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- Add SQLCipher encryption for ait-mainnet database with configurable flag - Add db_encryption_enabled and db_encryption_key_path config settings - Implement encryption key loading and PRAGMA key setup via connection events - Add shutdown_db function for proper database cleanup - Export middleware classes in aitbc/__init__.py - Fix import path in sync.py for settings - Remove duplicate agent documentation from docs
71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# GPU Acceleration Research for ZK Circuits
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## Current GPU Hardware
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- GPU: NVIDIA GeForce RTX 4060 Ti
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- Memory: 16GB GDDR6
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- CUDA Capability: 8.9 (Ada Lovelace architecture)
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## Potential GPU-Accelerated ZK Libraries
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### 1. Halo2 (Recommended)
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- **Language**: Rust
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- **GPU Support**: Native CUDA acceleration
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- **Features**:
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- Lookup tables for efficient constraints
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- Recursive proofs
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- Multi-party computation support
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- Production-ready for complex circuits
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### 2. Arkworks
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- **Language**: Rust
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- **GPU Support**: Limited, but extensible
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- **Features**:
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- Modular architecture
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- Multiple proof systems (Groth16, Plonk)
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- Active ecosystem development
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### 3. Plonk Variants
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- **Language**: Rust/Zig
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- **GPU Support**: Some implementations available
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- **Features**:
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- Efficient for large circuits
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- Better constant overhead than Groth16
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### 4. Custom CUDA Implementation
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- **Approach**: Direct CUDA kernels for ZK operations
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- **Complexity**: High development effort
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- **Benefits**: Maximum performance optimization
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## Implementation Strategy
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### Phase 1: Research & Prototyping
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1. Set up Rust development environment
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2. Install Halo2 and benchmark basic operations
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3. Compare performance vs current CPU implementation
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4. Identify integration points with existing Circom circuits
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### Phase 2: Integration
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1. Create Rust bindings for existing circuits
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2. Implement GPU-accelerated proof generation
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3. Benchmark compilation speed improvements
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4. Test with modular ML circuits
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### Phase 3: Optimization
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1. Fine-tune CUDA kernels for ZK operations
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2. Implement batched proof generation
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3. Add support for recursive proofs
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4. Establish production deployment pipeline
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## Expected Performance Gains
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- Circuit compilation: 5-10x speedup
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- Proof generation: 3-5x speedup
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- Memory efficiency: Better utilization of GPU resources
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- Scalability: Support for larger, more complex circuits
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## Next Steps
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1. Install Rust and CUDA toolkit
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2. Set up Halo2 development environment
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3. Create performance baseline with current CPU implementation
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4. Begin prototyping GPU-accelerated proof generation
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