Ploop

Ploop is a programming construct described in theoretical and experimental discussions to express parallel iteration over a finite index space. A Ploop allows iterations to be executed concurrently by multiple workers while aiming for deterministic results when iterations are pure or when updates are combined through a specified reducer. It is used as an abstraction to compare and reason about parallel for-loops and data-parallel patterns, but it is not part of a widely standardized language specification.

Origins and terminology: The term Ploop emerged in the early 2010s within research on safe parallelism and high-level abstractions for concurrent programming. It was introduced to capture common patterns of map-like transformations and reductions within a single looping construct, helping to differentiate parallel looping approaches that emphasize safety and composability from ad hoc thread-based loops. Several experimental languages and libraries have prototyped Ploop-inspired syntax and semantics.

Semantics and implementation: A Ploop iterates over an index range [0, n) and applies a user-provided function f(i) for each index i. A runtime may partition the range into chunks, schedule them on available workers, and optionally accumulate results via a reducer or accumulator with deterministic combination rules. Ploops can support work-stealing, barriers, and checkpointing to balance load and ensure progress. Deterministic behavior typically relies on pure iterations or well-defined reduction operations; side effects in iterations are constrained to preserve correctness.

Usage and applications: Ploops are discussed as a suitable abstraction for data processing, simulations, image and signal processing, and other workloads with parallelizable iterations. They are particularly valued for improving load balance and cache locality when iteration costs vary or when side effects are controlled through reducers.

See also: Parallel for, Map-reduce, Data parallelism, Concurrency abstractions.