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060449e234
Previously parallel dispatch only parallelised by column, so a 6-column dataset on a 16-core machine used at most 6 cores with the other 10 idle. For large n this also caused memory-bandwidth saturation (each worker did a full n-row scan of clinical_breakpoints simultaneously). New row-batch mode (fork path, R >= 4.0, non-Windows): pieces_per_col = ceil(n_cores / n_cols) Jobs = n_cols × pieces_per_col (≈ n_cores jobs total) Each job: one column × one row slice Benefits: - All cores stay busy regardless of column count - Per-worker memory footprint shrinks by pieces_per_col × - Breakpoints lookup cache pressure reduced per worker PSOCK path (Windows / R < 4.0) is unchanged: per-job serialisation overhead makes row batching unprofitable there. run_as_sir_column() gains an optional `rows` parameter (NULL = all rows, backward-compatible). Results are reassembled via as.sir(c(as.character(.))) which is safe for already-clean SIR values. https://claude.ai/code/session_012DXCXbZUC54Zij1z9bFiHR