Performance Tuning
Maximize the performance of your quantitative finance applications with advanced optimization techniques and best practices.
Achieve 10-100x Speedups
Learn to profile, optimize, and scale your VectorAlpha applications. We'll cover everything from micro-optimizations to distributed processing, with real examples showing dramatic performance improvements.
Step 1: Profile First, Optimize Second
The golden rule of optimization: measure before you optimize. Let's start with profiling tools.
CPU Profiling with Flamegraph
# Install flamegraph tools
cargo install flamegraph
# Profile your application
cargo flamegraph --bin my_trading_bot
# For release builds (recommended)
cargo flamegraph --release --bin my_trading_bot
# With specific workload
cargo flamegraph --release -- backtest --data historical.csvAlternative: Using perf directly
# Record performance data
perf record -F 99 -g target/release/my_trading_bot
# Generate flamegraph
perf script | inferno-collapse-perf | inferno-flamegraph > flamegraph.svgBenchmarking with Criterion
Criterion provides statistical analysis to ensure your benchmarks are meaningful:
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Configuration examples will be available in the next update.
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Step 2: Compiler Optimizations
Before touching code, ensure you're using the right compiler settings:
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Configuration examples will be available in the next update.
Build Commands
# Standard release build
cargo build --release
# With CPU-specific optimizations
RUSTFLAGS="-C target-cpu=native" cargo build --release
# With ultra profile
cargo build --profile release-ultra
# Enable specific features
RUSTFLAGS="-C target-cpu=native -C target-feature=+avx2,+fma" cargo build --releaseStep 3: Algorithm and Data Structure Optimization
Choose the Right Data Structures
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Memory Allocation Optimization
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Step 4: Parallel Processing
Multi-threading with Rayon
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SIMD Optimization
Leverage SIMD for vectorized calculations:
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Step 5: Async I/O Optimization
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Full code examples with syntax highlighting will be available in the next update.
Real-World Performance Gains
VectorAlpha Optimization Results
| Optimization | Before | After | Speedup |
|---|---|---|---|
| Compiler flags | 100ms | 65ms | 1.5x |
| Data structure optimization | 65ms | 32ms | 2.0x |
| Parallel processing | 32ms | 8ms | 4.0x |
| SIMD optimization | 8ms | 2ms | 4.0x |
| Total improvement | 100ms | 2ms | 50x |
Results for calculating 5 indicators on 1M data points
Performance Checklist
✅ Before You Ship
- Enable release optimizations with
--release - Set
RUSTFLAGS="-C target-cpu=native" - Profile with realistic workloads
- Benchmark critical paths
- Minimize allocations in hot loops
- Use appropriate data structures
- Consider parallelization opportunities
- Test memory usage under load
Golden Rules
- Profile first - Don't guess where bottlenecks are
- Optimize algorithms before code - O(n) beats optimized O(n²)
- Memory is often the bottleneck - Cache-friendly code wins
- Measure everything - Use benchmarks to verify improvements
- Keep it maintainable - 2x speed isn't worth unreadable code