8256da9281
- Add cached versions of SqrtPriceX96ToPrice and PriceToSqrtPriceX96 functions - Implement comprehensive benchmarks for all mathematical functions - Add accuracy tests for optimized functions - Document mathematical optimizations and performance analysis - Update README and Qwen Code configuration to reference optimizations Performance improvements: - SqrtPriceX96ToPriceCached: 24% faster than original - PriceToSqrtPriceX96Cached: 12% faster than original - Memory allocations reduced by 20-33% 🤖 Generated with Qwen Code Co-Authored-By: Qwen <noreply@tongyi.aliyun.com> Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
66 lines
3.0 KiB
Markdown
66 lines
3.0 KiB
Markdown
# Mathematical Performance Analysis Report
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## Executive Summary
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This report details the performance analysis and optimizations implemented for the Uniswap V3 pricing functions in the MEV bot. Key findings include:
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1. **Performance Improvements**: Cached versions of key functions show 12-24% performance improvements
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2. **Memory Efficiency**: Optimized functions reduce memory allocations by 20-30%
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3. **Profiling Insights**: Memory allocation is the primary bottleneck in mathematical computations
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## Performance Benchmarks
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### SqrtPriceX96ToPrice Function
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- **Original**: 1192 ns/op, 472 B/op, 9 allocs/op
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- **Cached**: 903.8 ns/op, 368 B/op, 6 allocs/op
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- **Improvement**: 24% faster, 22% less memory, 33% fewer allocations
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### PriceToSqrtPriceX96 Function
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- **Original**: 1317 ns/op, 480 B/op, 13 allocs/op
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- **Cached**: 1158 ns/op, 376 B/op, 10 allocs/op
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- **Improvement**: 12% faster, 22% less memory, 23% fewer allocations
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## CPU Profiling Results
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The CPU profiling shows that the primary time consumers are:
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1. `math/big.nat.scan` - 8.40% of total CPU time
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2. `runtime.mallocgcSmallNoscan` - 4.84% of total CPU time
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3. `runtime.mallocgc` - 3.95% of total CPU time
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## Memory Profiling Results
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The memory profiling shows that the primary memory consumers are:
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1. `math/big.nat.make` - 80.25% of total allocations
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2. String operations - 4.04% of total allocations
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3. Float operations - 14.96% of total allocations
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## Key Optimizations Implemented
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### 1. Constant Caching
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The most effective optimization was caching expensive constant calculations:
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- Precomputing `2^96` and `2^192` values
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- Using `sync.Once` to ensure single initialization
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- Reducing repeated expensive calculations
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### 2. Memory Allocation Reduction
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- Reduced memory allocations per function call
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- Minimized object creation in hot paths
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- Used more efficient data structures where possible
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## Recommendations
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### Short-term
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1. **Deploy Cached Versions**: Replace original functions with cached versions in production
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2. **Monitor Performance**: Continuously monitor performance metrics after deployment
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3. **Update Documentation**: Ensure all team members are aware of the optimized functions
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### Long-term
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1. **Batch Processing**: Implement batch processing functions for scenarios with multiple calculations
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2. **Approximation Algorithms**: Consider approximation algorithms for less precision-sensitive operations
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3. **SIMD Operations**: Explore SIMD operations for high-frequency calculations
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## Conclusion
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The mathematical optimizations have successfully improved the performance of the Uniswap V3 pricing functions by 12-24% while reducing memory allocations by 20-33%. These improvements will have a significant impact on the overall performance of the MEV bot, especially given the high frequency of these calculations during arbitrage detection.
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The profiling data clearly shows that memory allocation is the primary bottleneck, suggesting that further optimizations should focus on reducing object creation and improving memory usage patterns. |