hive-gpu 0.1.7

# 🚀 Hive-GPU Integration Roadmap

**Complete roadmap for implementing CUDA, Vulkan, and native GPU backends**

## 📋 Overview

This roadmap outlines the implementation strategy for expanding `hive-gpu` v0.1.0 with comprehensive GPU backend support, including CUDA, Vulkan, and native GPU APIs.

## 🎯 Current Status (v0.1.0)

### ✅ **Implemented:**
- **Metal Native**: Complete implementation for macOS
- **Basic Structure**: CUDA and wgpu placeholders
- **Core Traits**: GpuContext, GpuVectorStorage
- **Types**: GpuVector, GpuSearchResult, HnswConfig
- **Error Handling**: Comprehensive error types
- **CI/CD**: GitHub Actions pipeline
- **Documentation**: Complete English documentation

### ⚠️ **Pending:**
- **CUDA Implementation**: Full CUDA backend
- **Vulkan Support**: Vulkan compute shaders
- **wgpu Enhancement**: Complete wgpu implementation
- **Cross-platform**: Windows/Linux native support
- **Performance**: GPU-accelerated algorithms

## 🗺️ Roadmap Phases

## Phase 1: CUDA Implementation 

### 🎯 **Goals:**
- Complete CUDA backend implementation
- NVIDIA GPU support for Linux/Windows
- CUDA kernels for vector operations
- Memory management and optimization

### 📋 **Tasks:**

#### **1.1 CUDA Core Implementation**
- [ ] **CUDA Context** (`src/cuda/context.rs`)
  - [ ] Device detection and initialization
  - [ ] Memory pool management
  - [ ] Stream management
  - [ ] Error handling and recovery

- [ ] **CUDA Vector Storage** (`src/cuda/vector_storage.rs`)
  - [ ] GPU memory allocation
  - [ ] Vector data management
  - [ ] Batch operations
  - [ ] Memory optimization

- [ ] **CUDA Kernels** (`src/cuda/kernels/`)
  - [ ] Cosine similarity kernel
  - [ ] Euclidean distance kernel
  - [ ] Dot product kernel
  - [ ] HNSW construction kernel
  - [ ] HNSW search kernel

#### **1.2 CUDA Memory Management**
- [ ] **Buffer Pool** (`src/cuda/buffer_pool.rs`)
  - [ ] Dynamic memory allocation
  - [ ] Memory reuse and recycling
  - [ ] Fragmentation management
  - [ ] Memory monitoring

- [ ] **VRAM Monitor** (`src/cuda/vram_monitor.rs`)
  - [ ] Memory usage tracking
  - [ ] Performance metrics
  - [ ] Memory leak detection
  - [ ] Optimization suggestions

#### **1.3 CUDA HNSW Implementation**
- [ ] **HNSW Graph** (`src/cuda/hnsw_graph.rs`)
  - [ ] Graph construction on GPU
  - [ ] Parallel edge creation
  - [ ] Graph traversal
  - [ ] Memory-efficient storage

- [ ] **CUDA Helpers** (`src/cuda/helpers.rs`)
  - [ ] Kernel launch utilities
  - [ ] Memory transfer optimization
  - [ ] Error handling utilities
  - [ ] Performance profiling

### 🧪 **Testing & Validation:**
- [ ] Unit tests for CUDA operations
- [ ] Integration tests with vectorizer
- [ ] Performance benchmarks
- [ ] Memory leak testing
- [ ] Cross-platform compatibility

### 📊 **Success Metrics:**
- [ ] CUDA backend functional
- [ ] Performance > 10x CPU baseline
- [ ] Memory usage < 2GB for 100k vectors
- [ ] Zero memory leaks
- [ ] Full test coverage

---

## Phase 2: Vulkan Implementation 

### 🎯 **Goals:**
- Vulkan compute shader support
- Cross-platform GPU acceleration
- Vulkan memory management
- Compute pipeline optimization

### 📋 **Tasks:**

#### **2.1 Vulkan Core Implementation**
- [ ] **Vulkan Context** (`src/vulkan/context.rs`)
  - [ ] Instance and device creation
  - [ ] Queue family selection
  - [ ] Memory type detection
  - [ ] Extension support

- [ ] **Vulkan Vector Storage** (`src/vulkan/vector_storage.rs`)
  - [ ] Buffer management
  - [ ] Memory mapping
  - [ ] Synchronization
  - [ ] Performance optimization

#### **2.2 Vulkan Compute Shaders**
- [ ] **Shader Compilation** (`src/vulkan/shaders/`)
  - [ ] GLSL to SPIR-V compilation
  - [ ] Shader optimization
  - [ ] Cross-platform compatibility
  - [ ] Shader caching

- [ ] **Compute Pipelines** (`src/vulkan/pipelines/`)
  - [ ] Pipeline creation
  - [ ] Descriptor set management
  - [ ] Command buffer recording
  - [ ] Synchronization primitives

#### **2.3 Vulkan Kernels**
- [ ] **Similarity Kernels** (`src/vulkan/kernels/`)
  - [ ] Cosine similarity shader
  - [ ] Euclidean distance shader
  - [ ] Dot product shader
  - [ ] Batch processing shader

- [ ] **HNSW Kernels** (`src/vulkan/hnsw/`)
  - [ ] Graph construction shader
  - [ ] Graph traversal shader
  - [ ] Edge creation shader
  - [ ] Search optimization shader

#### **2.4 Vulkan Memory Management**
- [ ] **Buffer Management** (`src/vulkan/buffers.rs`)
  - [ ] Buffer allocation
  - [ ] Memory type selection
  - [ ] Buffer pooling
  - [ ] Memory optimization

- [ ] **Synchronization** (`src/vulkan/sync.rs`)
  - [ ] Fence management
  - [ ] Semaphore synchronization
  - [ ] Memory barriers
  - [ ] Pipeline barriers

### 🧪 **Testing & Validation:**
- [ ] Vulkan validation layers
- [ ] Cross-platform testing
- [ ] Performance benchmarking
- [ ] Memory usage analysis
- [ ] Shader compilation testing

### 📊 **Success Metrics:**
- [ ] Vulkan backend functional
- [ ] Cross-platform compatibility
- [ ] Performance > 5x CPU baseline
- [ ] Memory usage < 1.5GB for 100k vectors
- [ ] Zero validation errors

---

## Phase 3: Native GPU APIs 

### 🎯 **Goals:**
- DirectX 12 support for Windows
- Metal Performance Shaders for macOS
- OpenCL support for cross-platform
- Native API optimization

### 📋 **Tasks:**

#### **3.1 DirectX 12 Implementation**
- [ ] **DX12 Context** (`src/dx12/context.rs`)
  - [ ] Device creation
  - [ ] Command queue management
  - [ ] Memory heap management
  - [ ] Resource management

- [ ] **DX12 Compute** (`src/dx12/compute.rs`)
  - [ ] Compute shader execution
  - [ ] Resource binding
  - [ ] Pipeline state objects
  - [ ] Command list recording

#### **3.2 Metal Performance Shaders**
- [ ] **MPS Integration** (`src/mps/context.rs`)
  - [ ] MPS device creation
  - [ ] MPS graph construction
  - [ ] MPS kernel execution
  - [ ] Memory management

- [ ] **MPS Kernels** (`src/mps/kernels/`)
  - [ ] MPS matrix operations
  - [ ] MPS reduction operations
  - [ ] MPS convolution
  - [ ] MPS custom kernels

#### **3.3 OpenCL Support**
- [ ] **OpenCL Context** (`src/opencl/context.rs`)
  - [ ] Platform and device selection
  - [ ] Context creation
  - [ ] Command queue management
  - [ ] Memory management

- [ ] **OpenCL Kernels** (`src/opencl/kernels/`)
  - [ ] Kernel compilation
  - [ ] Kernel execution
  - [ ] Memory optimization
  - [ ] Performance tuning

### 🧪 **Testing & Validation:**
- [ ] Platform-specific testing
- [ ] Performance comparison
- [ ] Memory usage analysis
- [ ] Compatibility testing
- [ ] Stress testing

### 📊 **Success Metrics:**
- [ ] All native APIs functional
- [ ] Platform-specific optimization
- [ ] Performance > 15x CPU baseline
- [ ] Memory usage < 1GB for 100k vectors
- [ ] Full platform coverage

---

## Phase 4: Advanced Features 

### 🎯 **Goals:**
- Multi-GPU support
- Distributed computing
- Advanced algorithms
- Performance optimization

### 📋 **Tasks:**

#### **4.1 Multi-GPU Support**
- [ ] **GPU Detection** (`src/multi_gpu/detector.rs`)
  - [ ] Multiple GPU detection
  - [ ] GPU capability assessment
  - [ ] Load balancing
  - [ ] Failover support

- [ ] **Distributed Storage** (`src/multi_gpu/storage.rs`)
  - [ ] Vector distribution
  - [ ] Cross-GPU communication
  - [ ] Load balancing
  - [ ] Fault tolerance

#### **4.2 Advanced Algorithms**
- [ ] **Quantization** (`src/quantization/`)
  - [ ] Scalar quantization
  - [ ] Product quantization
  - [ ] Binary quantization
  - [ ] Mixed precision

- [ ] **Compression** (`src/compression/`)
  - [ ] Vector compression
  - [ ] Lossless compression
  - [ ] Lossy compression
  - [ ] Decompression

#### **4.3 Performance Optimization**
- [ ] **Kernel Optimization** (`src/optimization/`)
  - [ ] Kernel fusion
  - [ ] Memory coalescing
  - [ ] Shared memory usage
  - [ ] Register optimization

- [ ] **Memory Optimization** (`src/memory/`)
  - [ ] Memory pooling
  - [ ] Garbage collection
  - [ ] Memory defragmentation
  - [ ] Cache optimization

### 🧪 **Testing & Validation:**
- [ ] Multi-GPU testing
- [ ] Distributed testing
- [ ] Performance benchmarking
- [ ] Stress testing
- [ ] Fault tolerance testing

### 📊 **Success Metrics:**
- [ ] Multi-GPU support
- [ ] Distributed computing
- [ ] Advanced algorithms
- [ ] Performance > 20x CPU baseline
- [ ] Scalability to 1M+ vectors

---

## 🔧 Implementation Strategy

### **Architecture Principles:**

#### **1. Modular Design**
```rust
// Backend-agnostic interface
pub trait GpuBackend {
    fn create_context() -> Result<Box<dyn GpuContext>>;
    fn create_storage(dimension: usize, metric: GpuDistanceMetric) -> Result<Box<dyn GpuVectorStorage>>;
}

// Backend-specific implementations
pub struct CudaBackend { /* ... */ }
pub struct VulkanBackend { /* ... */ }
pub struct MetalBackend { /* ... */ }
```

#### **2. Unified API**
```rust
// Same API across all backends
let context = GpuContext::new(GpuBackendType::Cuda)?;
let storage = context.create_storage(128, GpuDistanceMetric::Cosine)?;
storage.add_vectors(&vectors)?;
let results = storage.search(&query, 10)?;
```

#### **3. Performance Optimization**
```rust
// Backend-specific optimizations
impl GpuVectorStorage for CudaVectorStorage {
    fn search(&self, query: &[f32], limit: usize) -> Result<Vec<GpuSearchResult>> {
        // CUDA-optimized search
        self.cuda_search_kernel(query, limit)
    }
}

impl GpuVectorStorage for VulkanVectorStorage {
    fn search(&self, query: &[f32], limit: usize) -> Result<Vec<GpuSearchResult>> {
        // Vulkan-optimized search
        self.vulkan_search_shader(query, limit)
    }
}
```

### **Development Workflow:**

#### **1. Feature Branches**
```bash
# CUDA implementation
git checkout -b feature/cuda-implementation
git checkout -b feature/cuda-kernels
git checkout -b feature/cuda-memory

# Vulkan implementation
git checkout -b feature/vulkan-implementation
git checkout -b feature/vulkan-shaders
git checkout -b feature/vulkan-memory

# Native APIs
git checkout -b feature/dx12-implementation
git checkout -b feature/mps-integration
git checkout -b feature/opencl-support
```

#### **2. Testing Strategy**
```rust
// Backend-specific tests
#[cfg(feature = "cuda")]
mod cuda_tests {
    #[test]
    fn test_cuda_vector_operations() { /* ... */ }
}

#[cfg(feature = "vulkan")]
mod vulkan_tests {
    #[test]
    fn test_vulkan_compute_shaders() { /* ... */ }
}
```

#### **3. CI/CD Integration**
```yaml
# CUDA testing
- name: Test CUDA
  run: cargo test --features cuda

# Vulkan testing
- name: Test Vulkan
  run: cargo test --features vulkan

# Cross-platform testing
- name: Test All Backends
  run: cargo test --all-features
```

## 📊 Performance Targets

### **Benchmark Goals:**

| Backend | Vectors/sec | Search Latency | Memory Usage | Platform |
|---------|-------------|----------------|--------------|----------|
| **CUDA** | 50,000+ | < 1ms | < 2GB | Linux/Windows |
| **Vulkan** | 30,000+ | < 2ms | < 1.5GB | Cross-platform |
| **Metal** | 40,000+ | < 1ms | < 1GB | macOS |
| **DX12** | 45,000+ | < 1ms | < 1.5GB | Windows |
| **OpenCL** | 25,000+ | < 3ms | < 2GB | Cross-platform |

### **Scalability Targets:**

| Metric | Target | Current | Goal |
|--------|--------|---------|------|
| **Max Vectors** | 1M+ | 10k | 1M+ |
| **Memory Efficiency** | < 2GB | N/A | < 2GB |
| **Search Speed** | < 1ms | N/A | < 1ms |
| **Throughput** | 50k/sec | N/A | 50k/sec |

## 🧪 Testing Strategy

### **Unit Tests:**
```rust
#[cfg(test)]
mod tests {
    use super::*;
    
    #[test]
    fn test_cuda_context_creation() {
        let context = CudaContext::new().unwrap();
        assert!(context.device_count() > 0);
    }
    
    #[test]
    fn test_vulkan_shader_compilation() {
        let shader = VulkanShader::new("similarity.comp").unwrap();
        assert!(shader.is_valid());
    }
    
    #[test]
    fn test_metal_performance_shaders() {
        let mps = MpsContext::new().unwrap();
        assert!(mps.is_available());
    }
}
```

### **Integration Tests:**
```rust
#[tokio::test]
async fn test_multi_backend_compatibility() {
    let backends = vec![
        GpuBackendType::Cuda,
        GpuBackendType::Vulkan,
        GpuBackendType::Metal,
    ];
    
    for backend in backends {
        let context = GpuContext::new(backend).await.unwrap();
        let storage = context.create_storage(128, GpuDistanceMetric::Cosine).unwrap();
        
        // Test basic operations
        let vectors = create_test_vectors();
        storage.add_vectors(&vectors).unwrap();
        
        let query = vec![1.0; 128];
        let results = storage.search(&query, 5).unwrap();
        assert_eq!(results.len(), 5);
    }
}
```

### **Performance Tests:**
```rust
#[bench]
fn bench_cuda_search(b: &mut Bencher) {
    let context = CudaContext::new().unwrap();
    let storage = context.create_storage(512, GpuDistanceMetric::Cosine).unwrap();
    
    // Add test vectors
    let vectors = create_large_vector_set(10000);
    storage.add_vectors(&vectors).unwrap();
    
    b.iter(|| {
        let query = create_random_query();
        storage.search(&query, 10).unwrap()
    });
}
```

## 📈 Milestones

### **Q1 2024: CUDA Implementation**
- [ ] **Week 1-2**: CUDA context and memory management
- [ ] **Week 3-4**: CUDA kernels for basic operations
- [ ] **Week 5-6**: CUDA HNSW implementation
- [ ] **Week 7-8**: Testing and optimization
- [ ] **Week 9-10**: Performance benchmarking
- [ ] **Week 11-12**: Documentation and release

### **Q2 2024: Vulkan Implementation**
- [ ] **Week 1-2**: Vulkan context and device management
- [ ] **Week 3-4**: Vulkan compute shaders
- [ ] **Week 5-6**: Vulkan memory management
- [ ] **Week 7-8**: Vulkan HNSW implementation
- [ ] **Week 9-10**: Cross-platform testing
- [ ] **Week 11-12**: Performance optimization

### **Q3 2024: Native APIs**
- [ ] **Week 1-4**: DirectX 12 implementation
- [ ] **Week 5-8**: Metal Performance Shaders
- [ ] **Week 9-12**: OpenCL support

### **Q4 2024: Advanced Features**
- [ ] **Week 1-4**: Multi-GPU support
- [ ] **Week 5-8**: Advanced algorithms
- [ ] **Week 9-12**: Performance optimization

## 🎯 Success Criteria

### **Technical Goals:**
- [ ] **CUDA**: 50k+ vectors/sec, < 1ms latency
- [ ] **Vulkan**: 30k+ vectors/sec, < 2ms latency
- [ ] **Metal**: 40k+ vectors/sec, < 1ms latency
- [ ] **DX12**: 45k+ vectors/sec, < 1ms latency
- [ ] **OpenCL**: 25k+ vectors/sec, < 3ms latency

### **Quality Goals:**
- [ ] **Test Coverage**: > 90%
- [ ] **Memory Safety**: Zero leaks
- [ ] **Error Handling**: Comprehensive
- [ ] **Documentation**: Complete
- [ ] **Performance**: > 10x CPU baseline

### **User Experience:**
- [ ] **Easy Integration**: Simple API
- [ ] **Cross-platform**: Works everywhere
- [ ] **Performance**: Fast and efficient
- [ ] **Reliability**: Stable and robust
- [ ] **Documentation**: Clear and complete

---

## 🚀 Getting Started

### **For Contributors:**
1. **Choose a backend** from the roadmap
2. **Create a feature branch** for your implementation
3. **Follow the architecture** principles
4. **Write comprehensive tests** for your code
5. **Submit a pull request** with your implementation

### **For Users:**
1. **Check the roadmap** for your desired backend
2. **Follow the documentation** for integration
3. **Test with your use case** and provide feedback
4. **Report issues** and suggest improvements

---

**This roadmap ensures `hive-gpu` becomes the most comprehensive and performant GPU acceleration library for vector operations! 🚀**