memoryheavy

Memoryheavy is an adjective used to describe software, processes, or algorithms that require a large amount of memory relative to typical resources or available RAM. A memoryheavy component often has a high memory footprint during its critical phase, which can lead to paging or swapping on systems with limited memory and may affect overall system performance.

Memoryheavy workloads are common in domains such as in-memory databases, analytics, large-scale simulations, image or video processing pipelines, and machine learning tasks that load substantial data or models into memory. When sufficient memory is available, these components can run quickly and provide fast access to data, but they become bottlenecks on memory-constrained hosts or in multi-tenant environments.

Typical characteristics include a high peak memory usage, sensitivity to data size and representation, potential memory fragmentation, and garbage collection or allocator pressure in managed runtimes. Memoryheavy software may also exhibit reduced cache locality and increased overhead from data structures designed for speed rather than space.

Implications and strategies focus on managing memory pressure and maintaining performance. Techniques include reducing the memory footprint through more compact data structures, streaming or chunking data, using memory-mapped files or out-of-core algorithms, data compression, lazy loading, and careful cache management. Offloading to external storage or accelerators, selective loading, and profiling to identify memory hotspots are also common approaches.

Examples of memoryheavy scenarios include large in-memory data stores, graph analytics on big graphs, high-resolution video editing, and scientific simulations that process vast datasets. See also memory footprint, out-of-core computation, virtual memory, and garbage collection.