Computecentric

Computecentric is an adjective that describes approaches, systems, or organizations that place computation at the center of design and operation. The term combines "compute" with "centric" to signal that processing power, algorithms, and computational efficiency are primary drivers of architecture and decision making.

In computing architecture, a computecentric design prioritizes scalable processing resources, parallelism, and memory bandwidth. It encompasses multi-core and many-core systems, GPU-accelerated workloads, and cloud-native platforms that optimize compute for artificial intelligence, simulations, or data processing pipelines. In software development, a computecentric approach emphasizes algorithmic efficiency, profiling, and optimization for throughput and latency, sometimes at the expense of data layout unless data locality is crucial.

Historically, computecentric thinking has grown alongside high-performance computing, big data analytics, and cloud engineering, where compute capacity tends to be a central lever for performance and cost. It often intersects with, but differs from, data-centric or event-driven paradigms, which place data flows or events at the core of design.

Critiques note that a computecentric emphasis can neglect data governance, locality, energy efficiency, or user-centric concerns. In practice, many systems balance computecentric goals with data-centric or service-centric considerations to meet diverse requirements.

See also data-centric design; high-performance computing; cloud computing.