Memorydemanding

Memorydemanding is an adjective used to describe software, algorithms, or workloads that require substantial amounts of main memory to operate efficiently. A memorydemanding task typically cannot be processed effectively within limited RAM and may rely on large data structures, cache-heavy computation, or extensive buffering. The term is often used in contrast to memory-efficient or CPU-bound tasks.

Common domains include data analysis, machine learning, scientific simulations, high-resolution image or video processing, and real-time analytics, where data sizes or model parameters exceed the capacity of cheap hardware. Memorydemanding workloads are characterized by large memory footprints, high peak memory usage, and substantial working sets, sometimes leading to paging, thrashing, or out-of-memory errors on systems with insufficient RAM.

Measurement of memorydemanding behavior focuses on memory footprint, peak resident set size, and the memory-to-time trade-off. Factors that increase memory demand include data volume, dense data structures, in-memory caching, and aggressive parallelism that multiplies per-thread or per-process memory use.

Mitigation strategies involve memory profiling, choosing space-efficient algorithms and data structures, streaming or chunked processing, out-of-core techniques, memory-mapped files, data compression, and careful garbage collection or memory pooling. In some cases, hardware upgrades or cloud-based scaling are used to accommodate memory demand.

Related concepts include memory footprint, working set, virtual memory, and out-of-core computing.