Computationheavy

Computation-heavy describes workloads or tasks that demand a large amount of processing power relative to other resources such as memory or I/O. In practical terms, these are problems whose time-to-solution is dominated by CPU or accelerator compute rather than data movement. This classification is relative: a task may be computation-heavy on a mobile processor but not on a high-end server, where memory bandwidth or storage I/O may still be limiting.

Typical domains include scientific simulations, numerical modeling, machine learning model training, graphics rendering, cryptography, and large-scale data analytics. Examples range from climate and fluid dynamics simulations to molecular dynamics, finite element analysis, and training deep neural networks. The common thread is a heavy computational workload with high arithmetic intensity.

Key characteristics are the ratio of floating-point operations to memory accesses, and whether performance scales with additional compute resources (compute-bound) or is limited by memory bandwidth (memory-bound). Efficiency depends on algorithm choice, data locality, and parallelizability. Profiling and tuning are essential to identify hotspots and bottlenecks.

Hardware strategies emphasize parallelism and accelerators. Computation-heavy tasks benefit from GPUs or specialized accelerators, vector units, and multi-core or distributed systems. Achieving scalable performance requires attention to data layout, communication overhead, and workload partitioning. In practice, algorithmic improvements, approximation, and numerical stability considerations can reduce compute requirements.

Common metrics include FLOPS (floating-point operations per second) and arithmetic intensity, the ratio of operations to memory accesses. Trade-offs between accuracy, speed, energy use, and cost shape design choices. See also high-performance computing, numerical analysis, parallel computing, and compute-bound versus memory-bound concepts.