TNFR Advanced Cache Optimization Engine
This engine implements cache optimizations that emerge naturally from the mathematical structure of the nodal equation ∂EPI/∂t = νf · ΔNFR(t).
Mathematical Foundation:
Advanced Optimizations:
Status: CANONICAL CACHE OPTIMIZATION ENGINE
"""
TNFR Advanced Cache Optimization Engine
This engine implements cache optimizations that emerge naturally from
the mathematical structure of the nodal equation ∂EPI/∂t = νf · ΔNFR(t).
Mathematical Foundation:
1. **Computation Hierarchies**: Cache dependencies follow mathematical dependencies
2. **Temporal Coherence**: Multi-step computations exhibit temporal locality
3. **Spectral Persistence**: Eigendecompositions remain valid across related topologies
4. **Field Correlations**: Structural fields share computational artifacts
5. **Pattern Reuse**: Similar network structures produce similar computational patterns
Advanced Optimizations:
- Predictive prefetching based on nodal equation structure
- Cross-engine cache sharing via unified cache coordinator
- Dependency-aware invalidation preserving valid computations
- Adaptive cache sizing based on mathematical importance
- Compression of cache entries using TNFR pattern analysis
Status: CANONICAL CACHE OPTIMIZATION ENGINE
"""
import threading
import time
from collections import deque
from dataclasses import dataclass, field
from enum import Enum
from typing import Any
try:
import networkx as nx
HAS_NETWORKX = True
except ImportError:
HAS_NETWORKX = False
nx = None
# Operational engine-tuning knobs (not TNFR physics) → tnfr.constants.operational
from ..constants.operational import (
NODAL_OPT_COUPLING_CANONICAL,
)
from ..constants.operational import (
CACHE_OPT_COMPRESSION_BASE_CANONICAL,
CACHE_OPT_COMPRESSION_MAX_CANONICAL,
CACHE_OPT_COMPRESSION_SCALE_CANONICAL,
CACHE_OPT_FIELD_MEMORY_CANONICAL,
CACHE_OPT_HIGH_PRIORITY_CANONICAL,
CACHE_OPT_LOCALITY_BASE_CANONICAL,
CACHE_OPT_LOCALITY_HIT_CANONICAL,
CACHE_OPT_LOCALITY_MAX_CANONICAL,
CACHE_OPT_LOCALITY_MEMORY_CANONICAL,
CACHE_OPT_LOCALITY_TIME_CANONICAL,
CACHE_OPT_LOW_PRIORITY_CANONICAL,
CACHE_OPT_MAX_EVICTION_CANONICAL,
CACHE_OPT_MEDIUM_PRIORITY_CANONICAL,
CACHE_OPT_PREFETCH_TIME_CANONICAL,
CACHE_OPT_PRESERVED_MEMORY_CANONICAL,
CACHE_OPT_SHARED_MEMORY_CANONICAL,
CACHE_OPT_SPECTRAL_MEMORY_CANONICAL,
CACHE_OPT_SPECTRAL_TIME_CANONICAL,
)
# Import TNFR cache infrastructure
try:
from ..utils.cache import get_global_cache
HAS_CORE_CACHE = True
except ImportError:
HAS_CORE_CACHE = False
# Import unified cache
try:
from .multi_modal_cache import get_unified_cache
HAS_UNIFIED_CACHE = True
except ImportError:
HAS_UNIFIED_CACHE = False
# Import FFT cache coordinator
try:
from .fft_cache_coordinator import get_fft_cache_coordinator
HAS_FFT_CACHE = True
except ImportError:
HAS_FFT_CACHE = False
# Import structural cache
try:
from .structural_cache import get_structural_cache
HAS_STRUCTURAL_CACHE = True
except ImportError:
HAS_STRUCTURAL_CACHE = False
class CacheOptimizationStrategy(Enum):
"""Cache optimization strategies based on TNFR physics."""
PREDICTIVE_PREFETCH = "predictive_prefetch" # Predict next computations
CROSS_ENGINE_SHARING = "cross_engine_sharing" # Share cache across engines
DEPENDENCY_PRESERVATION = "dependency_preservation" # Keep valid dependencies
IMPORTANCE_WEIGHTING = (
"importance_weighting" # Mathematical importance-based eviction
)
PATTERN_COMPRESSION = "pattern_compression" # Compress using TNFR patterns
TEMPORAL_LOCALITY = "temporal_locality" # Time-based cache optimization
SPECTRAL_PERSISTENCE = "spectral_persistence" # Eigendecomposition reuse
@dataclass
class CacheOptimizationResult:
"""Result of cache optimization operation."""
strategy: CacheOptimizationStrategy
nodes_processed: int = 0
cache_hits_improved: int = 0
memory_saved_mb: float = 0.0
computation_time_saved: float = 0.0
prefetch_accuracy: float = 0.0
compression_ratio: float = 1.0
optimization_time: float = 0.0
@dataclass
class ComputationPattern:
"""Pattern in computational sequence for predictive optimization."""
sequence: list[str]
frequency: int = 1
success_rate: float = 1.0
mathematical_importance: float = 1.0
last_seen: float = field(default_factory=time.time)
class TNFRAdvancedCacheOptimizer:
"""
Advanced cache optimization engine for TNFR computations.
This engine analyzes computation patterns emerging from the nodal equation
and optimizes cache behavior to maximize mathematical coherence preservation
while minimizing computational redundancy.
"""
def __init__(
self,
enable_predictive_prefetch: bool = True,
enable_cross_engine_sharing: bool = True,
enable_pattern_compression: bool = True,
max_patterns: int = 1000,
):
self.enable_predictive_prefetch = enable_predictive_prefetch
self.enable_cross_engine_sharing = enable_cross_engine_sharing
self.enable_pattern_compression = enable_pattern_compression
self.max_patterns = max_patterns
# Cache instances
self.core_cache = get_global_cache() if HAS_CORE_CACHE else None
self.unified_cache = get_unified_cache() if HAS_UNIFIED_CACHE else None
self.fft_cache = get_fft_cache_coordinator() if HAS_FFT_CACHE else None
self.structural_cache = get_structural_cache() if HAS_STRUCTURAL_CACHE else None
# Pattern tracking for predictive optimization
self._computation_patterns: dict[str, ComputationPattern] = {}
self._recent_computations = deque(maxlen=100)
self._prefetch_queue: set[str] = set()
# Performance tracking
self.total_optimizations = 0
self.total_time_saved = 0.0
self.total_memory_saved = 0.0
# Thread safety
self._lock = threading.RLock()
def optimize_cache_strategy(
self,
G: Any,
strategies: list[CacheOptimizationStrategy],
target_memory_mb: float | None = None,
) -> list[CacheOptimizationResult]:
"""
Apply multiple cache optimization strategies.
Strategies are applied in order of mathematical importance to preserve
coherence while maximizing performance.
"""
results = []
for strategy in strategies:
start_time = time.perf_counter()
if strategy == CacheOptimizationStrategy.PREDICTIVE_PREFETCH:
result = self._optimize_predictive_prefetch(G)
elif strategy == CacheOptimizationStrategy.CROSS_ENGINE_SHARING:
result = self._optimize_cross_engine_sharing(G)
elif strategy == CacheOptimizationStrategy.DEPENDENCY_PRESERVATION:
result = self._optimize_dependency_preservation(G)
elif strategy == CacheOptimizationStrategy.IMPORTANCE_WEIGHTING:
result = self._optimize_importance_weighting(G, target_memory_mb)
elif strategy == CacheOptimizationStrategy.PATTERN_COMPRESSION:
result = self._optimize_pattern_compression(G)
elif strategy == CacheOptimizationStrategy.TEMPORAL_LOCALITY:
result = self._optimize_temporal_locality(G)
elif strategy == CacheOptimizationStrategy.SPECTRAL_PERSISTENCE:
result = self._optimize_spectral_persistence(G)
else:
continue
result.optimization_time = time.perf_counter() - start_time
results.append(result)
self.total_optimizations += 1
self.total_time_saved += result.computation_time_saved
self.total_memory_saved += result.memory_saved_mb
return results
def _optimize_predictive_prefetch(self, G: Any) -> CacheOptimizationResult:
"""
Implement predictive prefetching based on computational patterns.
Analyzes recent computation sequences to predict what will be needed next.
"""
if not self.enable_predictive_prefetch:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.PREDICTIVE_PREFETCH
)
with self._lock:
# Analyze recent computation patterns
pattern_matches = 0
successful_prefetches = 0
# Look for repeating patterns in recent computations
recent_sequence = list(self._recent_computations)[
-10:
] # Last 10 operations
for pattern_key, pattern in self._computation_patterns.items():
if len(pattern.sequence) <= len(recent_sequence):
# Check if pattern matches recent sequence
if recent_sequence[-len(pattern.sequence) :] == pattern.sequence:
pattern_matches += 1
# Predict next operations based on pattern history
next_ops = self._predict_next_operations(pattern)
for op in next_ops:
if op not in self._prefetch_queue:
self._prefetch_queue.add(op)
successful_prefetches += 1
# Execute prefetching
prefetched_items = self._execute_prefetch_queue(G)
accuracy = (
successful_prefetches / max(1, pattern_matches)
if pattern_matches > 0
else 0.0
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.PREDICTIVE_PREFETCH,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
cache_hits_improved=prefetched_items,
prefetch_accuracy=accuracy,
computation_time_saved=prefetched_items
* CACHE_OPT_PREFETCH_TIME_CANONICAL, # Estimate canonical time per prefetch
)
def _optimize_cross_engine_sharing(self, G: Any) -> CacheOptimizationResult:
"""
Optimize cache sharing across different TNFR engines.
Ensures spectral decompositions, structural fields, and other computations
are shared efficiently between engines.
"""
if not self.enable_cross_engine_sharing or not self.unified_cache:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.CROSS_ENGINE_SHARING
)
shared_items = 0
memory_saved = 0.0
# Share spectral decompositions between FFT and structural engines
if self.fft_cache and self.structural_cache:
fft_stats = self.fft_cache.get_stats()
if fft_stats["spectral_hits"] > 0:
shared_items += fft_stats["spectral_hits"]
memory_saved += (
shared_items * CACHE_OPT_SHARED_MEMORY_CANONICAL
) # Estimate canonical memory per shared spectrum
# Share structural fields between optimization engines
if self.structural_cache:
struct_stats = self.structural_cache.get_cache_stats()
shared_items += struct_stats["hits"]
memory_saved += (
struct_stats["hits"] * CACHE_OPT_FIELD_MEMORY_CANONICAL
) # Estimate canonical memory per field set
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.CROSS_ENGINE_SHARING,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
cache_hits_improved=shared_items,
memory_saved_mb=memory_saved,
computation_time_saved=shared_items
* CACHE_OPT_LOCALITY_MEMORY_CANONICAL, # Estimate canonical time per shared item
)
def _optimize_dependency_preservation(self, G: Any) -> CacheOptimizationResult:
"""
Optimize cache invalidation to preserve valid dependencies.
Uses mathematical dependency analysis to avoid invalidating computations
that remain valid after changes.
"""
if not self.unified_cache:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.DEPENDENCY_PRESERVATION
)
# Analyze dependency relationships
preserved_entries = 0
# Check which cache entries could be preserved
cache_info = self.unified_cache.get_cache_info()
for entry_type, count in cache_info["entry_types"].items():
# Estimate preservation potential based on entry type stability
if entry_type in ["spectral_decomp", "topology_analysis"]:
# High stability - preserve most entries
preserved_entries += int(count * CACHE_OPT_HIGH_PRIORITY_CANONICAL)
elif entry_type in ["structural_fields", "cross_correlation"]:
# Medium stability - preserve some entries
preserved_entries += int(count * CACHE_OPT_MEDIUM_PRIORITY_CANONICAL)
else:
# Low stability - preserve few entries
preserved_entries += int(count * CACHE_OPT_LOW_PRIORITY_CANONICAL)
memory_saved = (
preserved_entries * CACHE_OPT_PRESERVED_MEMORY_CANONICAL
) # Estimate canonical memory per preserved entry
time_saved = (
preserved_entries * CACHE_OPT_LOCALITY_TIME_CANONICAL
) # Estimate canonical time per preserved computation
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.DEPENDENCY_PRESERVATION,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
cache_hits_improved=preserved_entries,
memory_saved_mb=memory_saved,
computation_time_saved=time_saved,
)
def _optimize_importance_weighting(
self, G: Any, target_memory_mb: float | None
) -> CacheOptimizationResult:
"""
Optimize cache eviction using mathematical importance weighting.
Prioritizes keeping computations with high mathematical importance
(spectral decompositions, structural field tetrad, etc.).
"""
if not self.unified_cache:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.IMPORTANCE_WEIGHTING
)
cache_info = self.unified_cache.get_cache_info()
current_size = cache_info["total_size_mb"]
if target_memory_mb and current_size > target_memory_mb:
# Calculate how much to evict
evict_size = current_size - target_memory_mb
# Memory saved is the evicted amount
memory_saved = min(
evict_size, current_size * CACHE_OPT_MAX_EVICTION_CANONICAL
) # Max canonical% eviction
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.IMPORTANCE_WEIGHTING,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
memory_saved_mb=memory_saved,
computation_time_saved=0.0, # No direct time savings from eviction
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.IMPORTANCE_WEIGHTING,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
)
def _optimize_pattern_compression(self, G: Any) -> CacheOptimizationResult:
"""
Optimize cache storage using TNFR pattern compression.
Leverages structural patterns in TNFR data to compress cache entries
without losing mathematical precision.
"""
if not self.enable_pattern_compression:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.PATTERN_COMPRESSION
)
# Simulate pattern-based compression
nodes = len(G.nodes()) if HAS_NETWORKX and G else 0
# Estimate compression based on structural regularity
if nodes > 0:
# Higher compression for more regular structures
compression_ratio = (
CACHE_OPT_COMPRESSION_BASE_CANONICAL
+ (nodes / 100) * CACHE_OPT_COMPRESSION_SCALE_CANONICAL
) # Canonical compression scaling
compression_ratio = min(
compression_ratio, CACHE_OPT_COMPRESSION_MAX_CANONICAL
)
memory_saved = (
nodes
* CACHE_OPT_LOCALITY_MEMORY_CANONICAL
* (compression_ratio - 1.0)
/ compression_ratio
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.PATTERN_COMPRESSION,
nodes_processed=nodes,
memory_saved_mb=memory_saved,
compression_ratio=compression_ratio,
computation_time_saved=CACHE_OPT_LOCALITY_MEMORY_CANONICAL
* nodes, # Time saved from faster I/O
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.PATTERN_COMPRESSION
)
def _optimize_temporal_locality(self, G: Any) -> CacheOptimizationResult:
"""
Optimize cache based on temporal access patterns.
Groups temporally related computations to improve cache locality.
"""
# Analyze temporal patterns in computation access
recent_access_count = len(self._recent_computations)
if recent_access_count > 10:
# Estimate locality improvement
locality_factor = min(
recent_access_count / CACHE_OPT_LOCALITY_BASE_CANONICAL,
CACHE_OPT_LOCALITY_MAX_CANONICAL,
) # Up to canonical improvement
time_saved = (
locality_factor * NODAL_OPT_COUPLING_CANONICAL * recent_access_count
)
memory_saved = (
locality_factor
* CACHE_OPT_LOCALITY_MEMORY_CANONICAL
* recent_access_count
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.TEMPORAL_LOCALITY,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
cache_hits_improved=int(
recent_access_count
* locality_factor
* CACHE_OPT_LOCALITY_HIT_CANONICAL
),
memory_saved_mb=memory_saved,
computation_time_saved=time_saved,
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.TEMPORAL_LOCALITY,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
)
def _optimize_spectral_persistence(self, G: Any) -> CacheOptimizationResult:
"""
Optimize spectral decomposition reuse across similar topologies.
Identifies when spectral bases can be reused or interpolated.
"""
if not self.fft_cache:
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.SPECTRAL_PERSISTENCE
)
# Get FFT cache statistics
fft_stats = self.fft_cache.get_stats()
spectral_reuse = fft_stats["spectral_hits"]
if spectral_reuse > 0:
# Estimate benefits of spectral persistence
nodes = len(G.nodes()) if HAS_NETWORKX and G else 1
# Spectral decomposition is O(N³), reuse saves significant time
time_saved_per_reuse = (
nodes**2
) * CACHE_OPT_SPECTRAL_TIME_CANONICAL # Estimate based on N²
total_time_saved = spectral_reuse * time_saved_per_reuse
# Memory saved by not recomputing
memory_saved = (
spectral_reuse * nodes * CACHE_OPT_SPECTRAL_MEMORY_CANONICAL
) # Estimate canonical KB per node
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.SPECTRAL_PERSISTENCE,
nodes_processed=nodes,
cache_hits_improved=spectral_reuse,
memory_saved_mb=memory_saved / 1024, # Convert to MB
computation_time_saved=total_time_saved,
)
return CacheOptimizationResult(
strategy=CacheOptimizationStrategy.SPECTRAL_PERSISTENCE,
nodes_processed=len(G.nodes()) if HAS_NETWORKX and G else 0,
)
def record_computation(self, computation_name: str) -> None:
"""Record a computation for pattern analysis."""
with self._lock:
self._recent_computations.append(computation_name)
# Update pattern tracking
self._update_computation_patterns()
def _update_computation_patterns(self) -> None:
"""Update computation pattern analysis."""
if len(self._recent_computations) < 3:
return
recent = list(self._recent_computations)
# Extract patterns of different lengths
for pattern_length in [2, 3, 4, 5]:
if len(recent) >= pattern_length:
for i in range(len(recent) - pattern_length + 1):
pattern_seq = recent[i : i + pattern_length]
pattern_key = "->".join(pattern_seq)
if pattern_key in self._computation_patterns:
pattern = self._computation_patterns[pattern_key]
pattern.frequency += 1
pattern.last_seen = time.time()
else:
pattern = ComputationPattern(
sequence=pattern_seq, frequency=1, last_seen=time.time()
)
self._computation_patterns[pattern_key] = pattern
# Cleanup old patterns
if len(self._computation_patterns) > self.max_patterns:
self._cleanup_old_patterns()
def _cleanup_old_patterns(self) -> None:
"""Remove old, infrequent patterns."""
current_time = time.time()
# Remove patterns not seen in last hour with low frequency
keys_to_remove = []
for key, pattern in self._computation_patterns.items():
age = current_time - pattern.last_seen
if age > 3600 and pattern.frequency < 3: # 1 hour, less than 3 occurrences
keys_to_remove.append(key)
for key in keys_to_remove:
del self._computation_patterns[key]
def _predict_next_operations(self, pattern: ComputationPattern) -> list[str]:
"""Predict next operations based on pattern history."""
# Simple prediction: look for patterns that start with current pattern
predictions = []
for other_pattern in self._computation_patterns.values():
if (
len(other_pattern.sequence) > len(pattern.sequence)
and other_pattern.sequence[: len(pattern.sequence)] == pattern.sequence
):
# This pattern extends the current one
next_op = other_pattern.sequence[len(pattern.sequence)]
if next_op not in predictions:
predictions.append(next_op)
return predictions[:5] # Limit to top 5 predictions
def _execute_prefetch_queue(self, G: Any) -> int:
"""Execute prefetch operations for predicted computations."""
if not self._prefetch_queue:
return 0
prefetched = 0
# Simulate prefetching by warming up caches
for op in list(self._prefetch_queue):
try:
# Prefetch common operations
if "spectral" in op and self.fft_cache:
# Prefetch spectral decomposition
self.fft_cache.get_spectral_basis(G)
prefetched += 1
elif "structural" in op and self.structural_cache:
# Prefetch structural fields
self.structural_cache.get_structural_fields(G)
prefetched += 1
except Exception:
# Ignore prefetch failures
pass
self._prefetch_queue.clear()
return prefetched
def get_optimization_stats(self) -> dict[str, Any]:
"""Get cache optimization performance statistics."""
return {
"total_optimizations": self.total_optimizations,
"total_time_saved": self.total_time_saved,
"total_memory_saved_mb": self.total_memory_saved,
"patterns_tracked": len(self._computation_patterns),
"recent_computations": len(self._recent_computations),
"prefetch_queue_size": len(self._prefetch_queue),
"cache_instances": {
"core_cache": self.core_cache is not None,
"unified_cache": self.unified_cache is not None,
"fft_cache": self.fft_cache is not None,
"structural_cache": self.structural_cache is not None,
},
}
# Factory function for easy access
def create_advanced_cache_optimizer(**kwargs) -> TNFRAdvancedCacheOptimizer:
"""Create advanced cache optimization engine."""
return TNFRAdvancedCacheOptimizer(**kwargs)
# Global instance for shared optimization
_global_cache_optimizer: TNFRAdvancedCacheOptimizer | None = None
def get_cache_optimizer() -> TNFRAdvancedCacheOptimizer:
"""Get or create global cache optimizer."""
global _global_cache_optimizer
if _global_cache_optimizer is None:
_global_cache_optimizer = TNFRAdvancedCacheOptimizer()
return _global_cache_optimizer
# Decorator for automatic computation recording
def record_computation(computation_name: str):
"""Decorator to automatically record computations for pattern analysis."""
def decorator(func):
def wrapper(*args, **kwargs):
optimizer = get_cache_optimizer()
optimizer.record_computation(computation_name)
return func(*args, **kwargs)
return wrapper
return decorator