Memory management is a key concern when working with large datasets. Many researchers and developers will load entire datasets into memory for processing. Although this is a straightforward approach that allows for quick access and manipulation of data, it has its drawbacks. When the dataset size approaches or exceeds the available physical memory, performance degrades rapidly due to excessive swapping, leading to increased latency and reduced throughput. Memory-mapped files are an alternative strategy to access and manipulate large datasets without the need to load them fully into memory.

A background on memory-mapped Files

Memory mapping is the process of mapping a file or a portion of a file directly into virtual memory. This mapping establishes a one-to-one correspondence between the file’s contents on disk and specific addresses in the process’s memory space. Instead of relying on traditional I/O operations, such as read() an write(), which involve copying data between kernel space and user space, the process can access the file’s contents directly through memory addresses. Then, page faults are used to determine which chunks to load into physical memory. However, this chunks are significantly smaller than the whole file contents. This direct access reduces overhead and can significantly speed up data processing, especially for large files or applications that require high-throughput I/O operations.