Runtime.NativeDataParallelOwned

Per-coordinate ownership for a data-parallel learner whose update is separable (Learning.DataParallel.dataParallelSeparable) with q words of optimizer state per coordinate: coordinate i is owned by owner i mod n, and only its owner holds that coordinate's optimizer state and updates it. The shared state words stay replicated. `p` chooses the parameters' residency: replicated on every owner (p = 0, optimizer-state partitioning) or partitioned with the optimizer state (p = 1, parameter partitioning: a coordinate's parameter is one more word its owner holds). One update with replicated parameters, on a star of owners 0..n-1: every owner: its samples' (numerator, mass) leaf; sum: the declared left-deep binary64 sum at owner 0, replicated (Runtime.NativeCombine); every owner: means, then only the shared assignments and its own coordinates' assignments of the update program (dataParallelOwnedProgram), in program order; assemble: every owner offers its parameters with an ownership mask; a second ordered combine assembles them by selection, never by arithmetic, refusing two owners for one coordinate, and replicates the result; every owner checks that every coordinate was owned and installs the assembled parameters. With partitioned parameters the assembly gathers them before they are read, and the gathered copy is an explicit, short-lived read replica: assemble: every owner offers its own parameters (the others +0) with its ownership mask; the assembly gathers and replicates them; materialize: every coordinate was owned; the result becomes this owner's read replica, tagged with the version it was gathered at (MaterializeReadVersion); every owner: its leaf, reading only a held replica of the current version; release: the replica is cleared and no longer held (ReleaseReadReplica); sum, then means and the owned assignments on the owner's own parameter and state words; a replica still held when the version advances refuses (`data-parallel-replica-retained`). The replica is held across all of the owner's samples (one gathered version per update) and is part of the owner's peak storage, not its persistent state. Either way the results equal the replicated realization's bit for bit: separability makes each owned coordinate's results independent of the others. Persistence: owner 0 publishes the replicated words (parameters when replicated, shared state); every owned word is a shard of its owner (the star publication prepares each owner's shards from that owner's capture). Restore reads the replicated words and only the restoring owner's own words. Publication requires every owner's capture prefix and replicated words to agree. Partitioned checkpoints bind the learner's placement-independent identity, not its split of samples or its owner count, so a saved state resumes on another number of owners.

98constructor DataParallelOwnedUpdate