Memory usage benchmarking helps you understand how your application consumes RAM, which is particularly important for scientific applications that work with large datasets.

By tracking memory consumption, you can:

• Identify memory leaks that could cause your application to crash during long-running analyses
• Find opportunities to reduce memory footprint, allowing larger datasets to be processed
• Understand memory usage patterns to better plan resource requirements

For Python scientific applications, memory usage can be dominated by large data structures like NumPy arrays, pandas DataFrames, or other domain-specific containers. Tools like objgraph can help you visualize object references and identify unexpected memory retention.

When your scientific code needs to process datasets larger than available RAM, understanding memory usage patterns becomes crucial for implementing efficient out-of-core processing strategies.