2023-08-16 –, Aula
Scientific code is often complex, resource-intensive, and sensitive to performance issues, making accurate timing and benchmarking critical for optimising performance and ensuring reproducibility. However, benchmarking scientific code presents several challenges, including variability in input data, hardware and software dependencies, and optimisation trade-offs. In this talk, I discuss the importance of timing and benchmarking for scientific code and outline strategies for addressing these challenges. Specifically, I emphasise the need for representative input data, controlled benchmarking environments, appropriate metrics, and careful documentation of the benchmarking process. By following these strategies, developers can effectively optimise code performance, select efficient algorithms and data structures, and ensure the reliability and reproducibility of scientific computations.
Scientific code plays a crucial role in advancing scientific research and discovery, but its complexity, resource-intensiveness, and sensitivity to performance issues make accurate timing and benchmarking critical for optimal performance and reproducibility. To this end, this talk addresses the importance of timing and benchmarking for scientific code, and outlines the challenges and strategies associated with it.
One of the main challenges in benchmarking scientific code is the variability of input data, which can influence the benchmarking results. To overcome this, it is essential to use representative input data that accurately reflects real-world scenarios. In addition, it is crucial to establish a controlled benchmarking environment to minimise the impact of external variables on the results. This includes running benchmarks on the same hardware and software configurations, using the same input data, and running multiple trials to ensure consistency.
Another challenge is the choice of appropriate metrics to measure performance. Depending on the specific requirements of the application, this may involve measuring execution time, memory usage, or other metrics. In addition, optimisation trade-offs can also affect benchmarking results, highlighting the importance of carefully balancing performance with other factors such as accuracy and maintainability.
To ensure reproducibility, careful documentation of the benchmarking process is necessary, including the input data, hardware and software configurations, and benchmarking methodology. By following best practices such as these, developers can effectively optimise code performance, select efficient algorithms and data structures, and ensure the reliability and reproducibility of scientific computations.
In summary, this talk highlights the significance of accurate timing and benchmarking for scientific code, and presents strategies and best practices for overcoming the challenges associated with it. By implementing these strategies, researchers and developers can accelerate scientific progress and drive innovation through robust and reliable scientific computations.
Timing and benchmarking are critical for scientific code, but pose challenges such as input variability and optimisation trade-offs. Using representative input data, controlled environments, and appropriate metrics can help.
Category [Data Science and Visualization]:Data Analysis and Data Engineering
Expected audience expertise: Domain:none
Expected audience expertise: Python:some
Public link to supporting material:
Kai is a SciPy maintainer and a software developer at BHP. He is interested in all things Python, particularly in pushing Python's performance to the language's limits.