memoryintensive

Memoryintensive, often hyphenated as memory-intensive, describes software or workloads that require a large amount of main memory (RAM) to execute efficiently. Such tasks typically operate on large data sets or hold extensive in-memory state, and performance often hinges on the ability to access many memory locations quickly. Common examples include large-scale data processing, scientific simulations, image and video processing, and in-memory databases or caches. In contrast to CPU-intensive or I/O-bound workloads, memory-intensive tasks are limited by available RAM and memory bandwidth rather than CPU speed or disk access.

Memory pressure can lead to reduced performance through cache misses and, on systems with virtual memory, paging and swapping. Excessive memory use can also cause contention among threads or processes, worsen garbage collection pauses in managed runtimes, and complicate container or virtual machine memory limits. Efficient memory use may involve choosing memory-efficient algorithms, streaming or chunked processing, in-place transformations, data compression, and careful data structure choices to reduce footprint.

To measure memory intensity, developers profile peak and steady-state memory usage, working set, and allocation patterns. Tools include profilers and heap analyzers, OS monitors for RSS or virtual memory, and tracing frameworks. Common optimization strategies include memory pooling, avoiding unnecessary copies, lazy or incremental computation, and out-of-core techniques that keep only a working portion of data in RAM.

In practice, the term is used across domains such as databases, machine learning, graphics, and scientific computing, where RAM availability is a primary constraint and system reliability depends on staying within memory budgets.