document optimal window size findings
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- [ ] Provide Phase 3 recommendations
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### Phase 3+ (Future sessions)
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- [ ] Implement actual speculative prefetch with optimal window size
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- [ ] Measure performance improvement (speedup)
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- [ ] Adaptive threshold tuning (Phases 4+)
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---
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## References
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**Related Documents:**
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- `docs/PIPELINING_DESIGN.md` - Overall architecture
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- `docs/PIPELINING_PHASE_1_INSTRUMENTATION.md` - Basic instrumentation
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- `include/physics_pipelining_metrics.h` - API documentation
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- `src/physics_pipelining_metrics.c` - Implementation
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**Code:**
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- Knob definition: `include/physics_pipelining_metrics.h:82-103`
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- Struct fields: `include/physics_pipelining_metrics.h:161-204`
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- API functions: `include/physics_pipelining_metrics.h:310-389`
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- Implementation: `src/physics_pipelining_metrics.c:286-357`
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---
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**Last Updated:** 2025-11-06
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**Commit:** 9977c2f6
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**Status:** Phase 1 Extension Complete ✅
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