dda703de10
✅ v0.2 Release Preparation: - Update version to 0.2.0 in pyproject.toml - Create release build script for CLI binaries - Generate comprehensive release notes ✅ OpenClaw DAO Governance: - Implement complete on-chain voting system - Create DAO smart contract with Governor framework - Add comprehensive CLI commands for DAO operations - Support for multiple proposal types and voting mechanisms ✅ GPU Acceleration CI: - Complete GPU benchmark CI workflow - Comprehensive performance testing suite - Automated benchmark reports and comparison - GPU optimization monitoring and alerts ✅ Agent SDK Documentation: - Complete SDK documentation with examples - Computing agent and oracle agent examples - Comprehensive API reference and guides - Security best practices and deployment guides ✅ Production Security Audit: - Comprehensive security audit framework - Detailed security assessment (72.5/100 score) - Critical issues identification and remediation - Security roadmap and improvement plan ✅ Mobile Wallet & One-Click Miner: - Complete mobile wallet architecture design - One-click miner implementation plan - Cross-platform integration strategy - Security and user experience considerations ✅ Documentation Updates: - Add roadmap badge to README - Update project status and achievements - Comprehensive feature documentation - Production readiness indicators 🚀 Ready for v0.2.0 release with agent-first architecture
71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# GPU Acceleration Research for ZK Circuits
|
|
|
|
## Current GPU Hardware
|
|
- GPU: NVIDIA GeForce RTX 4060 Ti
|
|
- Memory: 16GB GDDR6
|
|
- CUDA Capability: 8.9 (Ada Lovelace architecture)
|
|
|
|
## Potential GPU-Accelerated ZK Libraries
|
|
|
|
### 1. Halo2 (Recommended)
|
|
- **Language**: Rust
|
|
- **GPU Support**: Native CUDA acceleration
|
|
- **Features**:
|
|
- Lookup tables for efficient constraints
|
|
- Recursive proofs
|
|
- Multi-party computation support
|
|
- Production-ready for complex circuits
|
|
|
|
### 2. Arkworks
|
|
- **Language**: Rust
|
|
- **GPU Support**: Limited, but extensible
|
|
- **Features**:
|
|
- Modular architecture
|
|
- Multiple proof systems (Groth16, Plonk)
|
|
- Active ecosystem development
|
|
|
|
### 3. Plonk Variants
|
|
- **Language**: Rust/Zig
|
|
- **GPU Support**: Some implementations available
|
|
- **Features**:
|
|
- Efficient for large circuits
|
|
- Better constant overhead than Groth16
|
|
|
|
### 4. Custom CUDA Implementation
|
|
- **Approach**: Direct CUDA kernels for ZK operations
|
|
- **Complexity**: High development effort
|
|
- **Benefits**: Maximum performance optimization
|
|
|
|
## Implementation Strategy
|
|
|
|
### Phase 1: Research & Prototyping
|
|
1. Set up Rust development environment
|
|
2. Install Halo2 and benchmark basic operations
|
|
3. Compare performance vs current CPU implementation
|
|
4. Identify integration points with existing Circom circuits
|
|
|
|
### Phase 2: Integration
|
|
1. Create Rust bindings for existing circuits
|
|
2. Implement GPU-accelerated proof generation
|
|
3. Benchmark compilation speed improvements
|
|
4. Test with modular ML circuits
|
|
|
|
### Phase 3: Optimization
|
|
1. Fine-tune CUDA kernels for ZK operations
|
|
2. Implement batched proof generation
|
|
3. Add support for recursive proofs
|
|
4. Establish production deployment pipeline
|
|
|
|
## Expected Performance Gains
|
|
- Circuit compilation: 5-10x speedup
|
|
- Proof generation: 3-5x speedup
|
|
- Memory efficiency: Better utilization of GPU resources
|
|
- Scalability: Support for larger, more complex circuits
|
|
|
|
## Next Steps
|
|
1. Install Rust and CUDA toolkit
|
|
2. Set up Halo2 development environment
|
|
3. Create performance baseline with current CPU implementation
|
|
4. Begin prototyping GPU-accelerated proof generation
|
|
|