Week 19: Performance Analysis and Benchmarking (Advanced)

Overview

Week 19 focuses on measuring, analyzing, and optimizing Rust code performance. You’ll learn how to use benchmarking frameworks, profiling tools, and performance analysis techniques to identify bottlenecks and improve efficiency. These skills are crucial for developing high-performance systems and applications.

Day 1-3: Benchmarking Frameworks

Topics

  • Introduction to benchmarking:
    • Methodology and best practices
    • Common pitfalls in benchmarking
    • Micro vs macro benchmarks
    • Statistical significance
  • The benchmark ecosystem:
    • Standard library benchmarks (unstable)
    • Criterion.rs
    • Iai (performance regression testing)
    • Bencher crate
    • Benchmark harness alternatives
  • Designing meaningful benchmarks:
    • Isolating performance factors
    • Controlling environmental variables
    • Avoiding measurement bias
    • Realistic workloads
    • Reproducible results
  • Statistical analysis of benchmarks:
    • Interpreting benchmark results
    • Variance and outliers
    • Comparing performance between versions
    • Throughput vs latency measurements
    • Parameterized benchmarks
  • Continuous benchmarking:
    • Setting up CI for performance testing
    • Detecting performance regressions
    • Reporting and visualization
    • GitHub Actions for benchmarking

Resources

Use Cases

  • Performance regression testing
  • Algorithm comparison
  • Library optimization
  • Hardware selection
  • Resource scaling planning
  • Comparing implementation strategies
  • Performance budgeting

Day 4-7: Profiling Tools and Techniques

Topics

  • CPU profiling:
    • Sampling vs instrumentation profiling
    • Perf on Linux
    • DTrace on macOS
    • ETW on Windows
    • Flamegraphs for visualization
    • Call graph analysis
  • Memory profiling:
    • Allocation tracking
    • Heap profiling
    • Cache behavior analysis
    • DHAT (Dynamic Heap Analysis Tool)
    • Valgrind tools
  • Platform-specific profiling:
    • CPU performance counters
    • Branch prediction analysis
    • Instruction-level profiling
    • Cache miss analysis
    • Hardware performance events
  • Profiling specialized workloads:
    • I/O-bound applications
    • Networking performance
    • Multi-threaded code
    • GPU utilization
    • Energy profiling
  • Interpreting profiling data:
    • Identifying hot spots
    • Recognizing patterns
    • Common performance issues
    • Correlating profiles with code

Resources

Use Cases

  • Finding performance bottlenecks
  • Optimizing memory usage
  • Reducing CPU consumption
  • Improving I/O performance
  • Identifying lock contention
  • Cache optimization
  • Reducing energy consumption

Exercises

  1. Set up Criterion.rs to benchmark a set of algorithms with statistical analysis
  2. Create a benchmark suite for a data structure implementation
  3. Generate and analyze flamegraphs for a CPU-bound application
  4. Profile memory usage patterns and optimize allocations
  5. Set up continuous benchmarking in CI for a project
  6. Use performance counters to analyze cache behavior of different algorithms

Advanced Challenges

  1. Implement a custom benchmarking harness for a specialized workload
  2. Create a profiling tool that integrates with Rust’s compiler to identify optimization opportunities
  3. Develop a performance visualization tool for Rust applications
  4. Benchmark and optimize a complex algorithm across multiple platforms
  5. Create a comprehensive performance regression testing suite for a library

Next Steps

After completing Week 19, you’ll have the skills to analyze and optimize the performance of your Rust code systematically. Week 20 will build on this foundation by exploring SIMD and parallel computing for maximum computational throughput.