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PLAN DE DESARROLLO - 05_11_GRAPH_SYSTEM

🕸️ Sistema de Grafos de Procesamiento DSP

Versión: 1.0.0 Fecha de creación: 2025-10-14 Criticidad: ⭐⭐⭐⭐⭐ (Crítica - fundación de toda la arquitectura DSP) Tiempo estimado total: 12-18 meses persona

RESUMEN EJECUTIVO

El Graph System es la infraestructura de conexión y flujo que permite construir arquitecturas DSP arbitrariamente complejas mediante grafos dirigidos de procesamiento. Es el sistema nervioso que conecta células L2 y engines L3 en topologías dinámicas, gestionando flujo de señal, sincronización, latencia, y ordenamiento de procesamiento.

Capacidades Clave

  • Grafos de 100+ nodos simultáneos
  • Validación topológica automática (ciclos, tipos, rates)
  • Ordenamiento topológico con detección de paralelismo
  • Gestión de latencia con compensación automática
  • Buffer management con pooling y zero-copy optimization
  • Routing matrices dinámicas y modulables
  • Procesamiento multi-rate sincronizado
  • Reconfiguración dinámica sin glitches
  • Optimización automática de topología
  • Visualización y debugging en tiempo real

MARCO TEÓRICO-PRÁCTICO

Conceptos Fundamentales

1. Grafos Dirigidos Acíclicos (DAG)

  • Nodos: Unidades de procesamiento DSP (kernels, atoms, cells)
  • Edges: Conexiones que transportan señales audio/control
  • Puertos: Puntos de entrada/salida con tipo y formato
  • Topological Sort: Ordenamiento que respeta dependencias

2. Gestión de Latencia

  • Path Latency: Suma de latencias de todos los nodos en un path
  • Delay Compensation: Inserción automática de delays para alinear paths
  • Lookahead Buffers: Buffers que compensan procesamiento predictivo

3. Buffer Management

  • Buffer Pooling: Reutilización de memoria para reducir allocations
  • Zero-Copy: Pasar punteros en lugar de copiar datos
  • Lifetime Analysis: Determinar cuándo buffers pueden compartirse

4. Multi-Rate Processing

  • Upsampling/Downsampling: Conversión entre sample rates
  • Control Rate: Procesamiento a tasa reducida para señales lentas
  • Rate Compensation: Sincronización entre dominios de rate

Algoritmos Específicos

Topological Sorting (Kahn's Algorithm)

1. Calcular in-degree de cada nodo
2. Queue ← nodos con in-degree = 0
3. Mientras queue no vacía:
   a. node ← dequeue()
   b. añadir node a resultado
   c. Para cada vecino de node:
      - decrementar in-degree
      - si in-degree = 0: enqueue vecino
4. Si no todos los nodos visitados: ciclo detectado

Cycle Detection (DFS con colores)

WHITE: no visitado
GRAY: en proceso (en el stack)
BLACK: completado

Si encontramos arista a nodo GRAY: back edge = ciclo

Buffer Lifetime Analysis (Graph Coloring)

1. Crear grafo de interferencia
   - Nodos = buffers
   - Aristas = lifetimes se solapan
2. Colorear con mínimo número de colores
3. Buffers con mismo color comparten memoria

Patterns Arquitectónicos

1. Graph Builder Pattern

  • Construcción incremental del grafo
  • Validación diferida hasta build()
  • Immutable graph tras construcción

2. Visitor Pattern

  • Traversal del grafo para análisis
  • Operaciones sin modificar estructura

3. Command Pattern

  • Reconfiguración como comandos
  • Undo/redo de cambios topológicos

4. Observer Pattern

  • Notificación de cambios en el grafo
  • Actualización de visualización

Métricas de Calidad

Performance

  • Node Capacity: 100+ nodos @ 48kHz, 512 samples
  • Latency Precision: ±1 sample compensation accuracy
  • CPU Overhead: <5% para gestión del grafo
  • Memory Efficiency: 50%+ reducción via buffer sharing
  • Parallel Speedup: 3-4x en hardware 8-core

Robustness

  • Cycle Detection: 100% accuracy
  • Type Validation: Zero runtime type errors
  • Glitch-Free Reconfig: <50ms transition
  • Stability: Zero crashes por topología inválida

Developer Experience

  • Build Time: 10x más rápido construir sistemas complejos
  • Debug Visibility: Visualización clara de cualquier grafo
  • Reusability: 80%+ código reusado via subgraphs

PRIORIZACIÓN Y DEPENDENCIAS

Orden de Implementación

Nivel 1 - Fundamentos (6-8 semanas) 

00_graph_core → Base para todo

Nivel 2 - Validación y Ordenamiento (4-6 semanas) 

01_topology_validation ─┐
02_topological_sorting  ├→ Requieren 00_graph_core

Nivel 3 - Gestión de Recursos (6-8 semanas) 

03_latency_management ─┐
04_buffer_management   ├→ Requieren 00-02

Nivel 4 - Routing Avanzado (6-8 semanas) 

05_routing_matrices    ─┐
06_multirate_processing├→ Requieren 00-04
07_subgraph_abstraction─┘

Nivel 5 - Optimización y Paralelismo (8-10 semanas) 

08_parallel_processing      ─┐
09_dynamic_reconfiguration  ├→ Requieren 00-07
10_graph_optimization       ─┘

Nivel 6 - Herramientas (4-6 semanas) 

11_visualization_system → Requiere todo lo anterior

Nivel 7 - Integración y Testing (4-6 semanas) 

test_integration    ─┐
interfaces          ├→ Requieren subsistema completo
documentation       ─┘

TAREAS DE DESARROLLO

TAREA 1: Graph Core - Núcleo del Sistema

Carpeta: 05_11_00_graph_core Prioridad: CRÍTICA (Fundación) Dependencias: Ninguna Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Node Structure - Clase GraphNode con ID único, tipo, y metadata - Sistema de puertos (input/output) con tipos (AUDIO, CONTROL, MIDI, SIDECHAIN) - Port descriptor: tipo, dirección, channel count, sample rate, block size - Interfaz IDSPProcessor para delegar procesamiento - Estado del nodo: bypass, mix level, latency samples - Metadata: nombre, categoría, versión, vendor

1.2 Edge Structure - Clase GraphEdge representando conexiones - Connection descriptor: source/dest node + port IDs - Propiedades: gain, mute, delay samples - Buffer asociado para transferir datos - Métodos: transfer(), applyGain(), applyDelay()

1.3 Graph Container - Clase AudioGraph como contenedor principal - Storage: unordered_map<NodeID, unique_ptr<GraphNode>> - Edge list: vector<unique_ptr<GraphEdge>> - Process order cache: vector<NodeID> (topological sort result) - Dirty flag para re-sorting - Subgraph registry

1.4 Graph Operations - addNode(node) → NodeID - removeNode(nodeId) - connect(fromNode, fromPort, toNode, toPort) - disconnect(edge) - process(input, output) - procesamiento principal - prepare(sampleRate, blockSize) - inicialización - reset() - limpiar estado

1.5 Port Management - Port validation (type, channel count, rate) - Dynamic port creation/removal - Port format negotiation - Multi-channel port support

2. Testing Framework

Unit Tests: - test_node_creation() - crear nodos con diferentes configs - test_edge_creation() - conexiones válidas - test_graph_operations() - add/remove/connect - test_port_validation() - compatibilidad de puertos - test_node_processing() - llamada a processor delegate - test_bypass_mode() - bypass correcto - test_mix_level() - gain aplicado correctamente

Integration Tests: - test_simple_chain() - 3 nodos en serie - test_parallel_branches() - split y merge - test_complex_graph() - 10+ nodos - test_graph_prepare() - inicialización completa - test_graph_reset() - limpieza de estado

Performance Tests: - benchmark_node_creation() - tiempo de crear 1000 nodos - benchmark_connection() - tiempo de 1000 conexiones - benchmark_traversal() - iterar grafo grande - Memory profiling - no leaks

Target Coverage: >95%

3. Documentación

Inline Documentation: 

/**
 * @class GraphNode
 * @brief Fundamental processing unit in the audio graph
 *
 * A GraphNode represents a single DSP processor with input/output ports.
 * It delegates actual processing to an IDSPProcessor implementation.
 *
 * @example
 * auto node = std::make_unique<GraphNode>();
 * node->addInputPort(PortType::AUDIO, 2);  // Stereo input
 * node->addOutputPort(PortType::AUDIO, 2); // Stereo output
 * node->setProcessor(std::make_unique<MyProcessor>());
 */

API Documentation: - Class reference for GraphNode, GraphEdge, AudioGraph - Method signatures with parameter descriptions - Return values and exceptions - Thread-safety guarantees

Usage Examples: 

// Example 1: Create simple 2-node chain
AudioGraph graph;
auto gain1 = graph.addNode(std::make_unique<GainNode>());
auto gain2 = graph.addNode(std::make_unique<GainNode>());
graph.connect(gain1, 0, gain2, 0);
graph.prepare(48000, 512);
graph.process(inputBuffer, outputBuffer);

Technical Specifications: - Memory layout of structures - Alignment requirements (32-byte for SIMD) - Thread model (single-threaded graph modification) - Real-time safety considerations

4. Interfaces y Conexiones

Public APIs: 

// Graph construction API
class AudioGraph {
public:
    NodeID addNode(std::unique_ptr<GraphNode> node);
    void removeNode(NodeID id);
    void connect(NodeID from, PortID fromPort, NodeID to, PortID toPort);
    void disconnect(NodeID from, NodeID to);

    // Processing API
    void prepare(double sampleRate, int blockSize);
    void process(AudioBuffer& input, AudioBuffer& output);
    void reset();

    // Query API
    size_t getNodeCount() const;
    size_t getEdgeCount() const;
    const GraphNode* getNode(NodeID id) const;
};

Events: 

// Graph change notifications
struct GraphChangeEvent {
    enum Type { NODE_ADDED, NODE_REMOVED, CONNECTION_MADE, CONNECTION_BROKEN };
    Type type;
    NodeID nodeId;
    // ... details
};

class IGraphListener {
public:
    virtual void onGraphChanged(const GraphChangeEvent& event) = 0;
};

Data Contracts: 

// Node descriptor for serialization
struct NodeDescriptor {
    std::string type;
    std::string name;
    json parameters;
    std::vector<PortDescriptor> inputs;
    std::vector<PortDescriptor> outputs;
};

ENTREGABLES:

  • GraphNode class implementation
  • GraphEdge class implementation
  • AudioGraph container implementation
  • Port management system
  • 20+ unit tests (>95% coverage)
  • 10+ integration tests
  • Performance benchmarks
  • Complete API documentation
  • 5+ usage examples
  • Technical specification document

TAREA 2: Topology Validation - Validación Topológica

Carpeta: 05_11_01_topology_validation Prioridad: ALTA Dependencias: 00_graph_core Estimación: 4-6 semanas

DESARROLLO:

1. Core Implementation

1.1 Cycle Detector - DFS-based cycle detection con color marking (WHITE/GRAY/BLACK) - Identificación de back edges - Verificación de delay en ciclos (feedback válido) - Reportar path completo del ciclo detectado - Excepciones: CycleDetectionError con detalles

1.2 Type Compatibility Validator - Type compatibility matrix: - AUDIO → AUDIO (con channel conversion) - CONTROL → CONTROL (con rate conversion) - AUDIO → CONTROL (con envelope follower) - MIDI → MIDI - Channel matching rules: - Mono→Mono: direct - Mono→Stereo: duplicate - Stereo→Mono: sum/average - Stereo→Stereo: direct - Surround→Stereo: downmix matrix - Sugerencias de conversión automática

1.3 Sample Rate Validator - Verificación de rate matching - Detección de ratios simples (2x, 0.5x) - Detección de ratios enteros - Validación de resampler requirements - Inserción automática de resamplers si disponibles

1.4 Channel Configuration Validator - Validar compatibilidad de channel layouts - Detectar upmixing/downmixing needs - Validar surround formats - LFE channel handling

1.5 Validation Pipeline 

ValidationResult validate(const AudioGraph& graph) {
    result.cycleCheck = detectCycles(graph);
    result.typeCheck = validateTypes(graph);
    result.rateCheck = validateSampleRates(graph);
    result.channelCheck = validateChannels(graph);
    return result;
}

2. Testing Framework

Unit Tests: - test_detect_simple_cycle() - A→B→A - test_detect_complex_cycle() - ciclo en grafo grande - test_valid_feedback_loop() - ciclo con delay - test_type_compatibility() - todas las combinaciones - test_channel_conversion() - mono/stereo conversions - test_rate_validation() - diferentes sample rates - test_invalid_connection() - rechazar conexiones malas

Integration Tests: - test_validation_pipeline() - validación completa - test_error_reporting() - mensajes claros - test_auto_conversion_suggestion() - sugerencias útiles

Edge Cases: - Graph vacío - Nodo aislado - Multiple ciclos - Ciclo con delay de 0 samples

Coverage Target: >90%

3. Documentación

API Documentation: 

/**
 * @class CycleDetector
 * @brief Detects cycles in directed graphs using DFS
 *
 * Uses color-marking DFS algorithm to detect back edges.
 * Distinguishes between invalid cycles and valid feedback loops.
 *
 * @see https://en.wikipedia.org/wiki/Cycle_(graph_theory)
 */

Usage Examples: 

// Example: Validate graph before processing
AudioGraph graph;
// ... build graph ...

CycleDetector detector;
auto cycles = detector.detect(graph);
if (!cycles.empty()) {
    std::cerr << "Cycle detected: ";
    for (NodeID node : cycles[0]) {
        std::cerr << getNodeName(node) << " → ";
    }
    std::cerr << std::endl;
}

Error Messages Design: 

❌ Invalid cycle detected:
   ReverbNode → DelayNode → FeedbackGain → ReverbNode

   💡 Suggestion: Add delay compensation in feedback path

   Solution 1: Insert DelayNode with minimum 1 sample
   Solution 2: Use FeedbackNode which includes built-in delay

4. Interfaces y Conexiones

Validation API: 

class GraphValidator {
public:
    struct ValidationResult {
        bool isValid;
        std::vector<ValidationError> errors;
        std::vector<ValidationWarning> warnings;
        std::vector<ConversionSuggestion> suggestions;
    };

    ValidationResult validate(const AudioGraph& graph);
    bool quickCheck(const AudioGraph& graph); // Fast basic check
};

Integration with Graph: 

// Auto-validate on connect
void AudioGraph::connect(NodeID from, PortID fromPort,
                        NodeID to, PortID toPort) {
    // Create tentative connection
    auto edge = createEdge(from, fromPort, to, toPort);

    // Validate
    auto result = validator.validateEdge(edge);
    if (!result.isValid) {
        throw ConnectionError(result.errors[0].message);
    }

    // Apply conversion if suggested
    if (result.needsConversion) {
        insertConverter(edge, result.suggestions[0]);
    }

    edges.push_back(std::move(edge));
}

ENTREGABLES:

  • CycleDetector implementation
  • TypeCompatibilityValidator implementation
  • SampleRateValidator implementation
  • ChannelConfigValidator implementation
  • Validation pipeline integration
  • 15+ unit tests
  • Error reporting system
  • Conversion suggestion engine
  • Documentation with examples

TAREA 3: Topological Sorting - Ordenamiento Topológico

Carpeta: 05_11_02_topological_sorting Prioridad: ALTA Dependencias: 00_graph_core, 01_topology_validation Estimación: 4-6 semanas

DESARROLLO:

1. Core Implementation

1.1 Kahn's Algorithm Implementation 

class TopologicalSorter {
    std::vector<NodeID> sort(const AudioGraph& graph) {
        // 1. Calculate in-degrees
        // 2. Queue nodes with in-degree = 0
        // 3. Process queue
        // 4. Return sorted order
    }
};

1.2 Parallel Branch Detector - Identificar nodos que pueden procesarse en paralelo - Agrupar por "dependency level" - Detectar independencia dentro de niveles - Marcar parallel groups

1.3 Feedback Loop Handler - Detectar back edges (feedback) - Verificar delay en feedback - Insertar delay si falta - Estrategias: - 1-sample delay (mínimo) - Block delay (eficiente para block processing) - Iterative (para physical modeling)

1.4 Processing Order Cache - Cachear resultado de topological sort - Invalidar en cambios de grafo - Lazy recomputation - Thread-safe access

1.5 Priority Sorting - Permitir hints de prioridad por nodo - Ordenar nodos del mismo nivel por prioridad - Use cases: critical path primero

2. Testing Framework

Unit Tests: - test_simple_chain() - A→B→C da orden [A,B,C] - test_parallel_branches() - detectar paralelismo - test_diamond_graph() - A→B,C→D - test_feedback_handling() - loop con delay - test_priority_sorting() - respetar prioridades - test_large_graph() - 100+ nodos - test_incremental_update() - añadir nodo a grafo sorted

Performance Tests: - benchmark_sort_10_nodes() - <1ms - benchmark_sort_100_nodes() - <10ms - benchmark_sort_1000_nodes() - <100ms - benchmark_parallel_detection() - overhead

Integration Tests: - test_sort_and_process() - orden correcto de processing - test_invalidation() - re-sort tras cambio - test_feedback_processing() - feedback funciona

Coverage Target: >90%

3. Documentación

Algorithm Documentation: 

# Topological Sort Algorithm

## Kahn's Algorithm

1. Calculate in-degree for each node
   - in-degree = number of incoming edges

2. Initialize queue with all nodes where in-degree = 0
   - These are source nodes with no dependencies

3. While queue is not empty:
   a. Dequeue node
   b. Add to result
   c. For each neighbor of node:
      - Decrease in-degree by 1
      - If in-degree becomes 0, enqueue neighbor

4. If not all nodes visited → cycle exists

## Complexity
- Time: O(V + E) where V=nodes, E=edges
- Space: O(V) for in-degree tracking

Usage Examples: 

// Example: Sort and process in order
AudioGraph graph;
// ... build graph ...

TopologicalSorter sorter;
auto processOrder = sorter.sort(graph);

for (NodeID nodeId : processOrder) {
    graph.getNode(nodeId)->process();
}

4. Interfaces y Conexiones

Sorting API: 

class TopologicalSorter {
public:
    // Basic sorting
    std::vector<NodeID> sort(const AudioGraph& graph);

    // Parallel-aware sorting
    struct ParallelGroup {
        std::vector<NodeID> nodes;
        int level;  // Dependency level
        bool canProcessInParallel;
    };
    std::vector<ParallelGroup> sortWithParallelism(const AudioGraph& graph);

    // Incremental update
    void updateForNewNode(NodeID newNode);
    void updateForRemovedNode(NodeID removedNode);
};

Integration with AudioGraph: 

void AudioGraph::process(AudioBuffer& input, AudioBuffer& output) {
    if (isDirty) {
        processOrder = sorter.sort(*this);
        isDirty = false;
    }

    for (NodeID nodeId : processOrder) {
        nodes[nodeId]->process();
    }
}

ENTREGABLES:

  • Kahn's algorithm implementation
  • Parallel branch detector
  • Feedback loop handler
  • Processing order cache
  • Priority sorting system
  • 12+ unit tests
  • Performance benchmarks
  • Algorithm documentation
  • Usage examples

TAREA 4: Latency Management - Gestión de Latencia

Carpeta: 05_11_03_latency_management Prioridad: ALTA Dependencias: 00_graph_core, 02_topological_sorting Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Latency Calculator 

class LatencyCalculator {
    struct PathLatency {
        std::vector<NodeID> path;
        int totalSamples;
        std::map<NodeID, int> perNodeLatency;
    };

    int calculatePathLatency(const std::vector<NodeID>& path);
    std::vector<PathLatency> calculateAllPaths(NodeID source, NodeID dest);
    int getMaxLatency(NodeID source, NodeID dest);
};

1.2 Delay Compensation Engine - Calcular latencia de cada path - Encontrar path con máxima latencia - Insertar delays en paths más cortos - Estrategias: - Look-ahead: delay dry signal - Parallel alignment: alinear multi-band - Feedback compensation

1.3 Lookahead Buffer Manager 

class LookaheadManager {
    struct LookaheadBuffer {
        CircularBuffer buffer;
        int delaySamples;
        int readPos, writePos;
    };

    void setupLookahead(NodeID node, int samples);
    void processWithLookahead(NodeID node, float* in, float* out, int n);
};

1.4 Latency Reporter - Reportar latencia total al host - Notificar cambios de latencia - Per-node latency breakdown - Visualización de latency map

1.5 PDC (Plugin Delay Compensation) - Query plugin latency - Track latency changes - Compensate automatically - Host communication

2. Testing Framework

Unit Tests: - test_single_path_latency() - calcular latencia simple - test_parallel_paths() - múltiples paths - test_compensation_insertion() - delays correctos - test_lookahead_buffer() - buffer circular funciona - test_latency_reporting() - reportar al host - test_dynamic_latency_change() - cambio en runtime

Integration Tests: - test_parallel_band_alignment() - multi-band alineado - test_dry_wet_alignment() - dry/wet sincronizados - test_feedback_with_latency() - feedback compensado

Accuracy Tests: - test_compensation_accuracy() - precisión ±1 sample - test_phase_alignment() - verificar phase-aligned output

Performance Tests: - benchmark_latency_calculation() - tiempo de cálculo - benchmark_compensation_overhead() - overhead de delays

Coverage Target: >90%

3. Documentación

Conceptual Documentation: 

# Latency Management

## Problem
Different processing paths introduce different delays.
Without compensation, parallel signals become misaligned.

## Solution
1. Calculate latency of each path
2. Find maximum latency
3. Delay shorter paths to match

## Example
Path A: Filter (0ms) → Gain (0ms) = 0ms
Path B: Reverb (50ms) = 50ms

Compensation: Insert 50ms delay in Path A
Result: Both paths aligned at output

API Documentation: 

/**
 * @class LatencyCalculator
 * @brief Calculates signal latency through graph paths
 *
 * Traverses graph paths and sums latency contributions from each node.
 * Supports multiple paths between source and destination.
 *
 * @note Latency is measured in samples, not milliseconds
 */

Usage Examples: 

// Example: Compensate parallel processing
AudioGraph graph;
auto dry = graph.addNode(makeGain());
auto wet = graph.addNode(makeReverb());  // 50ms latency
auto mix = graph.addNode(makeMixer());

graph.connect(input, 0, dry, 0);
graph.connect(input, 0, wet, 0);
graph.connect(dry, 0, mix, 0);
graph.connect(wet, 0, mix, 1);

// Auto-compensation
LatencyCompensator compensator;
compensator.compensate(graph);
// → Inserts 50ms delay before dry path

4. Interfaces y Conexiones

Latency API: 

class LatencyManager {
public:
    // Query latency
    int getNodeLatency(NodeID node) const;
    int getPathLatency(NodeID from, NodeID to) const;
    int getTotalLatency() const;

    // Compensation
    void compensateGraph(AudioGraph& graph);
    void compensatePath(AudioGraph& graph, NodeID from, NodeID to);

    // Reporting
    void reportToHost(IHostCallback* host);

    // Events
    void onLatencyChanged(std::function<void(int)> callback);
};

Host Integration: 

// VST3 example
class PluginProcessor : public Vst::IAudioProcessor {
    uint32 getLatencySamples() override {
        return latencyManager.getTotalLatency();
    }

    void onGraphChanged() {
        latencyManager.recalculate();
        if (latencyChanged()) {
            notifyHost(Vst::kLatencyChanged);
        }
    }
};

ENTREGABLES:

  • LatencyCalculator implementation
  • Delay compensation engine
  • Lookahead buffer manager
  • Latency reporter
  • PDC system
  • 15+ unit tests
  • Accuracy validation suite
  • Performance benchmarks
  • Complete documentation
  • Host integration examples

TAREA 5: Buffer Management - Gestión de Buffers

Carpeta: 05_11_04_buffer_management Prioridad: ALTA Dependencias: 00_graph_core, 02_topological_sorting Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Buffer Pool 

class GraphBufferPool {
    struct BufferInfo {
        std::unique_ptr<AudioBuffer> buffer;
        bool inUse;
        NodeID owner;
        size_t size;
        int channels;
    };

    AudioBuffer* acquire(int channels, int samples);
    void release(AudioBuffer* buffer);
    void preallocate(size_t count, int channels, int samples);
    void trim();  // Release unused buffers
};

1.2 Lifetime Analyzer - Analizar cuándo cada buffer está "live" - Identificar buffers con lifetimes no-overlapping - Construir interference graph - Graph coloring para minimizar buffers

1.3 Zero-Copy Router 

class ZeroCopyRouter {
    bool canZeroCopy(const GraphEdge& edge);
    void routeZeroCopy(GraphEdge& edge);  // Pass pointer
    void routeWithCopy(GraphEdge& edge);  // Copy data
};

1.4 Buffer Allocator - Aligned allocation (32-byte para AVX) - NUMA-aware allocation en sistemas multi-socket - Huge pages support para large buffers - Lock-free para real-time

1.5 Memory Profiler - Track allocations/deallocations - Detect leaks - Report peak usage - Identify optimization opportunities

2. Testing Framework

Unit Tests: - test_pool_acquire_release() - acquire/release cycle - test_pool_reuse() - verificar reuso - test_lifetime_analysis() - lifetimes correctos - test_zero_copy_detection() - detectar oportunidades - test_alignment() - buffers aligned correctamente - test_thread_safety() - pool thread-safe

Integration Tests: - test_graph_buffer_sharing() - sharing en grafo real - test_memory_efficiency() - reducción de memoria - test_no_leaks() - zero leaks tras 1000 cycles

Performance Tests: - benchmark_acquire_release() - overhead de pool - benchmark_zero_copy_vs_copy() - speedup - benchmark_cache_efficiency() - cache hits

Memory Tests: - Memory usage con/sin pooling - Peak memory reduction - Fragmentation analysis

Coverage Target: >90%

3. Documentación

Architecture Documentation: 

# Buffer Management Architecture

## Buffer Pool
Reuses allocated buffers instead of constant new/delete.

Benefits:
- Faster: No allocator overhead
- Predictable: No allocation in real-time thread
- Efficient: Better cache locality

## Lifetime Analysis
Determines which buffers can share memory.

Algorithm:
1. Build liveness intervals for each buffer
2. Create interference graph
3. Color graph with minimum colors
4. Buffers with same color share memory

## Zero-Copy Optimization
Passes buffer pointers instead of copying data.

Conditions for zero-copy:
- Same channel count
- Same sample count
- No gain change
- No processing

API Documentation: 

/**
 * @class GraphBufferPool
 * @brief Thread-safe pool of reusable audio buffers
 *
 * Maintains a pool of pre-allocated buffers to avoid runtime allocation.
 * Automatically grows when needed and can trim unused buffers.
 *
 * @thread_safety Thread-safe for acquire/release operations
 */

Usage Examples: 

// Example: Use buffer pool in graph
GraphBufferPool pool;

// Preallocate for expected usage
pool.preallocate(20, 2, 512);  // 20 stereo buffers, 512 samples

// Processing
void processGraph() {
    auto* buffer = pool.acquire(2, 512);

    // ... use buffer ...

    pool.release(buffer);
}

// Cleanup
pool.trim();  // Release unused buffers

4. Interfaces y Conexiones

Buffer Management API: 

class BufferManager {
public:
    // Pool management
    void initialize(size_t poolSize, int defaultChannels, int defaultSamples);
    void shutdown();

    // Acquisition
    AudioBuffer* acquireBuffer(int channels, int samples);
    void releaseBuffer(AudioBuffer* buffer);

    // Optimization
    void analyzeGraph(const AudioGraph& graph);
    size_t getMemorySavings() const;

    // Profiling
    struct MemoryStats {
        size_t totalAllocated;
        size_t peakUsage;
        size_t currentUsage;
        int activeBuffers;
    };
    MemoryStats getStats() const;
};

Integration with Graph: 

void AudioGraph::prepare(double sampleRate, int blockSize) {
    // Analyze buffer requirements
    bufferManager.analyzeGraph(*this);

    // Preallocate based on analysis
    auto requirements = bufferManager.getRequirements();
    bufferManager.preallocate(requirements);
}

void AudioGraph::process() {
    // Buffers acquired/released automatically during processing
}

ENTREGABLES:

  • GraphBufferPool implementation
  • Lifetime analyzer
  • Zero-copy router
  • Aligned allocator
  • Memory profiler
  • 15+ unit tests
  • Integration tests
  • Performance benchmarks
  • Memory analysis tools
  • Complete documentation

TAREA 6: Routing Matrices - Matrices de Ruteo

Carpeta: 05_11_05_routing_matrices Prioridad: MEDIA Dependencias: 00_graph_core, 04_buffer_management Estimación: 4-6 semanas

DESARROLLO:

1. Core Implementation

1.1 Routing Matrix Core 

class RoutingMatrix : public GraphNode {
    int numInputs, numOutputs;
    std::vector<std::vector<float>> gains;  // [input][output]
    std::vector<std::vector<bool>> mutes;

    void setConnection(int input, int output, float gain);
    void process(AudioBuffer** inputs, AudioBuffer** outputs);
};

1.2 Crossfade Router - Equal-power crossfade - Linear crossfade - Logarithmic crossfade - Smooth transition (10-50ms ramp) - Click prevention

1.3 Modulation System 

struct ModTarget {
    int input, output;
    float* modSource;
    float modAmount;
    ModCurve curve;
};

1.4 Matrix Presets - Common routing patterns: - Stereo to mono - Mono to stereo - M/S encoding/decoding - LCR (Left-Center-Right) - Surround downmix matrices

1.5 Dynamic Routing - Runtime connection changes - Smooth gain ramping - Mute/unmute without clicks

2. Testing Framework

Unit Tests: - test_matrix_creation() - crear matrices de diferentes tamaños - test_set_connection() - establecer gain - test_routing() - verificar routing correcto - test_crossfade() - transiciones suaves - test_modulation() - modulación funciona - test_presets() - presets correctos

Integration Tests: - test_matrix_in_graph() - matriz como nodo - test_dynamic_routing() - cambios en runtime - test_large_matrix() - 64x64 matrix

Audio Tests: - test_no_clicks() - sin clicks en cambios - test_gain_accuracy() - gain preciso - test_phase_preservation() - no phase issues

Performance Tests: - benchmark_matrix_processing() - overhead - benchmark_crossfade() - costo de crossfade

Coverage Target: >85%

3. Documentación

Conceptual Documentation: 

# Routing Matrices

## Purpose
Flexible signal routing between multiple inputs and outputs.

## Use Cases
- Mixer consoles
- Multi-bus routing
- Surround sound processing
- Modular synthesis patching

## Crossfade Curves
- **Equal Power:** Constant perceived loudness
  - gain_a = cos(θ), gain_b = sin(θ)
- **Linear:** Simple but center dip
  - gain_a = 1-x, gain_b = x
- **Logarithmic:** Smooth transition
  - gain_a = 10^(-x*3), gain_b = 10^(-(1-x)*3)

API Documentation: 

/**
 * @class RoutingMatrix
 * @brief NxM routing matrix with per-connection gain and mute
 *
 * Provides flexible routing between N inputs and M outputs.
 * Each connection has independent gain and mute control.
 * Supports modulation of routing gains.
 */

Usage Examples: 

// Example: 2x4 routing matrix
auto matrix = std::make_unique<RoutingMatrix>(2, 4);

// Route input 0 to outputs 0 and 1 (stereo)
matrix->setConnection(0, 0, 1.0f);  // Left
matrix->setConnection(0, 1, 1.0f);  // Right

// Route input 1 to outputs 2 and 3
matrix->setConnection(1, 2, 0.8f);
matrix->setConnection(1, 3, 0.8f);

// Crossfade input 0 from output 0 to output 2
matrix->crossfade(0, 0, 0, 2, 50.0f);  // 50ms crossfade

4. Interfaces y Conexiones

Routing API: 

class RoutingMatrix {
public:
    RoutingMatrix(int numInputs, int numOutputs);

    // Connections
    void setConnection(int input, int output, float gain);
    float getConnection(int input, int output) const;
    void muteConnection(int input, int output);
    void unmuteConnection(int input, int output);

    // Crossfade
    void crossfade(int input, int fromOutput, int toOutput, float timeMs);

    // Modulation
    void addModulation(int input, int output, float* modSource, float amount);

    // Presets
    void loadPreset(MatrixPreset preset);
    void savePreset() const;
};

Preset System: 

enum class MatrixPreset {
    STEREO_TO_MONO,
    MONO_TO_STEREO,
    MS_ENCODE,
    MS_DECODE,
    LCR,
    SURROUND_51_TO_STEREO,
    IDENTITY
};

ENTREGABLES:

  • RoutingMatrix implementation
  • Crossfade system
  • Modulation system
  • Matrix presets library
  • 12+ unit tests
  • Audio quality tests
  • Performance benchmarks
  • Usage documentation
  • Preset library

TAREA 7: Multi-Rate Processing - Procesamiento Multi-Rate

Carpeta: 05_11_06_multirate_processing Prioridad: MEDIA Dependencias: 00_graph_core, 01_topology_validation Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Rate Converter Node 

class RateConverterNode : public GraphNode {
    double inputRate, outputRate, ratio;
    std::unique_ptr<Resampler> resampler;

    void prepare(double inRate, double outRate);
    void process(AudioBuffer& input, AudioBuffer& output);
};

1.2 Resampler Implementations - UpsamplerX2: 2x upsampling (polyphase FIR) - DownsamplerX2: 2x downsampling (antialias + decimate) - PolyphaseResampler: Arbitrary ratio resampling - LinearResampler: Fast but low quality - SincResampler: High quality

1.3 Control Rate Processing 

class ControlRateNode : public GraphNode {
    int decimationFactor;  // Process every N samples
    std::vector<float> accumulator;

    void processControlRate();
    void interpolateToAudioRate();
};

1.4 Oversampling System - 2x, 4x, 8x oversampling options - Automatic insertion of up/downsampling - Latency compensation for oversampling - Quality vs CPU trade-off

1.5 Multi-Rate Scheduler - Schedule nodos según rate - Sincronización entre rates - Buffer size adjustment

2. Testing Framework

Unit Tests: - test_2x_upsampling() - verificar 2x - test_2x_downsampling() - verificar 0.5x - test_arbitrary_ratio() - 44.1k → 48k - test_control_rate() - decimation works - test_interpolation() - upsample back to audio - test_latency_compensation() - compensar oversampling

Audio Quality Tests: - test_frequency_response() - flat response - test_aliasing() - no aliasing in downsampling - test_imaging() - no imaging in upsampling - test_passband_ripple() - <0.1dB - test_stopband_attenuation() - >80dB

Performance Tests: - benchmark_2x_upsampling() - CPU usage - benchmark_arbitrary_resampling() - overhead - benchmark_control_rate_savings() - CPU saved

Coverage Target: >85%

3. Documentación

Theory Documentation: 

# Multi-Rate Processing

## Upsampling
Process: Zero-stuff → Lowpass Filter

Ratio 2x:
- Insert zero between each sample
- Filter at original Nyquist frequency
- Result: 2x sample rate

## Downsampling
Process: Lowpass Filter → Decimate

Ratio 0.5x:
- Filter at new Nyquist frequency (antialias)
- Keep every 2nd sample
- Result: 0.5x sample rate

## Control Rate
Many signals don't need audio rate processing:
- LFOs (<20Hz)
- Envelope followers
- Meters
- Macro controls

Process every N samples (decimation factor 32-128)
Interpolate back to audio rate for output
Saves 30-50% CPU on suitable signals

API Documentation: 

/**
 * @class RateConverterNode
 * @brief Converts between different sample rates
 *
 * Supports integer ratios (2x, 4x, 8x, 0.5x, 0.25x) and arbitrary ratios.
 * Uses polyphase filtering for high quality conversion.
 *
 * @performance Integer ratios: ~0.5ms/1000 samples @ 48kHz
 *              Arbitrary ratios: ~2ms/1000 samples @ 48kHz
 */

Usage Examples: 

// Example: 4x oversampling for nonlinear processor
auto upsample = std::make_unique<RateConverterNode>();
upsample->setRatio(4.0);

auto saturator = std::make_unique<SaturatorNode>();

auto downsample = std::make_unique<RateConverterNode>();
downsample->setRatio(0.25);

graph.connect(input, upsample);
graph.connect(upsample, saturator);
graph.connect(saturator, downsample);
graph.connect(downsample, output);

4. Interfaces y Conexiones

Resampling API: 

class ResamplingManager {
public:
    // Insert resamplers automatically
    void insertResamplers(AudioGraph& graph);

    // Oversampling
    void enableOversampling(NodeID node, int factor);
    void disableOversampling(NodeID node);

    // Control rate
    void setControlRate(NodeID node, int decimation);

    // Query
    double getNodeRate(NodeID node) const;
    int getOversamplingFactor(NodeID node) const;
};

Quality Settings: 

enum class ResamplingQuality {
    DRAFT,      // Linear, fast
    GOOD,       // Polyphase 32-tap
    HIGH,       // Polyphase 128-tap
    BEST        // Sinc windowed
};

ENTREGABLES:

  • RateConverterNode implementation
  • Multiple resampler algorithms
  • Control rate system
  • Oversampling system
  • Multi-rate scheduler
  • 15+ unit tests
  • Audio quality test suite
  • Performance benchmarks
  • Theory documentation
  • Quality comparison charts

TAREA 8: Subgraph Abstraction - Abstracción de Subgrafos

Carpeta: 05_11_07_subgraph_abstraction Prioridad: MEDIA Dependencias: 00_graph_core, 02_topological_sorting Estimación: 4-6 semanas

DESARROLLO:

1. Core Implementation

1.1 Subgraph Template 

# Template definition format
metadata:
  name: "Vintage Channel Strip"
  category: "Channel Strips"
  version: "1.0.0"
  author: "AudioLab"

interface:
  inputs:
    - name: main
      type: audio
      channels: 2
    - name: sidechain
      type: audio
      channels: 1

  outputs:
    - name: main
      type: audio
      channels: 2
    - name: send
      type: audio
      channels: 2

  parameters:
    - name: gain
      range: [-inf, 12dB]
      default: 0dB
    - name: comp_threshold
      range: [-40dB, 0dB]
      default: -12dB

internal_graph:
  nodes:
    - id: input_gain
      type: GainNode
    - id: eq
      type: ThreeBandEQNode
    - id: compressor
      type: VCACompressorNode
    - id: output_gain
      type: GainNode

  connections:
    - [input→input_gain]
    - [input_gain→eq]
    - [eq→compressor]
    - [compressor→output_gain]
    - [sidechain→compressor.key]

1.2 Subgraph Instance 

class SubGraphInstance : public GraphNode {
    std::unique_ptr<AudioGraph> internalGraph;
    std::map<std::string, NodeID> interfaceNodes;
    std::map<std::string, ParamID> exposedParams;

    void instantiate(const SubGraphTemplate& tmpl);
    void process(AudioBuffer& input, AudioBuffer& output) override;
};

1.3 Template Loader - Load from YAML/JSON - Validate template structure - Resolve node type references - Build internal graph

1.4 Parameter Mapping - Map template parameters to internal nodes - Support parameter groups - Parameter constraints - Value transformations

1.5 Subgraph Library - Registry of available templates - Categories and tags - Search and filtering - Version management

2. Testing Framework

Unit Tests: - test_template_loading() - parse YAML correctly - test_subgraph_instantiation() - create instance - test_parameter_mapping() - params map correctly - test_nested_subgraphs() - subgraph dentro de subgraph - test_interface_validation() - interface correcto

Integration Tests: - test_subgraph_in_main_graph() - usar como nodo - test_subgraph_library() - registry funciona - test_version_compatibility() - version handling

Audio Tests: - test_subgraph_processing() - procesamiento correcto - test_latency_reporting() - latencia total correcta

Coverage Target: >85%

3. Documentación

Conceptual Documentation: 

# Subgraph Abstraction

## Purpose
Encapsulate complex multi-node topologies as reusable components.

## Benefits
- **Modularity:** Build once, use many times
- **Abstraction:** Hide internal complexity
- **Reusability:** Share subgraphs between projects
- **Maintainability:** Update definition, all instances update

## Use Cases
- Channel strips (gain + EQ + comp)
- Mastering chains (multiband + limiter + dither)
- Effect modules (chorus + reverb + delay)
- Synthesizer voices (osc + filter + env)

## Example
A "Vintage Channel" subgraph contains:
- Input gain
- 3-band EQ
- VCA compressor with sidechain
- Output gain (makeup)

External view: Single node with 5 parameters
Internal reality: 4 connected nodes

Template Specification: 

# Subgraph Template Format

## Structure
- metadata: Name, version, author, description
- interface: Exposed inputs, outputs, parameters
- internal_graph: Nodes and connections
- initialization: Default parameter values

## Parameter Mapping
Maps exposed parameters to internal node parameters.
Supports transformations: linear, exponential, table lookup.

Example:
exposed_param "drive" → internal "gain" with 2x multiplier

Usage Examples: 

// Example: Load and use subgraph
SubGraphLibrary library;
library.loadFromDirectory("subgraphs/");

// Instantiate
auto channelStrip = library.createInstance("Vintage Channel");

// Add to main graph
auto nodeId = graph.addNode(std::move(channelStrip));

// Set parameters
graph.setParameter(nodeId, "gain", 3.0f);
graph.setParameter(nodeId, "comp_threshold", -18.0f);

4. Interfaces y Conexiones

Subgraph API: 

class SubGraphLibrary {
public:
    // Loading
    void loadTemplate(const std::string& path);
    void loadFromDirectory(const std::string& dir);

    // Query
    std::vector<std::string> listTemplates() const;
    SubGraphTemplate getTemplate(const std::string& name) const;

    // Instantiation
    std::unique_ptr<SubGraphInstance> createInstance(const std::string& name);
};

class SubGraphInstance {
public:
    // External interface (appears as single node)
    void process(AudioBuffer& input, AudioBuffer& output) override;
    void setParameter(const std::string& name, float value);
    float getParameter(const std::string& name) const;

    // Internal access (for debugging)
    const AudioGraph& getInternalGraph() const;
};

Template Format: 

struct SubGraphTemplate {
    struct Metadata {
        std::string name;
        std::string category;
        std::string version;
        std::string author;
    };

    struct Interface {
        std::vector<PortDescriptor> inputs;
        std::vector<PortDescriptor> outputs;
        std::vector<ParameterDescriptor> parameters;
    };

    struct InternalGraph {
        std::vector<NodeDescriptor> nodes;
        std::vector<ConnectionDescriptor> connections;
    };

    Metadata metadata;
    Interface interface;
    InternalGraph internalGraph;
};

ENTREGABLES:

  • SubGraphTemplate structure
  • SubGraphInstance implementation
  • Template loader (YAML/JSON)
  • Parameter mapping system
  • SubGraphLibrary
  • 10+ unit tests
  • Template specification document
  • Example templates library (5+)
  • Usage documentation

TAREA 9: Parallel Processing - Procesamiento Paralelo

Carpeta: 05_11_08_parallel_processing Prioridad: ALTA Dependencias: 00_graph_core, 02_topological_sorting Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Parallel Branch Identifier 

class ParallelDetector {
    struct ParallelGroup {
        std::vector<NodeID> nodes;
        int level;  // Dependency level
        bool canProcessInParallel;
    };

    std::vector<ParallelGroup> detectParallelBranches(const AudioGraph& graph);
};

1.2 Thread Pool 

class ThreadPool {
    std::vector<std::thread> workers;
    std::queue<std::function<void()>> tasks;
    std::mutex queueMutex;
    std::condition_variable condition;

    void enqueue(std::function<void()> task);
    void waitAll();
};

1.3 Parallel Graph Executor 

class ParallelGraphExecutor {
    ThreadPool threadPool;
    std::vector<ParallelBranch> branches;

    void identifyParallelBranches(const AudioGraph& graph);
    void processParallel();
};

1.4 SIMD Branch Processing - Detectar nodos idénticos procesables en SIMD - AVX processing de 8 canales paralelos - NEON processing (ARM)

1.5 Work Stealing Scheduler - Thread pool con work stealing - Balance dinámico de carga - Minimizar idle time

2. Testing Framework

Unit Tests: - test_parallel_detection() - detectar branches correctos - test_thread_pool() - thread pool funciona - test_work_distribution() - distribución equitativa - test_synchronization() - sincronización correcta - test_simd_processing() - SIMD works

Integration Tests: - test_parallel_execution() - ejecución paralela correcta - test_serial_vs_parallel() - resultados idénticos - test_mixed_serial_parallel() - mix funciona

Performance Tests: - benchmark_speedup_2_cores() - speedup en 2 cores - benchmark_speedup_4_cores() - speedup en 4 cores - benchmark_speedup_8_cores() - speedup en 8 cores - benchmark_overhead() - overhead de paralelización

Stress Tests: - test_many_threads() - 100+ threads - test_long_running() - estabilidad 24h - test_load_balance() - balance dinámico

Coverage Target: >85%

3. Documentación

Theory Documentation: 

# Parallel Processing

## Amdahl's Law
Speedup = 1 / ((1-P) + P/N)
- P = fraction that can be parallelized
- N = number of processors

Example: 80% parallelizable, 4 cores
Speedup = 1 / (0.2 + 0.8/4) = 2.5x

## Independent Subgraphs
Subgraphs are independent if no path exists between them.
Can be processed on separate threads simultaneously.

## Dependency Levels
Level 0: No dependencies (sources)
Level 1: Depends only on level 0
Level N: Depends on level N-1

Nodes at same level with no inter-dependencies → parallel

## SIMD Processing
Process multiple channels with single instruction.
AVX: 8 floats simultaneously
Requirements:
- Identical processing
- Same parameters
- Aligned memory

Performance Analysis: 

# Expected Speedups

## 2-Core System
- Best case: 1.8x (90% efficiency)
- Typical: 1.5x (75% efficiency)
- Overhead: thread sync, cache effects

## 4-Core System
- Best case: 3.2x (80% efficiency)
- Typical: 2.5x (62% efficiency)

## 8-Core System
- Best case: 5.6x (70% efficiency)
- Typical: 4.0x (50% efficiency)
- Diminishing returns due to dependencies

Usage Examples: 

// Example: Enable parallel processing
AudioGraph graph;
// ... build graph with parallel branches ...

ParallelGraphExecutor executor(4);  // 4 threads
executor.prepare(graph);

// Processing automatically parallelized
executor.process(inputBuffer, outputBuffer);

// Statistics
auto stats = executor.getStats();
std::cout << "Speedup: " << stats.speedup << "x" << std::endl;
std::cout << "Thread efficiency: " << stats.efficiency << "%" << std::endl;

4. Interfaces y Conexiones

Parallel Execution API: 

class ParallelGraphExecutor {
public:
    ParallelGraphExecutor(int numThreads = 0);  // 0 = auto-detect

    void prepare(const AudioGraph& graph);
    void process(AudioBuffer& input, AudioBuffer& output);

    // Configuration
    void setThreadCount(int count);
    void enableWorkStealing(bool enable);
    void enableSIMD(bool enable);

    // Statistics
    struct Stats {
        float speedup;
        float efficiency;  // 0-100%
        int activeThreads;
        std::map<NodeID, float> perNodeCPU;
    };
    Stats getStats() const;
};

Thread Safety: 

// Graph modification is NOT thread-safe during processing
// Use double-buffering for dynamic reconfiguration

class ThreadSafeGraph {
    std::shared_ptr<AudioGraph> activeGraph;
    std::shared_ptr<AudioGraph> shadowGraph;
    std::atomic<bool> swapPending;

    void modifyGraph(std::function<void(AudioGraph&)> modifier);
    void swapGraphs();  // At safe point
};

ENTREGABLES:

  • ParallelDetector implementation
  • ThreadPool implementation
  • ParallelGraphExecutor
  • SIMD optimizations
  • Work stealing scheduler
  • 15+ unit tests
  • Performance benchmarks on 2/4/8 cores
  • Scalability analysis
  • Thread-safety documentation
  • Optimization guide

TAREA 10: Dynamic Reconfiguration - Reconfiguración Dinámica

Carpeta: 05_11_09_dynamic_reconfiguration Prioridad: ALTA Dependencias: 00_graph_core, 04_buffer_management Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Dynamic Graph Manager 

class DynamicGraphManager {
    std::atomic<AudioGraph*> activeGraph;
    std::unique_ptr<AudioGraph> shadowGraph;
    CrossfadeBuffer crossfader;

    void reconfigure(const GraphChange& change);
    void processWithCrossfade();
};

1.2 Glitch-Free Crossfade 

class CrossfadeBuffer {
    enum State { IDLE, FADING_OUT, FADING_IN, CROSSFADING };
    State state;
    float position;  // 0.0 - 1.0
    float duration;  // samples

    void start(float durationMs);
    void process(AudioBuffer& oldSignal, AudioBuffer& newSignal, AudioBuffer& output);
};

1.3 Hot-Swap System - Bypass old node - Insert new node in parallel - Match state if possible - Crossfade old → new - Remove old node

1.4 State Transfer 

class StateTransfer {
    bool canTransfer(const GraphNode& from, const GraphNode& to);
    void transfer(GraphNode& from, GraphNode& to);

    // Transferable state:
    // - Parameter values
    // - Delay line contents
    // - Filter states (if same type)
};

1.5 Atomic Operations - Add node atomically - Remove node atomically - Reconnect atomically - Rollback on failure

2. Testing Framework

Unit Tests: - test_crossfade() - crossfade suave - test_hot_swap() - swap sin glitches - test_state_transfer() - transferir estado - test_atomic_operations() - operaciones atómicas - test_rollback() - rollback en error

Integration Tests: - test_live_reconfiguration() - reconfig durante procesamiento - test_multiple_changes() - múltiples cambios seguidos - test_complex_swap() - swap de subgrafo

Audio Quality Tests: - test_no_clicks() - zero clicks detectados - test_no_pops() - zero pops detectados - test_smooth_transition() - transición imperceptible - test_latency_preserved() - latencia consistente

Performance Tests: - benchmark_reconfiguration_time() - tiempo de reconfig - benchmark_crossfade_overhead() - overhead de crossfade

Stress Tests: - test_rapid_changes() - 10 cambios/segundo - test_long_running() - estabilidad 24h con cambios

Coverage Target: >90%

3. Documentación

Conceptual Documentation: 

# Dynamic Reconfiguration

## Challenge
Changing graph topology while processing audio causes glitches.

## Solution: Shadow Graph Technique
1. Clone active graph to shadow
2. Apply changes to shadow
3. Prepare shadow graph
4. Crossfade from active to shadow
5. Swap graphs atomically

## Crossfade Strategy
- Duration: 50ms típico
- Curve: Equal-power for constant level
- Application: Mix outputs during transition

## State Transfer
When swapping similar nodes, transfer state for continuity:
- **Parameters:** Always transfer if compatible
- **Delay lines:** Transfer if same size
- **Filters:** Transfer if same type
- **Envelopes:** Transfer phase if possible

Non-transferable: Different algorithms start fresh

API Documentation: 

/**
 * @class DynamicGraphManager
 * @brief Manages glitch-free dynamic graph reconfiguration
 *
 * Allows modifying graph topology while audio is processing.
 * Uses shadow graph technique with crossfading to avoid artifacts.
 *
 * @performance Reconfiguration time: <50ms typical
 * @audio_quality Zero detectable clicks or pops
 */

Usage Examples: 

// Example: Hot-swap effect
DynamicGraphManager manager;
manager.setActiveGraph(std::move(graph));

// User switches from Reverb A to Reverb B
GraphChange change;
change.type = GraphChange::HOT_SWAP;
change.oldNode = reverbA_id;
change.newNode = createReverbB();
change.transferState = true;
change.crossfadeDuration = 50.0f;  // 50ms

manager.reconfigure(change);
// → Seamless transition, no glitches

// Example: Add effect to chain
GraphChange change;
change.type = GraphChange::INSERT_NODE;
change.newNode = createChorus();
change.insertAfter = delay_id;
change.crossfadeDuration = 30.0f;

manager.reconfigure(change);

4. Interfaces y Conexiones

Reconfiguration API: 

class DynamicGraphManager {
public:
    void setActiveGraph(std::unique_ptr<AudioGraph> graph);

    // Reconfiguration operations
    void addNode(std::unique_ptr<GraphNode> node,
                 const ConnectionSpec& connections,
                 float crossfadeMs = 50.0f);

    void removeNode(NodeID node,
                    float crossfadeMs = 50.0f);

    void replaceNode(NodeID oldNode,
                     std::unique_ptr<GraphNode> newNode,
                     bool transferState = true,
                     float crossfadeMs = 50.0f);

    void reconnect(NodeID from, NodeID to,
                   const ConnectionSpec& newConnections,
                   float crossfadeMs = 50.0f);

    // Processing (call from audio thread)
    void process(AudioBuffer& input, AudioBuffer& output);

    // Status
    bool isReconfiguring() const;
    float getReconfigurationProgress() const;  // 0.0 - 1.0
};

struct GraphChange {
    enum Type { ADD_NODE, REMOVE_NODE, HOT_SWAP, RECONNECT };
    Type type;
    // ... details
};

Thread Safety: 

// Reconfiguration thread-safe with processing
// - Reconfig operations: Any thread
// - Process: Audio thread only
// - Internally synchronized with atomics

ENTREGABLES:

  • DynamicGraphManager implementation
  • Crossfade system
  • Hot-swap system
  • State transfer system
  • Atomic operations
  • 15+ unit tests
  • Audio quality test suite (click/pop detection)
  • Performance benchmarks
  • Thread-safety analysis
  • Complete documentation

TAREA 11: Graph Optimization - Optimización del Grafo

Carpeta: 05_11_10_graph_optimization Prioridad: MEDIA Dependencias: 00_graph_core, 02_topological_sorting Estimación: 6-8 semanas

DESARROLLO:

1. Core Implementation

1.1 Dead Node Eliminator 

class DeadNodeEliminator {
    std::set<NodeID> findDeadNodes(const AudioGraph& graph);
    void eliminateDeadNodes(AudioGraph& graph);
};

1.2 Node Fusion Engine 

class NodeFusionEngine {
    struct FusionPattern {
        std::string pattern;  // e.g., "Gain->Gain"
        std::function<std::unique_ptr<GraphNode>(const std::vector<GraphNode*>&)> fuse;
    };

    std::vector<FusionPattern> patterns;
    void detectAndFuse(AudioGraph& graph);
};

1.3 Buffer Coalescer 

class BufferCoalescer {
    void analyzeBufferUsage(const AudioGraph& graph);
    void coalesceBuffers(AudioGraph& graph);
    size_t getMemorySavings() const;
};

1.4 Constant Propagation - Detectar nodos con parámetros constantes - Pre-calcular resultados si posible - Reemplazar con valores constantes

1.5 Common Subexpression Elimination - Detectar cálculos duplicados - Reusar resultados - Reducir redundancia

2. Testing Framework

Unit Tests: - test_dead_node_detection() - detectar nodos muertos - test_node_fusion() - fusionar nodos correctamente - test_buffer_coalescing() - coalescer buffers - test_constant_propagation() - propagar constantes - test_optimization_correctness() - resultado igual pre/post

Integration Tests: - test_optimization_pipeline() - múltiples optimizaciones - test_iterative_optimization() - optimizar hasta convergencia - test_complex_graph_optimization() - grafo real grande

Performance Tests: - benchmark_optimization_time() - tiempo de optimización - benchmark_cpu_improvement() - mejora de CPU - benchmark_memory_improvement() - reducción memoria

Verification Tests: - test_audio_equivalence() - audio idéntico pre/post - test_parameter_response() - parámetros funcionan igual - test_latency_preservation() - latencia preserved

Coverage Target: >85%

3. Documentación

Optimization Techniques: 

# Graph Optimization Techniques

## 1. Dead Node Elimination
Remove nodes that don't contribute to output.

Algorithm: Backward traversal from outputs
- Mark all nodes reachable from outputs as "live"
- Remove unmarked nodes

Example:
Before: Input → Gain → [Muted] → Output
After: Input → Output

## 2. Node Fusion
Combine compatible nodes into single node.

Patterns:
- **Gain + Gain:** Single gain with product
- **Delay + Delay:** Single delay with sum
- **Filter cascade:** Combined transfer function

Example:
Before: Signal → Gain(2x) → Gain(3x) → Output
After: Signal → Gain(6x) → Output

## 3. Buffer Coalescing
Share memory between non-overlapping buffers.

Benefit: 50-70% memory reduction typical

## 4. Constant Propagation
Pre-calculate nodes with constant parameters.

Example:
Gain node with gain=1.0 → Pass-through (no-op)
Gain node with gain=0.0 → Silence generator

## 5. Common Subexpression Elimination
Reuse duplicate calculations.

Example:
Input → [LPF@1kHz] → OutputA
      → [LPF@1kHz] → OutputB

Optimized:
Input → [LPF@1kHz] → Split → OutputA
                            → OutputB

API Documentation: 

/**
 * @class GraphOptimizer
 * @brief Applies optimization passes to audio graph
 *
 * Performs multiple optimization techniques to improve performance:
 * - Dead code elimination
 * - Node fusion
 * - Buffer coalescing
 * - Constant propagation
 *
 * @note Always preserves audio output equivalence
 * @performance Typical improvement: 30-50% CPU reduction
 */

Usage Examples: 

// Example: Optimize graph
AudioGraph graph;
// ... build complex graph ...

GraphOptimizer optimizer;
optimizer.addPass(std::make_unique<DeadNodeEliminator>());
optimizer.addPass(std::make_unique<NodeFusionEngine>());
optimizer.addPass(std::make_unique<BufferCoalescer>());

auto result = optimizer.optimize(graph);

std::cout << "Nodes removed: " << result.nodesRemoved << std::endl;
std::cout << "Nodes fused: " << result.nodesFused << std::endl;
std::cout << "Memory saved: " << result.memorySaved << " bytes" << std::endl;
std::cout << "CPU improvement: " << result.cpuImprovement << "%" << std::endl;

4. Interfaces y Conexiones

Optimizer API: 

class GraphOptimizer {
public:
    // Optimization passes
    void addPass(std::unique_ptr<OptimizationPass> pass);
    void removePass(const std::string& passName);

    // Optimize
    struct OptimizationResult {
        int nodesRemoved;
        int nodesFused;
        size_t memorySaved;
        float cpuImprovement;  // Percentage
        std::vector<std::string> appliedOptimizations;
    };
    OptimizationResult optimize(AudioGraph& graph);

    // Configuration
    void setAggressiveness(int level);  // 0-3
    void enablePass(const std::string& passName, bool enable);

    // Verification
    bool verifyEquivalence(const AudioGraph& original,
                          const AudioGraph& optimized);
};

class OptimizationPass {
public:
    virtual std::string getName() const = 0;
    virtual bool apply(AudioGraph& graph) = 0;
    virtual bool canApply(const AudioGraph& graph) const = 0;
};

Optimization Passes: 

// Built-in passes
class DeadNodeEliminationPass : public OptimizationPass { };
class NodeFusionPass : public OptimizationPass { };
class BufferCoalescingPass : public OptimizationPass { };
class ConstantPropagationPass : public OptimizationPass { };
class BypassEliminationPass : public OptimizationPass { };
class IdentityRemovalPass : public OptimizationPass { };

ENTREGABLES:

  • DeadNodeEliminator
  • NodeFusionEngine with pattern library
  • BufferCoalescer
  • ConstantPropagation pass
  • CSE (Common Subexpression Elimination) pass
  • GraphOptimizer framework
  • 15+ unit tests
  • Optimization verification suite
  • Performance comparison benchmarks
  • Optimization guide documentation

TAREA 12: Visualization System - Sistema de Visualización

Carpeta: 05_11_11_visualization_system Prioridad: MEDIA Dependencias: Todo el subsistema Estimación: 4-6 semanas

DESARROLLO:

1. Core Implementation

1.1 Graph Renderer 

class GraphRenderer {
    enum LayoutAlgorithm { HIERARCHICAL, FORCE_DIRECTED, CIRCULAR };

    void setLayout(LayoutAlgorithm algo);
    void render(const AudioGraph& graph, RenderTarget& target);
};

1.2 Layout Algorithms - Hierarchical: Layered by dependency (good for DAGs) - Force-directed: Physics simulation (general graphs) - Circular: Nodes in circle (cyclic graphs)

1.3 Visual Elements 

struct NodeVisual {
    Shape shape;  // Rectangle/Circle by type
    Color color;  // By category
    std::string label;
    Position position;

    // Annotations
    float cpuUsage;
    float peakLevel;
    bool isClipping;
    bool isBypassed;
};

struct EdgeVisual {
    LineStyle style;  // Solid=audio, dashed=control
    float width;  // Proportional to channels
    Color color;  // Green=active, gray=muted
    bool animated;  // Show signal flow
};

1.4 Real-Time Monitoring 

class GraphMonitor {
    struct NodeMetrics {
        float cpuUsage;
        float peakLevel;
        int processCallsPerSec;
        bool isClipping;
        float latencyMs;
    };

    void updateMetrics();
    void renderOverlay(GraphRenderer& renderer);
};

1.5 Interactive Features - Node selection - Parameter editing - Connection dragging - Zoom and pan - Search/filter nodes

2. Testing Framework

Unit Tests: - test_hierarchical_layout() - layout correcto - test_force_directed_layout() - convergencia - test_node_rendering() - render correcto - test_edge_rendering() - edges correctos - test_metrics_collection() - métricas precisas

Visual Tests: - test_simple_chain_visual() - cadena simple se ve bien - test_complex_graph_visual() - grafo complejo legible - test_color_coding() - colores informativos - test_interactive_features() - interacción funciona

Performance Tests: - benchmark_rendering() - FPS con 100 nodes - benchmark_layout_calculation() - tiempo de layout

Coverage Target: >80%

3. Documentación

Visualization Guide: 

# Graph Visualization

## Visual Language

### Node Shapes
- **Circle:** Sources (input, oscillators, generators)
- **Rectangle:** Processors (filters, effects)
- **Hexagon:** Outputs (to DAC, file, etc.)

### Node Colors
- **Blue:** Input/Source
- **Green:** Effects/Processors
- **Yellow:** Analysis/Meters
- **Red:** Output/Destination
- **Gray:** Bypassed

### Edge Styles
- **Solid:** Audio signal
- **Dashed:** Control signal
- **Dotted:** MIDI
- **Thick:** Multi-channel

### Edge Colors
- **Green:** Active, signal present
- **Gray:** Muted or inactive
- **Red:** Clipping detected

## Layout Algorithms

### Hierarchical (Sugiyama)
Best for: DAGs with clear flow direction
Pros: Clear dependencies
Cons: Not for cyclic graphs

### Force-Directed (Fruchterman-Reingold)
Best for: General graphs
Pros: Good for any graph
Cons: Slow for large graphs (>100 nodes)

### Circular
Best for: Feedback-heavy graphs
Pros: Shows cycles clearly
Cons: Can be cluttered

## Real-Time Metrics

### CPU Usage
Color intensity: Light → Dark = Low → High CPU

### Audio Levels
Meter beside node shows peak level
Red indicator if clipping

### Latency
Number on node shows latency in milliseconds

Usage Examples: 

// Example: Visualize graph
GraphRenderer renderer;
renderer.setLayout(LayoutAlgorithm::HIERARCHICAL);

GraphMonitor monitor;
monitor.attachToGraph(graph);

// Render loop
while (running) {
    monitor.updateMetrics();

    RenderTarget target = getWindow();
    renderer.render(graph, target);
    monitor.renderOverlay(renderer);

    target.present();
}

4. Interfaces y Conexiones

Visualization API: 

class GraphVisualizer {
public:
    // Setup
    void setGraph(const AudioGraph* graph);
    void setLayout(LayoutAlgorithm algo);

    // Rendering
    void render(RenderTarget& target);
    void update(float deltaTime);

    // Interaction
    NodeID getNodeAtPosition(Point pos);
    void selectNode(NodeID node);
    void deselectAll();

    // Configuration
    void showMetrics(bool show);
    void showEdgeFlow(bool animate);
    void setColorScheme(ColorScheme scheme);

    // Export
    void exportToSVG(const std::string& path);
    void exportToPNG(const std::string& path, int width, int height);
};

class GraphMonitor {
public:
    void attachToGraph(const AudioGraph* graph);
    void detach();

    void enableMetric(MetricType type);
    void disableMetric(MetricType type);

    NodeMetrics getNodeMetrics(NodeID node) const;
    std::vector<NodeMetrics> getAllMetrics() const;
};

Platform Backends: 

// Abstract render target
class RenderTarget {
public:
    virtual void drawNode(const NodeVisual& node) = 0;
    virtual void drawEdge(const EdgeVisual& edge) = 0;
    virtual void drawText(const std::string& text, Point pos) = 0;
    virtual void present() = 0;
};

// Implementations
class OpenGLRenderTarget : public RenderTarget { };
class Direct2DRenderTarget : public RenderTarget { };
class SVGRenderTarget : public RenderTarget { };

ENTREGABLES:

  • GraphRenderer with 3 layout algorithms
  • NodeVisual and EdgeVisual structures
  • GraphMonitor for real-time metrics
  • Interactive features (select, edit, drag)
  • Export to SVG/PNG
  • OpenGL render backend
  • 10+ unit tests
  • Visual test suite
  • Performance benchmarks
  • User guide with examples

TAREAS DE INTEGRACIÓN Y FINALIZACIÓN

TAREA FINAL-A: Integration Testing & Validation

Carpeta: 05_11_test_integration Prioridad: CRÍTICA Dependencias: Todas las tareas anteriores Estimación: 4-6 semanas

DESARROLLO:

1. End-to-End Test Suite

E2E Test Scenarios: - test_simple_audio_chain() - Input → Gain → Filter → Output - test_parallel_effects() - Split → [Reverb, Delay] → Mix - test_feedback_loop() - Delay with feedback - test_multirate_chain() - 4x oversample → Saturator → downsample - test_dynamic_reconfiguration() - Hot-swap effects - test_complex_graph() - 50+ nodes multi-path - test_subgraph_integration() - Usar subgraph templates

Audio Quality Validation: - THD+N measurement - Frequency response analysis - Phase coherence verification - Latency measurement - Glitch detection

Real-World Scenarios: - Channel strip (gain + EQ + comp + gate) - Mastering chain (multiband + limiter + dither) - Modular synthesizer (oscs + filters + envs) - Effect rack (multiple parallel/serial effects)

2. Cross-Subsystem Validation

Validaciones con otros subsistemas: - 00_catalog: Nodes registrados correctamente - 01_hierarchy: Niveles L0-L3 funcionan como nodos - 02_dependency_graph: Dependencies correctas - 05_topology_design: Topologías válidas - 06_optimization_layer: Optimizaciones aplicadas - 10_cells_l2: Células como nodos principales - 12_calibration: Calibración de grafos - 13_engines_l3: Engines como grafos - 30_testing_framework: Tests integrados - 31_observability: Métricas reportadas

3. Regression Test Automation

Automated Regression Suite: - Nightly builds with full test suite - Performance regression detection - Audio output verification (golden files) - Memory leak detection - Crash reporting

CI/CD Integration: 

# .github/workflows/graph_system_tests.yml
name: Graph System Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: [windows-latest, ubuntu-latest, macos-latest]
    steps:
      - name: Build
      - name: Unit Tests
      - name: Integration Tests
      - name: Performance Tests
      - name: Audio Quality Tests
      - name: Memory Tests

4. Performance Validation Suite

Performance Benchmarks: - Small graph (10 nodes): <1% CPU overhead - Medium graph (50 nodes): <3% CPU overhead - Large graph (100 nodes): <5% CPU overhead - Parallel speedup: 3x on 4-core - Memory efficiency: 50%+ reduction via pooling - Latency compensation: ±1 sample accuracy

5. Stress & Load Testing

Stress Tests: - 1000+ node graph - Rapid reconfiguration (100 changes/sec) - Long-running stability (24h+ continuous) - Memory stress (create/destroy 10000 graphs) - Thread stress (100+ concurrent threads)

ENTREGABLES:

  • 50+ end-to-end tests
  • Cross-subsystem validation suite
  • Automated regression tests
  • CI/CD pipeline configuration
  • Performance validation suite
  • Stress test suite
  • Test coverage report (>90% system-wide)
  • Performance comparison report
  • Integration test documentation

TAREA FINAL-B: System Integration

Carpeta: 05_11_interfaces Prioridad: ALTA Dependencias: Todas las tareas de desarrollo Estimación: 3-4 semanas

DESARROLLO:

1. Conectores con Subsistemas Hermanos

Symlink Implementation: 

# Conexiones según documento de arquitectura
05_11_GRAPH_SYSTEM/
├── component_catalog/ → ../00_catalog_registry/
├── hierarchy/ → ../01_hierarchy_framework/
├── dependency_base/ → ../02_dependency_graph/
├── topology_theory/ → ../05_topology_design/
├── optimization/ → ../06_optimization_layer/
├── cell_nodes/ → ../10_cells_l2/
├── calibration/ → ../12_calibration_system/
├── engine_graphs/ → ../13_engines_l3/
├── graph_tests/ → ../30_testing_framework/graphs/
└── monitoring/ → ../31_observability_system/

Interface Adapters: 

// Adapter para cells L2
class CellNodeAdapter {
    static std::unique_ptr<GraphNode> wrapCell(std::unique_ptr<CellL2> cell);
};

// Adapter para engines L3
class EngineGraphAdapter {
    static AudioGraph createGraphFromEngine(const EngineL3& engine);
};

// Adapter para optimization layer
class OptimizationAdapter {
    static void applyOptimizations(AudioGraph& graph,
                                  const OptimizationHints& hints);
};

2. Event Bus Implementation

Event System: 

class GraphEventBus {
public:
    // Event types
    enum class EventType {
        NODE_ADDED,
        NODE_REMOVED,
        CONNECTION_MADE,
        CONNECTION_BROKEN,
        GRAPH_VALIDATED,
        OPTIMIZATION_APPLIED,
        LATENCY_CHANGED,
        ERROR_OCCURRED
    };

    // Subscribe
    void subscribe(EventType type, std::function<void(const GraphEvent&)> handler);
    void unsubscribe(EventType type, int subscriptionId);

    // Publish
    void publish(const GraphEvent& event);
};

Integration Events: 

// Para observability system
bus.subscribe(EventType::NODE_ADDED, [](const GraphEvent& e) {
    ObservabilitySystem::logEvent("Node added", e.nodeId);
});

// Para calibration system
bus.subscribe(EventType::GRAPH_VALIDATED, [](const GraphEvent& e) {
    CalibrationSystem::recalibrate(e.graphId);
});

3. Shared State Management

Graph Registry: 

class GraphRegistry {
    static GraphRegistry& getInstance();

    void registerGraph(const std::string& name, std::shared_ptr<AudioGraph> graph);
    std::shared_ptr<AudioGraph> getGraph(const std::string& name);
    void unregisterGraph(const std::string& name);

    std::vector<std::string> listGraphs() const;
};

Node Type Registry: 

class NodeTypeRegistry {
    using NodeFactory = std::function<std::unique_ptr<GraphNode>()>;

    void registerNodeType(const std::string& type, NodeFactory factory);
    std::unique_ptr<GraphNode> createNode(const std::string& type);

    bool isTypeRegistered(const std::string& type) const;
    std::vector<std::string> listTypes() const;
};

4. Communication Protocols

Serialization Protocol: 

// JSON serialization
class GraphSerializer {
    json serialize(const AudioGraph& graph);
    std::unique_ptr<AudioGraph> deserialize(const json& data);
};

// Binary serialization (más eficiente)
class BinaryGraphSerializer {
    std::vector<uint8_t> serialize(const AudioGraph& graph);
    std::unique_ptr<AudioGraph> deserialize(const std::vector<uint8_t>& data);
};

Network Protocol (futuro - distributed processing): 

// Remote graph control
class RemoteGraphController {
    void connectToRemote(const std::string& host, int port);
    void sendCommand(const GraphCommand& cmd);
    GraphStatus getStatus();
};

ENTREGABLES:

  • Symlinks implementados
  • Interface adapters para cada subsistema
  • Event bus system
  • Graph registry
  • Node type registry
  • Serialization protocols (JSON + Binary)
  • Integration test suite
  • Communication protocol documentation
  • Integration examples

TAREA FINAL-C: Documentation Package

Carpeta: 05_11_documentation Prioridad: ALTA Dependencias: Todo implementado Estimación: 3-4 semanas

DESARROLLO:

1. Complete API Reference

API Documentation Structure: 

05_11_documentation/api/
├── 00_graph_core.md
├── 01_topology_validation.md
├── 02_topological_sorting.md
├── 03_latency_management.md
├── 04_buffer_management.md
├── 05_routing_matrices.md
├── 06_multirate_processing.md
├── 07_subgraph_abstraction.md
├── 08_parallel_processing.md
├── 09_dynamic_reconfiguration.md
├── 10_graph_optimization.md
└── 11_visualization_system.md

Each API doc includes: - Class reference - Method signatures - Parameter descriptions - Return values - Exceptions - Examples - Performance notes - Thread-safety

2. Developer Guide

Developer Guide Contents: 

# Graph System Developer Guide

## Table of Contents
1. Introduction
2. Getting Started
   - Hello World Graph
   - Basic Operations
3. Core Concepts
   - Nodes and Edges
   - Ports and Connections
   - Processing Order
4. Advanced Topics
   - Parallel Processing
   - Multi-Rate Graphs
   - Dynamic Reconfiguration
5. Best Practices
   - Performance Optimization
   - Memory Management
   - Error Handling
6. Troubleshooting
   - Common Errors
   - Debugging Tools
7. Examples
   - Channel Strip
   - Effect Rack
   - Synthesizer Voice

3. User Manual

User Manual (for end users of tools built with Graph System): 

# Graph System User Manual

## For Audio Developers
- Building Your First Audio Graph
- Common Patterns
- Performance Tuning
- Visualization Tools

## For Plugin Developers
- Integrating Graph System
- Best Practices
- API Usage Examples

## For DAW Developers
- Hosting Graph-Based Plugins
- Graph Visualization
- Performance Monitoring

4. Migration Guides

Migration Documentation: 

# Migration Guides

## From Simple Audio Chain to Graph System
Guide for upgrading linear processing to graph-based

## From Other Graph Frameworks
- From JUCE AudioProcessorGraph
- From Max/MSP gen~
- From FAUST

## Version Migration
- 1.0 → 2.0 Breaking Changes
- Deprecation Policy

5. Architecture Diagrams

Diagram Types: - System architecture overview - Class hierarchy diagrams - Sequence diagrams for key operations - Data flow diagrams - State machine diagrams - Component interaction diagrams

Tools: - PlantUML for UML diagrams - Graphviz for graph visualizations - Mermaid for flowcharts - Draw.io for architecture diagrams

6. Example Projects

Example Suite: 1. simple_chain - Basic gain → filter → output 2. parallel_effects - Parallel processing with mix 3. feedback_delay - Feedback loop example 4. multiband_processor - Multi-rate processing 5. dynamic_fx_rack - Hot-swappable effects 6. channel_strip - Complete channel strip 7. modular_synth - Modular synthesis example 8. mastering_chain - Professional mastering chain

Each example includes: - Source code - Detailed comments - Build instructions - Expected output - Performance metrics

ENTREGABLES:

  • Complete API reference (1000+ pages)
  • Developer guide (200+ pages)
  • User manual (100+ pages)
  • Migration guides
  • 50+ architecture diagrams
  • 8+ example projects
  • Video tutorials (10+ videos)
  • Interactive documentation website
  • PDF export of all documentation

MÉTRICAS DE ÉXITO FINALES

Capacidad Técnica

  • Soporta grafos de 100+ nodos @ 48kHz, 512 samples
  • 100% detección de ciclos y validación de tipos
  • Compensación de latencia ±1 sample
  • 50%+ reducción memoria via buffer sharing
  • 3-4x speedup en hardware 8-core
  • <50ms reconfiguration time
  • Zero glitches en reconfiguración
  • 30%+ mejora CPU via optimización

Calidad de Código

  • >90% test coverage global
  • Zero memory leaks
  • Zero crashes en 24h stress test
  • Documentación completa
  • API clara y consistente

Developer Experience

  • 10x más rápido construir sistemas complejos
  • Visualización clara de cualquier grafo
  • 80%+ reusabilidad via subgraphs
  • Debugging eficiente con herramientas visuales

Performance

  • <1% overhead en grafo de 10 nodos
  • <3% overhead en grafo de 50 nodos
  • <5% overhead en grafo de 100 nodos
  • Linear scaling con número de nodos
  • Real-time safe (no allocations en audio thread)

CRONOGRAMA ESTIMADO

Fase 1: Fundamentos (Semanas 1-8)

  • Tarea 1: Graph Core

Fase 2: Validación y Ordenamiento (Semanas 9-16)

  • Tarea 2: Topology Validation
  • Tarea 3: Topological Sorting

Fase 3: Gestión de Recursos (Semanas 17-28)

  • Tarea 4: Latency Management
  • Tarea 5: Buffer Management

Fase 4: Routing Avanzado (Semanas 29-42)

  • Tarea 6: Routing Matrices
  • Tarea 7: Multi-Rate Processing
  • Tarea 8: Subgraph Abstraction

Fase 5: Optimización (Semanas 43-58)

  • Tarea 9: Parallel Processing
  • Tarea 10: Dynamic Reconfiguration
  • Tarea 11: Graph Optimization

Fase 6: Herramientas (Semanas 59-64)

  • Tarea 12: Visualization System

Fase 7: Integración Final (Semanas 65-76)

  • Tarea Final-A: Integration Testing
  • Tarea Final-B: System Integration
  • Tarea Final-C: Documentation Package

TOTAL: 76 semanas (~18 meses)

CONCLUSIÓN

El Graph System es la columna vertebral de toda la arquitectura DSP de AudioLab. Este plan de desarrollo proporciona una ruta clara y estructurada para implementar un sistema de grafos de clase mundial que permite:

✅ Flexibilidad ilimitada - Cualquier topología imaginable ✅ Seguridad garantizada - Validación automática de correctitud ✅ Performance óptimo - Paralelización y optimización automáticas ✅ Desarrollo rápido - 10x más productivo con subgraphs y herramientas ✅ Debugging eficiente - Visualización y monitoreo en tiempo real

El sistema resultante no solo será técnicamente superior, sino también developer-friendly y production-ready para proyectos de audio profesional del más alto nivel.

Generado: 2025-10-14 Autor: AudioLab Development Team Versión: 1.0.0