Corebins

Corebins are a conceptual data-organization primitive used in distributed and parallel computing to group work units and data by a core processing domain. A corebin is a container that holds items associated with a specific core identifier, such as a CPU core, a functional module, or a processing stage, enabling locality of reference and targeted scheduling.

Design and terminology: Each corebin uses a core_id. Items stored may be tasks, keys, messages, or data records. Routing to bins is determined by a core-assignment function, which may be hash-based, consistent-hash, or workload-aware. Corebins can be realized as in-memory queues, on-disk partitions, or hybrid structures. Typical operations include add, retrieve, drain, and iterate.

Implementation considerations: Corebins emphasize locality—work in a bin tends to stay with its core or module. This can reduce cross-core synchronization and improve cache efficiency. Durability and backpressure handling vary; some designs are transient, others persistent for fault tolerance.

Applications: Corebins are proposed for stream processing, graph analytics, and real-time event pipelines where workload segmentation by core improves throughput and latency. They can complement partitioning schemes and be integrated with schedulers that bind threads to cores.

Limitations: Balancing bins and preventing hot spots adds complexity. Overhead may arise if workloads do not align well with core boundaries.

History and status: Corebins originated in theoretical discussions of data locality in parallel systems. There are no canonical, widely adopted implementations, but related ideas such as per-core queues and partitioned streams appear in research and niche frameworks.

See also: Data partitioning, sharding, per-core queues, stream processing, actor model.