Optimise parallel as.sir(): row-batch mode when n_cols < n_cores

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

tests/testthat/test-sir.R

@@ -502,6 +502,21 @@ test_that("test-sir.R", {
   sir_single_par <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE, parallel = TRUE))
   expect_identical(sir_single_seq[["AMC"]], sir_single_par[["AMC"]])
 
+  # 9. row-batch mode (n_cols < n_cores): force row splitting via max_cores and
+  #    verify identical output to sequential for a dataset with 2 AB columns so
+  #    pieces_per_col = ceiling(max_cores / 2) >= 2 and row batching activates
+  df_wide <- data.frame(
+    mo  = "B_ESCHR_COLI",
+    AMC = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
+    GEN = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
+    stringsAsFactors = FALSE
+  )
+  sir_wide_seq <- suppressMessages(as.sir(df_wide, col_mo = "mo", info = FALSE))
+  sir_wide_par <- suppressMessages(as.sir(df_wide, col_mo = "mo", info = FALSE,
+                                          parallel = TRUE, max_cores = 8L))
+  expect_identical(sir_wide_seq[["AMC"]], sir_wide_par[["AMC"]])
+  expect_identical(sir_wide_seq[["GEN"]], sir_wide_par[["GEN"]])
+
   # 8. info = TRUE with parallel does not produce per-column worker messages
   #    (messages should only appear in the main process, not duplicated from workers)
   msgs <- capture.output(