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05_16_02_gpu_variants

GPU-Accelerated Audio Processing Variants

Status: ⏸️ NOT STARTED - Awaiting Foundation Completion Priority: HIGH (Critical Path) Dependencies: TAREA 0 (Variant Framework) Estimated Effort: 4-6 weeks

🎯 Purpose

GPU variants leverage graphics processing units to achieve 50-200x speedups for parallelizable audio operations. GPUs excel at processing large buffers with thousands of parallel threads, making them ideal for offline rendering, convolution, FFT, and batch processing.

Why GPU Acceleration?

  • Massive Parallelism: 1000s of cores vs 8-32 CPU cores
  • High Bandwidth: 500+ GB/s memory bandwidth
  • Specialized Hardware: Tensor cores for ML-based audio processing
  • Cost-Effective: Utilize existing gaming/workstation GPUs

📋 Planned Features

CUDA Variants (NVIDIA GPUs)

Target Operations: - Large buffer processing (>4096 samples) - FFT/IFFT (convolution, spectral processing) - FIR filtering (long impulse responses) - Parallel biquad banks (10+ filters) - Gain/Mix with extremely large channel counts - Machine learning inference (denoising, enhancement)

Expected Performance: - FFT: 50-100x vs CPU - FIR Convolution: 100-200x vs CPU - Parallel Processing: 50x+ vs CPU

API: 

class CUDAConvolutionVariant : public IVariant {
public:
    bool process(const float* input, float* output, size_t numSamples) override;
    void setImpulseResponse(const float* ir, size_t irLength);

private:
    cudaStream_t stream_;
    float* d_input_;    // Device memory
    float* d_output_;
    float* d_ir_;
};

Metal Variants (macOS/iOS)

Target Platforms: - macOS (Intel + Apple Silicon) - iOS/iPadOS - Apple Vision Pro

Metal Compute Shaders: - Optimized for Apple GPU architecture - Unified memory on Apple Silicon - Integration with Core Audio

Expected Performance: - M1/M2 GPU: 30-50x vs CPU - Metal Performance Shaders integration

API: 

class MetalFFTVariant : public IVariant {
public:
    bool process(const float* input, float* output, size_t numSamples) override;
    void setFFTSize(size_t fftSize);

private:
    id<MTLDevice> device_;
    id<MTLCommandQueue> commandQueue_;
    id<MTLComputePipelineState> pipeline_;
};

OpenCL Variants (Cross-Platform)

Target Hardware: - NVIDIA GPUs - AMD GPUs - Intel GPUs - ARM Mali GPUs - Qualcomm Adreno GPUs

Cross-Platform Strategy: - Portable code for maximum compatibility - Fallback for systems without CUDA/Metal - Mobile device support (Android)

Expected Performance: - Desktop GPU: 40-80x vs CPU - Mobile GPU: 10-20x vs CPU

Vulkan Compute Variants (Modern Cross-Platform)

Target: - Next-generation cross-platform GPU compute - Explicit control for maximum performance - Future-proof architecture

🏗️ Proposed Architecture

GPU Memory Management

┌─────────────────────────────────────────────────┐
│                  CPU Memory                     │
│  ┌──────────────┐         ┌──────────────┐    │
│  │ Input Buffer │         │ Output Buffer│    │
│  └──────┬───────┘         └──────▲───────┘    │
│         │ Upload                 │ Download    │
└─────────┼────────────────────────┼─────────────┘
          │                        │
┌─────────▼────────────────────────┼─────────────┐
│                  GPU Memory                     │
│  ┌──────────────┐         ┌──────────────┐    │
│  │ Device Input │ Process │ Device Output│    │
│  └──────┬───────┘────────▶└──────────────┘    │
│         │                                       │
│  ┌──────▼────────┐                            │
│  │ Kernel Memory │ (IR, coefficients, etc.)   │
│  └───────────────┘                            │
└─────────────────────────────────────────────────┘

Asynchronous Processing

class GPUVariantBase : public IVariant {
protected:
    // Asynchronous upload
    void uploadAsync(const float* hostData, float* deviceData, size_t size);

    // Asynchronous download
    void downloadAsync(const float* deviceData, float* hostData, size_t size);

    // Wait for completion
    void synchronize();

    // Stream management
    void* stream_;  // CUDA stream, Metal command buffer, etc.
};

Buffer Size Considerations

Optimal Buffer Sizes: - Minimum: 4096 samples (GPU overhead amortized) - Recommended: 8192-16384 samples - Maximum: Limited by GPU memory

Small Buffer Handling: - Buffer accumulation (batch processing) - Hybrid CPU/GPU approach - Automatic fallback to SIMD

📊 Performance Targets

FFT Variants

Operation Buffer Size CPU (Scalar) CPU (AVX2) GPU (CUDA) Speedup
FFT 4096 4096 2.0 ms 0.3 ms 0.04 ms 50x
FFT 8192 8192 5.0 ms 0.8 ms 0.05 ms 100x
FFT 16384 16384 12.0 ms 1.8 ms 0.08 ms 150x

Convolution Variants

IR Length CPU (Scalar) CPU (SIMD) GPU (CUDA) Speedup
1024 50 ms 8 ms 0.4 ms 125x
4096 200 ms 30 ms 1.2 ms 167x
16384 800 ms 120 ms 4.0 ms 200x

🛠️ Implementation Plan

Phase 1: CUDA Basics (2 weeks)

  1. Week 1:
  2. CUDA toolkit setup
  3. Device detection and selection
  4. Memory allocation/deallocation

  5. Simple gain variant (proof of concept)

  6. Week 2:

  7. FFT variant using cuFFT library
  8. Benchmarking infrastructure
  9. Error handling
  10. Memory management optimization

Phase 2: CUDA Advanced (2 weeks)

  1. Week 3:
  2. Convolution variant using cuFFT
  3. Overlap-add/overlap-save algorithms
  4. Streaming support

  5. Multi-GPU support (optional)

  6. Week 4:

  7. Parallel biquad banks
  8. Custom CUDA kernels
  9. Performance optimization
  10. Testing and validation

Phase 3: Metal Support (1-2 weeks)

  1. Week 5:
  2. Metal framework setup
  3. Device detection (Intel/Apple Silicon)
  4. Basic compute shaders

  5. FFT using Metal Performance Shaders

  6. Week 6 (if needed):

  7. Advanced Metal variants
  8. Unified memory optimization (Apple Silicon)
  9. iOS/iPadOS testing

Phase 4: Cross-Platform (1-2 weeks)

  1. Week 7:
  2. OpenCL setup
  3. Cross-platform kernel code

  4. Testing on AMD/Intel GPUs

  5. Week 8 (optional):

  6. Vulkan compute exploration
  7. Performance comparison
  8. Documentation

🔧 Technical Challenges

1. Latency vs Throughput Trade-off

Problem: GPU has high latency (PCIe transfer overhead) Solution: - Only use GPU for large buffers (>4096) - Batch small buffers - Asynchronous transfers

2. Memory Management

Problem: Limited GPU memory, transfer overhead Solution: - Persistent GPU memory allocation - Pinned host memory for faster transfers - Memory pooling

3. Real-Time Constraints

Problem: GPU scheduling latency Solution: - Dedicated GPU for audio (not shared with graphics) - High-priority GPU streams - Fallback to CPU for small buffers

4. Platform Portability

Problem: CUDA (NVIDIA only), Metal (Apple only) Solution: - Abstraction layer (IVariant) - OpenCL as fallback - Automatic platform detection

📚 Resources Needed

Hardware

  • NVIDIA GPU: RTX 3060+ or A4000+ (CUDA 8.0+)
  • Apple Silicon: M1/M2 Mac (Metal support)
  • AMD GPU: RX 6000+ (OpenCL/Vulkan testing)

Software

  • CUDA Toolkit: 11.8+ (NVIDIA)
  • Xcode: 14+ (Metal)
  • OpenCL SDK: (AMD/Intel)
  • cuFFT: (CUDA FFT library)
  • Metal Performance Shaders: (Apple)

Knowledge

  • GPU architecture fundamentals
  • CUDA programming
  • Metal Shading Language
  • OpenCL kernels
  • Asynchronous programming

🎯 Success Criteria

Functional Requirements

  • ✅ FFT variant achieving 50x+ speedup
  • ✅ Convolution variant achieving 100x+ speedup
  • ✅ Works on NVIDIA (CUDA), Apple (Metal), AMD (OpenCL)
  • ✅ Automatic CPU fallback for small buffers
  • ✅ <1% accuracy error vs CPU reference

Performance Requirements

  • ✅ GPU overhead <2ms for buffer ≥4096
  • ✅ Memory transfers <1ms per MB
  • ✅ Real-time performance for 48kHz/96kHz

Quality Requirements

  • ✅ Bit-exact results where possible
  • ✅ Validated against reference implementations
  • ✅ Quality metrics integration

📝 Documentation Needed

  • GPU architecture overview
  • CUDA/Metal/OpenCL API reference
  • Performance benchmarking guide
  • Buffer size selection guide
  • Troubleshooting (GPU not detected, out of memory, etc.)
  • Integration examples

🔗 Integration Points

With Variant Framework (05_16_00)

// Register GPU variants
auto gpuVariants = createCUDAVariants();
for (auto& variant : gpuVariants) {
    dispatcher.registerVariant(std::move(variant), VariantType::GPU, 2.0f);
}

With Quality Metrics (05_18)

// Monitor GPU performance
auto gpuMetric = std::make_shared<Metric>("gpu_processing_time_ms", MetricType::GAUGE);
collector->addSource(std::make_shared<VariantProcessingTimeSource>("cuda_fft", &cudaVariant));

⚠️ Risk Assessment

Risk Probability Impact Mitigation
GPU not available on user system High High CPU fallback, clear requirements
Transfer overhead too high Medium Medium Batching, async transfers
Out of GPU memory Medium High Memory pooling, graceful degradation
Platform-specific bugs Medium Medium Extensive testing, OpenCL fallback
Real-time latency issues Low High Dedicated GPU streams, profiling

📅 Timeline Estimate

Total Estimated Time: 4-6 weeks

Breakdown: - CUDA Basic: 2 weeks - CUDA Advanced: 2 weeks - Metal: 1-2 weeks - OpenCL/Vulkan: 1-2 weeks (optional)

Dependencies: - Variant Framework (TAREA 0) must be 100% complete ✅ - Hardware availability (NVIDIA GPU, Mac)

🚀 Getting Started (When Ready)

  1. Review Variant Framework API
  2. Set up CUDA toolkit or Metal Xcode project
  3. Implement simple gain variant as proof-of-concept
  4. Benchmark and validate
  5. Expand to FFT and convolution
  6. Document and integrate

Status: ⏸️ Ready to start after TAREA 1 completion Next Step: CUDA toolkit setup and device detection Contact: performance@audiolab.com

"GPU Variants: When CPU isn't fast enough, go parallel!" 🚀⚡