Ocannl.CalibrateMemory-bandwidth calibration (gh-ocannl-578): STREAM-style streaming kernels whose byte counts are exact by construction — one access site per tensor node and direction, no guards — so the calibration rows they append under config autotune_calibration_file satisfy the fitter's per-leg exactness rule and can constrain the envelope's memory leg (model_peak_memory_bandwidth). Matmul-family tuning data cannot: its rows are compute-bound (their bytes-over-time understates achievable bandwidth no matter how exactly counted) and typically carry guards-taken/union upper bounds, so the fit of the memory leg needs exactly this kind of calibration diversity — see tools/calibrate_bandwidth.ml for the command-line entry point and Ir.Cost_model.Calibration for the fit semantics.
module Asgns = Ir.Assignmentsmodule Idx = Ir.Indexingval named : Base.string -> Asgns.comp -> Asgns.compval stream :
?elems:??? ->
?repeats:??? ->
Context.t ->
(Base.string * Autotune.report) Base.List.tstream ?elems ?repeats ctx times the four STREAM kernels — copy c =: a, scale b =: 0.5 *. a, add c =: a + b, triad a =: b + 0.5 *. c — over 1-D single-precision tensors of elems cells each (default 2^26, 256 MiB per tensor: well past any last-level cache, so the measured rates are main-memory rates). Each kernel goes through Autotune.tune with seed candidates only (~rounds:0) and the schedule cache disabled, so every timed candidate appends a calibration row through the ordinary emission path; the search is forced on (~search:true) — timing is the point of a calibration pass, whatever profile the config picked. Choose elems a power of two: an extent every workgroup size divides evenly keeps the parallelized candidates free of range guards, hence their rows bytes-exact.
Returns the kernels' tuning reports in order, (name, report). Raises Utils.User_error when config autotune_calibration_file is not set — the pass would time kernels with nowhere to record them.