1	# Prediction parameters, not machine bounds or correctness certificates. The
2	# worker count is not a profile value: callers pass the runtime's pool size.
3	import Base
4	
5	type Configuration is Data:
6	  Configuration{mac_ns: F32,copy_byte_ns: F32,operation_ns: F32,grain_ns: F32,wake_round_ns: F32}
7	
8	# One element's cost. Bytes price loading, storing and the loop that moves
9	# them, as the copies that measured copy_byte_ns do; operations are the other
10	# scalar work, counted from source.
11	def element_ns(+bytes: F32,+operations: F32,config: Configuration) -> F32:
12	  Configuration{mac_ns,byte_ns,operation_ns,grain,wake} = config
13	  (bytes * byte_ns + operations * operation_ns : F32)
14	
15	# The official runtime grows fork tasks and queues fork-free tasks for its
16	# work round (monk_step, row_grow, cube_run). No fixed number of leaves can
17	# be subtracted as already completed by growth. Price complete waves of at
18	# most workers leaves, using the largest path (including clones) per wave.
19	# This is a conservative scheduling estimate, not a bound on the runtime's
20	# static ring distribution. Callers charge the two wake rounds separately.
21	def region_ns(+workers: U32,+leaves: U32,+span: F32) -> F32:
22	  +pool = U32.max(workers,1)
23	  +waves = (leaves / pool + Bool.pick(U32,U32.is_zero((leaves % pool : U32)),0,1) : U32)
24	  (span * U32.to_f32(waves) : F32)