Planparallelism

Planparallelism, or plan parallelism, is a form of parallel computation in which the execution of a computational plan—composed of tasks and dependencies—is distributed across multiple processing units so that independent tasks can run in parallel while preserving the overall order dictated by the plan.

It differs from data parallelism, which replicates the same operation across data items, and from pure task parallelism where independent tasks run concurrently but the plan's structure is not a core driver. In planparallelism, the computation is explicitly represented as a plan graph or DAG, where nodes are operators or tasks and edges encode dependencies. Scheduling aims to maximize parallelism while honoring dependencies.

Implementation uses a plan graph or DAG, with a scheduler mapping tasks to cores or machines. Techniques include coarse-grained decomposition, speculative execution, pipelining, and load balancing. Communication cost, memory locality, and synchronization points are critical factors. Some systems leverage existing workflow engines, database optimizers, or parallel graph processing frameworks.

Applications include large-scale simulations, scientific workflows, database query execution plans, compiler optimization pipelines, and distributed workflow systems. Planparallelism is particularly useful when tasks vary in cost or when dependencies limit straightforward data parallelism.

Advantages include better utilization of heterogeneous resources and ability to exploit parallelism in complex, dependency-driven workloads. Limitations involve overhead from coordination, dependency serialization, and diminishing returns as more processors are added. Runtime adaptation and fault tolerance are ongoing research areas.

See also: data parallelism, task parallelism, pipeline parallelism, DAG scheduling, parallel computation.