| shard-package | R Documentation |
Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. 'shard' enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking.
shard_map() - Primary parallel execution entry point
shards() - Create shard descriptors with autotuning
results() - Extract results from a shard_map run
succeeded() - Check if shard_map completed without failures
share() - Share an R object for parallel access
fetch() - Retrieve data from a shared object
materialize() - Alias for fetch()
is_shared() - Check if an object is shared
shared_info() - Get information about a shared object
buffer() - Create typed writable output buffer
buffer_open() - Open existing buffer from another process
buffer_path() - Get buffer path for cross-process sharing
buffer_info() - Get buffer information
buffer_close() - Close and release buffer
pool_create() - Create a supervised worker pool
pool_stop() - Stop the worker pool
pool_status() - Check worker status and RSS
pool_health_check() - Monitor and recycle workers
dispatch_chunks() - Execute chunks with supervision
pool_lapply() - Parallel lapply with supervision
pool_sapply() - Parallel sapply with supervision
Maintainer: Bradley Buchsbaum brad.buchsbaum@gmail.com [copyright holder]
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