05_11_08_parallel_processing - Procesamiento Paralelo

Estado: 📋 Pendiente Prioridad: ALTA Estimación: 6-8 semanas Dependencias: 00_graph_core, 02_topological_sorting

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🎯 PROPÓSITO

El Parallel Processing identifica y ejecuta ramas independientes del grafo simultáneamente para mejor utilización de CPU multi-core:

• 🚀 Detecta branches paralelas automáticamente
• 🚀 Thread pool con work stealing
• 🚀 SIMD processing de branches similares
• 🚀 3-4x speedup típico en 8-core

Sin paralelización: 100% de 1 core, resto idle Con paralelización: 75% de 4+ cores, throughput masivo

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📐 COMPONENTES

1. Parallel Branch Detector

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

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

Algorithm: 1. Group nodes by dependency level 2. Within each level, check for inter-dependencies 3. Nodes at same level with no paths between them → parallel

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();
};

Features: - Pre-allocated worker threads - Lock-free task queue (optional) - Work stealing for load balance - Thread-local storage for cache efficiency

3. Parallel Graph Executor

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

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

Execution Strategy: 1. Launch parallel branches on separate threads 2. Wait for all to complete (barrier) 3. Process serial dependencies 4. Repeat

4. SIMD Branch Processing

// Process 8 mono channels with single SIMD instruction
__m256 input = _mm256_load_ps(inputs);
__m256 output = processAVX(input, params);
_mm256_store_ps(outputs, output);

Requirements: - Identical processing algorithm - Same parameters (or batch) - Aligned memory (32-byte)

5. Work Stealing Scheduler

class WorkStealingScheduler {
    std::vector<std::deque<Task>> perThreadQueues;

    void enqueue(Task task);
    Task steal();  // From other thread's queue
};

Benefit: Dynamic load balancing - Busy threads steal work from idle threads - Minimizes idle time - Adapts to varying node complexity

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📂 ESTRUCTURA

05_11_08_parallel_processing/
├── include/
│   ├── ParallelDetector.h
│   ├── ThreadPool.h
│   ├── ParallelGraphExecutor.h
│   ├── SIMDProcessor.h
│   └── WorkStealingScheduler.h
├── src/
│   ├── ParallelDetector.cpp
│   ├── ThreadPool.cpp
│   ├── ParallelGraphExecutor.cpp
│   ├── SIMDProcessor.cpp
│   └── WorkStealingScheduler.cpp
├── tests/
│   ├── test_parallel_detection.cpp
│   ├── test_thread_pool.cpp
│   ├── test_parallel_execution.cpp
│   ├── test_simd_processing.cpp
│   └── test_correctness.cpp        # Serial vs Parallel match
├── examples/
│   ├── parallel_effects.cpp
│   ├── multiband_parallel.cpp
│   └── benchmarks.cpp
└── docs/
    ├── PARALLELIZATION_THEORY.md
    ├── PERFORMANCE_ANALYSIS.md
    └── TUNING_GUIDE.md

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🎯 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

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📊 MÉTRICAS DE ÉXITO

Speedup (vs serial execution)

• ✅ 2-core: 1.8x típico (90% efficiency)
• ✅ 4-core: 3.0x típico (75% efficiency)
• ✅ 8-core: 5.0x típico (62% efficiency)

Scalability

• ✅ Linear hasta 4 cores
• ✅ Sub-linear pero positivo 4-8 cores
• ✅ Diminishing returns >8 cores (dependencies)

Overhead

• ✅ <5% overhead vs optimal hand-tuned
• ✅ Thread creation: One-time at startup
• ✅ Sync overhead: <1% of processing time

Correctness

• ✅ Bit-exact match with serial execution
• ✅ Deterministic output (given same input)
• ✅ Zero data races (Valgrind/TSan clean)

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📈 EXPECTED PERFORMANCE

Graph with 40% parallelizable work

Amdahl's Law: Speedup = 1 / ((1-P) + P/N)

Cores   Theoretical   Typical   Efficiency
  1        1.0x        1.0x       100%
  2        1.43x       1.3x        65%
  4        1.82x       1.6x        40%
  8        2.11x       1.8x        22%

Graph with 80% parallelizable work

Cores   Theoretical   Typical   Efficiency
  1        1.0x        1.0x       100%
  2        1.82x       1.7x        85%
  4        3.08x       2.6x        65%
  8        4.71x       3.8x        47%

Factors affecting real-world performance: - Dependency density - Node complexity variance - Cache effects - Sync overhead - Memory bandwidth

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🧪 BENCHMARKS

Test Scenarios

1. Diamond Graph

   Input → Split → [Path A, Path B] → Merge → Output
   

2. Expected: 1.9x on 2-core

3. Work: 50% parallelizable

4. Multi-Band Processor

   Input → Split → [Band1, Band2, Band3, Band4] → Merge → Output
   

5. Expected: 3.5x on 4-core

6. Work: 90% parallelizable

7. Effect Rack

   Input → [Reverb, Delay, Chorus, Flanger] → Mix → Output
   

8. Expected: 3.8x on 4-core
9. Work: 95% parallelizable

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💡 OPTIMIZATION TIPS

When Parallelization Helps

✅ Multiple independent branches ✅ CPU-heavy nodes (convolution, FFT) ✅ Similar-cost nodes (balanced load) ✅ Sufficient work per node (>100μs)

When Parallelization Hurts

❌ Mostly serial dependencies ❌ Very fast nodes (<10μs each) ❌ Highly imbalanced load ❌ Memory-bound processing

Best Practices

• Group small nodes into larger chunks
• Balance work across threads
• Minimize sync points
• Use thread-local storage
• Align data for SIMD

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🔧 CONFIGURATION

ParallelGraphExecutor executor;

// Thread count (0 = auto-detect)
executor.setThreadCount(4);

// Enable/disable features
executor.enableWorkStealing(true);
executor.enableSIMD(true);

// Tuning
executor.setMinNodeCostForParallel(100);  // μs
executor.setMaxThreads(8);

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Creado: 2025-10-14 Versión: 1.0.0