Performance

This section covers essential practices for understanding and improving the performance of your scientific software. Performance optimization is crucial for scientific applications that may process large datasets or perform computationally intensive calculations.

What You’ll Learn

In this section, you’ll discover:

  • Time complexity analysis: How to understand and monitor the efficiency of your algorithms
  • Code profiling techniques: Tools and methods to identify performance bottlenecks
  • Benchmarking approaches: How to measure execution time and memory usage
  • Performance optimization strategies: Practical ways to improve your code’s efficiency

Understanding and improving performance is often a long-term concern that becomes increasingly important as your scientific software matures and is applied to larger or more complex problems.

Test example code in documentation before releaseMonitor your time complexity