TNFR Computational Centralization Hub
This module implements the natural centralization that emerges from the nodal equation: ∂EPI/∂t = νf · ΔNFR(t)
The hub recognizes that all TNFR computations share the same mathematical foundation and can be unified under a single computational infrastructure:
Centralization Principles:
Key Features:
Status: CANONICAL COMPUTATIONAL CENTRALIZATION HUB
"""
TNFR Computational Centralization Hub
This module implements the natural centralization that emerges from the nodal equation:
∂EPI/∂t = νf · ΔNFR(t)
The hub recognizes that all TNFR computations share the same mathematical foundation
and can be unified under a single computational infrastructure:
Centralization Principles:
1. **Single Mathematical Source**: All operations derive from nodal equation
2. **Unified Resource Management**: Shared memory, compute, and cache coordination
3. **Cross-Engine Communication**: Direct data sharing between optimization engines
4. **Intelligent Load Balancing**: Route computations to optimal engines
5. **Emergent Optimization**: System learns and adapts automatically
6. **Hierarchical Coordination**: Multi-scale operation from local to global
Key Features:
- Unified computation dispatch across all engines
- Shared memory pools for cross-engine data transfer
- Intelligent caching with mathematical dependency tracking
- Automatic backend selection (NumPy/JAX/PyTorch/GPU)
- Performance learning and adaptation
- Resource pooling and load balancing
- Mathematical consistency guarantees
Status: CANONICAL COMPUTATIONAL CENTRALIZATION HUB
"""
import threading
import time
import uuid
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass, field
from enum import Enum
from queue import PriorityQueue
from typing import Any, Callable
from ..errors import TNFRValueError
from ..mathematics.unified_numerical import np
try:
import networkx as nx
HAS_NETWORKX = True
except ImportError:
HAS_NETWORKX = False
nx = None
# Import all engines
try:
from .adelic import AdelicDynamics
from .advanced_fft_arithmetic import TNFRAdvancedFFTEngine
from .fft_engine import FFTDynamicsEngine
from .multi_modal_cache import TNFRUnifiedMultiModalCache
from .nodal_optimizer import NodalEquationOptimizer
from .optimization_orchestrator import TNFROptimizationOrchestrator
from .structural_cache import StructuralCoherenceCache
from .unified_backend import (
ComputationType,
TNFRUnifiedBackend,
UnifiedComputationRequest,
)
HAS_ALL_ENGINES = True
except ImportError:
HAS_ALL_ENGINES = False
# Import mathematical backends
try:
from ..mathematics.backend import available_backends, get_backend
HAS_MATH_BACKENDS = True
except ImportError:
HAS_MATH_BACKENDS = False
get_backend = None
available_backends = None
class ComputationPriority(Enum):
"""Priority levels for computation requests."""
CRITICAL = 1 # Real-time operator applications
HIGH = 2 # Interactive computations
NORMAL = 3 # Standard analysis
LOW = 4 # Background optimization
BATCH = 5 # Large batch processing
class EngineType(Enum):
"""Available computational engines."""
UNIFIED_BACKEND = "unified_backend"
OPTIMIZATION_ORCHESTRATOR = "orchestrator"
ADVANCED_FFT = "advanced_fft"
NODAL_OPTIMIZER = "nodal_optimizer"
FFT_ENGINE = "fft_engine"
STRUCTURAL_CACHE = "structural_cache"
ADELIC_DYNAMICS = "adelic_dynamics"
MULTI_MODAL_CACHE = "multi_modal_cache"
@dataclass
class ComputationRequest:
"""Unified computation request."""
request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
engine_type: EngineType = EngineType.UNIFIED_BACKEND
operation: str = "general_computation"
graph: Any | None = None
parameters: dict[str, Any] = field(default_factory=dict)
priority: ComputationPriority = ComputationPriority.NORMAL
callback: Callable | None = None
dependencies: set[str] = field(default_factory=set)
timeout_seconds: float = 300.0
enable_cache: bool = True
require_accuracy: bool = True
@dataclass
class ComputationResult:
"""Unified computation result."""
request_id: str
engine_used: EngineType
operation: str
success: bool
result_data: Any = None
execution_time: float = 0.0
cache_hits: int = 0
cache_misses: int = 0
memory_used_mb: float = 0.0
backend_used: str = "numpy"
accuracy_metrics: dict[str, float] = field(default_factory=dict)
error_message: str | None = None
metadata: dict[str, Any] = field(default_factory=dict)
@dataclass
class SystemResources:
"""System resource status."""
total_memory_mb: float = 0.0
available_memory_mb: float = 0.0
cpu_count: int = 1
gpu_available: bool = False
active_computations: int = 0
cache_utilization: float = 0.0
load_average: float = 0.0
class TNFRComputationalHub:
"""
Centralized computational hub for all TNFR operations.
This hub emerges naturally from recognizing that all TNFR computations
are variations of the same mathematical structure and can benefit from
unified resource management and cross-engine optimization.
"""
def __init__(
self,
max_workers: int = 4,
memory_budget_mb: float = 1024.0,
enable_gpu: bool = True,
cache_size_mb: float = 512.0,
):
self.max_workers = max_workers
self.memory_budget_mb = memory_budget_mb
self.enable_gpu = enable_gpu
self.cache_size_mb = cache_size_mb
# Initialize all engines
self._engines = {}
if HAS_ALL_ENGINES:
self._engines[EngineType.UNIFIED_BACKEND] = TNFRUnifiedBackend()
self._engines[EngineType.OPTIMIZATION_ORCHESTRATOR] = (
TNFROptimizationOrchestrator()
)
self._engines[EngineType.ADVANCED_FFT] = TNFRAdvancedFFTEngine()
self._engines[EngineType.MULTI_MODAL_CACHE] = TNFRUnifiedMultiModalCache(
cache_size_mb
)
self._engines[EngineType.NODAL_OPTIMIZER] = NodalEquationOptimizer()
self._engines[EngineType.FFT_ENGINE] = FFTDynamicsEngine()
self._engines[EngineType.STRUCTURAL_CACHE] = StructuralCoherenceCache()
self._engines[EngineType.ADELIC_DYNAMICS] = AdelicDynamics()
# Computation coordination
self._request_queue = PriorityQueue()
self._result_cache = {}
self._active_requests = {}
# Thread pool for parallel execution
self._executor = ThreadPoolExecutor(max_workers=max_workers)
self._queue_thread = None
self._shutdown = False
# Resource management
self._resource_monitor = SystemResources()
self._load_balancer = {}
# Performance tracking
self._performance_history = []
self._engine_performance = {engine: [] for engine in EngineType}
# Cross-engine shared memory
self._shared_memory_pool = {}
self._memory_locks = {}
# Start background processing
self._start_queue_processor()
def _start_queue_processor(self) -> None:
"""Start background queue processing thread."""
def process_queue():
while not self._shutdown:
try:
if not self._request_queue.empty():
priority, timestamp, request = self._request_queue.get(
timeout=1.0
)
self._process_request_async(request)
except Exception:
continue # Keep processing
self._queue_thread = threading.Thread(target=process_queue, daemon=True)
self._queue_thread.start()
def submit_computation(self, request: ComputationRequest) -> str:
"""
Submit computation request to hub.
Returns request ID for tracking.
"""
# Validate request
if not self._validate_request(request):
raise TNFRValueError(
f"Invalid computation request: {request}",
context={"request": str(request)},
suggestion="Ensure request has valid operation and parameters.",
)
# Add to queue with priority
timestamp = time.time()
self._request_queue.put((request.priority.value, timestamp, request))
self._active_requests[request.request_id] = request
return request.request_id
def get_result(
self, request_id: str, timeout: float | None = None
) -> ComputationResult | None:
"""Get computation result by request ID."""
start_time = time.time()
while True:
# Check if result is ready
if request_id in self._result_cache:
result = self._result_cache.pop(request_id)
self._active_requests.pop(request_id, None)
return result
# Check timeout
if timeout and (time.time() - start_time) > timeout:
return None
# Brief sleep to avoid busy waiting
time.sleep(0.01)
def execute_computation_sync(
self, request: ComputationRequest
) -> ComputationResult:
"""Execute computation synchronously."""
request_id = self.submit_computation(request)
result = self.get_result(request_id, timeout=request.timeout_seconds)
if result is None:
return ComputationResult(
request_id=request_id,
engine_used=request.engine_type,
operation=request.operation,
success=False,
error_message="Computation timed out",
)
return result
def _process_request_async(self, request: ComputationRequest) -> None:
"""Process computation request asynchronously."""
def process():
try:
result = self._execute_computation(request)
self._result_cache[request.request_id] = result
# Call callback if provided
if request.callback:
request.callback(result)
except Exception as e:
error_result = ComputationResult(
request_id=request.request_id,
engine_used=request.engine_type,
operation=request.operation,
success=False,
error_message=str(e),
)
self._result_cache[request.request_id] = error_result
# Submit to thread pool
self._executor.submit(process)
def _execute_computation(self, request: ComputationRequest) -> ComputationResult:
"""Execute single computation request."""
start_time = time.time()
# Select optimal engine for this computation
selected_engine = self._select_optimal_engine(request)
# Route to appropriate engine
try:
if selected_engine == EngineType.UNIFIED_BACKEND:
result_data = self._execute_unified_backend(request)
elif selected_engine == EngineType.OPTIMIZATION_ORCHESTRATOR:
result_data = self._execute_optimization_orchestrator(request)
elif selected_engine == EngineType.ADVANCED_FFT:
result_data = self._execute_advanced_fft(request)
elif selected_engine == EngineType.NODAL_OPTIMIZER:
result_data = self._execute_nodal_optimizer(request)
elif selected_engine == EngineType.FFT_ENGINE:
result_data = self._execute_fft_engine(request)
elif selected_engine == EngineType.ADELIC_DYNAMICS:
result_data = self._execute_adelic_dynamics(request)
else:
raise TNFRValueError(
f"Unknown engine type: {selected_engine}",
context={
"engine": selected_engine,
"available": [e.name for e in EngineType],
},
suggestion="Use a valid EngineType enum value.",
)
except Exception as e:
return ComputationResult(
request_id=request.request_id,
engine_used=selected_engine,
operation=request.operation,
success=False,
error_message=str(e),
)
execution_time = time.perf_counter() - start_time
# Update performance tracking
self._engine_performance[selected_engine].append(execution_time)
return ComputationResult(
request_id=request.request_id,
engine_used=selected_engine,
operation=request.operation,
success=True,
result_data=result_data,
execution_time=execution_time,
)
def _select_optimal_engine(self, request: ComputationRequest) -> EngineType:
"""
Select optimal engine based on request characteristics and system state.
This selection emerges from mathematical analysis of the computation type.
"""
# Use specified engine if available and suitable
if request.engine_type in self._engines:
return request.engine_type
# Intelligent selection based on operation and graph properties
if request.graph and HAS_NETWORKX:
num_nodes = len(request.graph.nodes())
# Large graphs benefit from specialized FFT engines
if num_nodes > 100:
if request.operation in ["spectral_analysis", "harmonic_analysis"]:
return EngineType.ADVANCED_FFT
elif request.operation in ["temporal_evolution", "multi_step"]:
return EngineType.FFT_ENGINE
# Medium graphs good for nodal optimization
elif 20 <= num_nodes <= 100:
if request.operation in ["nodal_evolution", "operator_sequence"]:
return EngineType.NODAL_OPTIMIZER
# Default to optimization orchestrator for intelligent routing
if EngineType.OPTIMIZATION_ORCHESTRATOR in self._engines:
return EngineType.OPTIMIZATION_ORCHESTRATOR
# Fallback to unified backend
return EngineType.UNIFIED_BACKEND
def _execute_unified_backend(self, request: ComputationRequest) -> Any:
"""Execute using unified backend."""
engine = self._engines[EngineType.UNIFIED_BACKEND]
# Map to unified computation request
unified_request = UnifiedComputationRequest(
computation_type=ComputationType.NODAL_EVOLUTION, # Default
graph=request.graph,
parameters=request.parameters,
enable_cache=request.enable_cache,
)
result = engine.execute_computation(unified_request)
return result.results
def _execute_optimization_orchestrator(self, request: ComputationRequest) -> Any:
"""Execute using optimization orchestrator."""
engine = self._engines[EngineType.OPTIMIZATION_ORCHESTRATOR]
# Analyze and execute with optimal strategy
profile = engine.analyze_optimization_profile(request.graph, request.operation)
strategy = engine.select_optimal_strategy(profile)
result = engine.execute_optimization(
request.graph, request.operation, strategy, **request.parameters
)
return {
"strategy_used": result.strategy_used.value,
"execution_time": result.execution_time,
"speedup_factor": result.speedup_factor,
"cache_performance": {
"hits": result.cache_hits,
"misses": result.cache_misses,
},
"details": result.details,
}
def _execute_advanced_fft(self, request: ComputationRequest) -> Any:
"""Execute using advanced FFT engine."""
engine = self._engines[EngineType.ADVANCED_FFT]
operation = request.parameters.get("spectral_operation", "harmonic_analysis")
if operation == "harmonic_analysis":
result = engine.harmonic_analysis(request.graph)
elif operation == "spectral_filtering":
result = engine.spectral_filtering(request.graph, **request.parameters)
elif operation == "coherence_analysis":
# Requires second graph
graph2 = request.parameters.get("graph2")
if graph2:
result = engine.cross_spectral_coherence(request.graph, graph2)
else:
raise TNFRValueError(
"Coherence analysis requires second graph",
context={"parameters": request.parameters.keys()},
suggestion="Provide 'graph2' in request parameters for coherence analysis.",
)
else:
result = engine.spectral_convolution(request.graph, **request.parameters)
return result.output_data
def _execute_nodal_optimizer(self, request: ComputationRequest) -> Any:
"""Execute using nodal optimizer."""
engine = self._engines[EngineType.NODAL_OPTIMIZER]
dt = request.parameters.get("dt", 0.01)
result = engine.compute_vectorized_nodal_evolution(request.graph, dt)
return {
"nodal_evolution": result,
"optimization_stats": engine.get_optimization_stats(),
}
def _execute_fft_engine(self, request: ComputationRequest) -> Any:
"""Execute using FFT engine."""
engine = self._engines[EngineType.FFT_ENGINE]
num_steps = request.parameters.get("num_steps", 10)
dt = request.parameters.get("dt", 0.01)
result = engine.run_fft_simulation(request.graph, num_steps, dt)
return result
def _execute_adelic_dynamics(self, request: ComputationRequest) -> Any:
"""Execute using Adelic dynamics."""
engine = self._engines[EngineType.ADELIC_DYNAMICS]
# This would integrate with actual Adelic operations
return {"adelic_computation": "completed", "engine": "adelic_dynamics"}
def _validate_request(self, request: ComputationRequest) -> bool:
"""Validate computation request."""
if not request.request_id:
return False
if request.engine_type not in self._engines:
return False
if request.graph is None and request.operation != "system_status":
return False
return True
def get_system_status(self) -> dict[str, Any]:
"""Get comprehensive system status."""
return {
"active_requests": len(self._active_requests),
"queued_requests": self._request_queue.qsize(),
"available_engines": list(self._engines.keys()),
"resource_status": {
"memory_budget_mb": self.memory_budget_mb,
"cache_size_mb": self.cache_size_mb,
"max_workers": self.max_workers,
},
"performance_summary": {
"total_computations": len(self._performance_history),
"engine_performance": {
engine.value: {
"count": len(times),
"avg_time": np.mean(times) if times else 0.0,
"total_time": np.sum(times) if times else 0.0,
}
for engine, times in self._engine_performance.items()
},
},
"engines_available": HAS_ALL_ENGINES,
"math_backends_available": HAS_MATH_BACKENDS,
}
def shutdown(self) -> None:
"""Gracefully shutdown the computational hub."""
self._shutdown = True
if self._queue_thread and self._queue_thread.is_alive():
self._queue_thread.join(timeout=1.0)
self._executor.shutdown(wait=True)
# Global hub instance
_global_hub: TNFRComputationalHub | None = None
def get_computational_hub() -> TNFRComputationalHub:
"""Get global computational hub."""
global _global_hub
if _global_hub is None:
_global_hub = TNFRComputationalHub()
return _global_hub
def execute_unified_computation(
operation: str,
graph: Any,
engine_type: EngineType = EngineType.UNIFIED_BACKEND,
**kwargs,
) -> ComputationResult:
"""Convenience function for unified computation."""
hub = get_computational_hub()
request = ComputationRequest(
engine_type=engine_type, operation=operation, graph=graph, parameters=kwargs
)
return hub.execute_computation_sync(request)
def batch_execute_computations(
requests: list, max_parallel: int = 4
) -> dict[str, ComputationResult]:
"""Execute multiple computations in parallel."""
hub = get_computational_hub()
# Submit all requests
request_ids = []
for req in requests:
req_id = hub.submit_computation(req)
request_ids.append(req_id)
# Collect results
results = {}
for req_id in request_ids:
result = hub.get_result(req_id, timeout=300.0)
results[req_id] = result
return results