Resourceintensive

Resource-intensive describes tasks or processes that require substantial computing resources such as CPU time, memory, disk I/O, network bandwidth, or energy. The term is relative to the hardware and software environment in which the task runs, and what is demanding on one system may be routine on another.

Common domains include video or image rendering, scientific simulations, large-scale data processing, database analytics, and machine learning model training. Resource-intensive work can also occur on mobile devices when apps perform heavy graphics, offline data synchronization, or on-device inference with large models.

Causes typically include large data sizes, computationally complex algorithms, high levels of parallelism, memory-intensive workloads, frequent disk I/O, or substantial network transfers. Poor data locality or memory leaks can exacerbate resource use, as can suboptimal compilation, garbage collection, or insufficient caching.

Measurement relies on metrics such as CPU utilization, memory usage, I/O wait, network throughput, and energy consumption. Resource-intensive applications can experience slower response times, higher operating costs, reduced scalability, and greater cooling or power requirements, especially in shared or cloud environments.

Handling resource-intensive tasks involves profiling and benchmarking to identify bottlenecks, followed by optimization: algorithm improvements, parallelization or vectorization, caching, streaming, lazy evaluation, and workload throttling. In cloud or data-center contexts, autoscaling, hardware accelerators (GPUs/TPUs), and careful resource allocation can help manage costs and performance.

The concept is related to, and sometimes contrasted with, terms such as compute-intensive, memory-intensive, and I/O-bound workloads. Because resource intensity is relative to the system, a task may be resource-intensive on one platform but easily handled on another.