# ============================================================================
# prof.ludic — per-pass GPU timing (R3D_PROF=1).
#
# Wall-clock timing of this renderer is useless at pass granularity: run-to-run
# variance on a laptop GPU is ±15%, which is larger than most passes. GL_TIME_ELAPSED
# queries measure what the GPU actually spent inside each pass, and averaging a few
# hundred frames inside ONE run cancels the run-to-run noise entirely.
#
# Each slot owns a small ring of query objects. A query is read back only after
# enough frames have passed that its result is certainly available, so profiling
# never introduces the very stall it is trying to measure.
# ============================================================================

const PROF_SLOTS: int = 16
const PROF_RING:  int = 4            # frames of latency before a result is read

var prof_on:      bool = false
var prof_names:   []pointer = null
var prof_ids:     words = null       # PROF_SLOTS * PROF_RING query objects
var prof_ns:      []long = null      # accumulated nanoseconds per slot
var prof_hits:    []long = null      # samples accumulated per slot
var prof_n:       int = 0            # slots in use
var prof_frame:   int = 0
var prof_active:  int = -1           # slot whose query is currently open
var prof_scratch: words = null

function prof_init() -> void {
  prof_on = Os.has_env("R3D_PROF")
  if not prof_on { return }
  prof_names = new []pointer
  prof_ns = new []long
  prof_hits = new []long
  prof_ids = words(PROF_SLOTS * PROF_RING)
  prof_scratch = words(4)
  gl_gen_queries(PROF_SLOTS * PROF_RING, prof_ids)
  prof_n = 0
  prof_frame = 0
  prof_active = -1
}

# the slot index for `name`, registering it on first sight (order = frame order)
function prof_slot(name: pointer) -> int {
  var i = 0
  while i < prof_n {
    if prof_names[i] == name { return i }
    i += 1
  }
  if prof_n >= PROF_SLOTS { return -1 }
  push(prof_names, name)
  push(prof_ns, 0)
  push(prof_hits, 0)
  prof_n += 1
  return prof_n - 1
}

function prof_begin(name: pointer) -> void {
  if not prof_on { return }
  if prof_active >= 0 { return }        # GL_TIME_ELAPSED queries cannot nest
  let s = prof_slot(name)
  if s < 0 { return }
  prof_active = s
  gl_begin_query(GL_TIME_ELAPSED, prof_ids[s * PROF_RING + (prof_frame % PROF_RING)])
}

function prof_end() -> void {
  if not prof_on { return }
  if prof_active < 0 { return }
  gl_end_query(GL_TIME_ELAPSED)
  prof_active = -1
}

# Collect the queries issued PROF_RING-1 frames ago — long finished, so no stall.
function prof_collect() -> void {
  if not prof_on { return }
  prof_frame += 1
  if prof_frame < PROF_RING { return }
  let slot_frame = (prof_frame + 1) % PROF_RING
  var s = 0
  while s < prof_n {
    let q = prof_ids[s * PROF_RING + slot_frame]
    gl_get_query_objectiv(q, GL_QUERY_RESULT_AVAILABLE, prof_scratch)
    if prof_scratch[0] != 0 {
      gl_get_query_objectui64v(q, GL_QUERY_RESULT, prof_scratch)
      # the low 32 bits are ample: a pass is far below 4 seconds
      prof_ns[s] = prof_ns[s] + prof_scratch[0]
      prof_hits[s] = prof_hits[s] + 1
    }
    s += 1
  }
}

function prof_report() -> void {
  if not prof_on { return }
  print("")
  print("GPU time per pass (mean over the run):")
  var total = 0
  var s = 0
  while s < prof_n {
    if prof_hits[s] > 0 { total = total + prof_ns[s] / prof_hits[s] }
    s += 1
  }
  s = 0
  while s < prof_n {
    if prof_hits[s] > 0 {
      let us = prof_ns[s] / prof_hits[s] / 1000
      var pct = 0
      if total > 0 { pct = (prof_ns[s] / prof_hits[s]) * 100 / total }
      print(`  {Text.pad_right(prof_names[s], 22)} {Text.pad_left(string(us), 7)} us   {string(pct)}%`)
    }
    s += 1
  }
  print(`  {Text.pad_right("TOTAL", 22)} {Text.pad_left(string(total / 1000), 7)} us`)
}

# ---- streaming work per frame (R3D_PROF=1) ----------------------------------
# Stutter while walking is not visible in an average: the cover for a newly entered
# chunk is generated in whichever frame the camera crosses a 32 m cell, so one frame
# in fifty does all the work. `Time.delta` here is a fixed 60 Hz timestep and there is
# no finer wall clock in the runtime, so measure the WORK instead — instances generated
# per frame is exactly what the hitch is made of, and it needs no clock at all.
var prof_gen: []int = null
var prof_gen_cur: int = 0
var prof_ft: []long = null         # real frame times, microseconds
var prof_last_us: long = 0

function prof_gen_add(n: int) -> void {
  if not prof_on { return }
  prof_gen_cur += n
}

# Per-chunk generation, so a hitch can be pinned on a stream and a band rather than on
# "streaming". One chunk is generated atomically, so the worst chunk is the worst frame.
var prof_chunk_us: []long = null
var prof_chunk_kind: []int = null
var prof_chunk_band: []int = null
var prof_chunk_n: []int = null
var prof_gen_us_cur: long = 0
var prof_gen_us: []long = null       # generation microseconds per frame
# CPU work that only happens on some frames — which is what a hitch is made of.
var prof_layer_us_cur: long = 0      # rebuilding and uploading instance buffers
var prof_bake_us_cur: long = 0       # the height-field shadow rebake
var prof_layer_us: []long = null
var prof_bake_us: []long = null
var prof_dt: []long = null           # frame time, aligned with the arrays above
var prof_up_cur: long = 0            # instance bytes uploaded this frame
var prof_up: []long = null
# Where a frame's time went at the coarsest useful split: work this process did, and
# time spent waiting for the GPU to finish it. Headless drains the GPU inside swap, so
# the two are cleanly separable there.
var prof_pre_swap: long = 0
var prof_cpu_us: []long = null
function prof_before_swap() -> void { if prof_on { prof_pre_swap = gl_now_us() } }

# The single most expensive CPU phase of each frame, and what it was. A hitch is one
# phase running long on one frame, so recording the worst one per frame is enough to
# name it without keeping a timeline.
var prof_mark_last: long = 0
var prof_mark_best: long = 0
var prof_mark_name: pointer = null
var prof_mark_us: []long = null
var prof_mark_who: []pointer = null
function prof_mark_start() -> void { if prof_on { prof_mark_last = gl_now_us();  prof_mark_best = 0;  prof_mark_name = null } }
function prof_cpu_mark(name: pointer) -> void {
  if not prof_on { return }
  let now = gl_now_us()
  let d = now - prof_mark_last
  prof_mark_last = now
  if d > prof_mark_best { prof_mark_best = d;  prof_mark_name = name }
}

# the game's own per-frame work, kept apart from the renderer's
var prof_game_us_cur: long = 0
var prof_game_us: []long = null
function prof_game_add(us: long) -> void { if prof_on { prof_game_us_cur = prof_game_us_cur + us } }

function prof_layer_add(us: long, bytes: long) -> void {
  if not prof_on { return }
  prof_layer_us_cur = prof_layer_us_cur + us
  prof_up_cur = prof_up_cur + bytes
}
function prof_bake_add(us: long) -> void { if prof_on { prof_bake_us_cur = prof_bake_us_cur + us } }

function prof_chunk(kind: int, band: int, count: int, us: long) -> void {
  if not prof_on { return }
  if prof_chunk_us == null {
    prof_chunk_us = new []int;  prof_chunk_kind = new []int
    prof_chunk_band = new []int;  prof_chunk_n = new []int
  }
  push(prof_chunk_us, us);  push(prof_chunk_kind, kind)
  push(prof_chunk_band, band);  push(prof_chunk_n, count)
  prof_gen_us_cur += us
}

function prof_chunk_report() -> void {
  if not prof_on or prof_chunk_us == null { return }
  print("")
  print("chunk generation (one chunk is atomic, so the worst chunk is the worst frame):")
  # totals per (kind, band)
  var kinds = new []int
  var bands = new []int
  var tot = new []long
  var cnt = new []int
  var mx = new []long
  var i = 0
  while i < len(prof_chunk_us) {
    var f = -1
    var j = 0
    while j < len(kinds) { if kinds[j] == prof_chunk_kind[i] and bands[j] == prof_chunk_band[i] { f = j };  j += 1 }
    if f < 0 {
      push(kinds, prof_chunk_kind[i]);  push(bands, prof_chunk_band[i])
      push(tot, 0);  push(cnt, 0);  push(mx, 0)
      f = len(kinds) - 1
    }
    tot[f] = tot[f] + prof_chunk_us[i]
    cnt[f] = cnt[f] + 1
    if prof_chunk_us[i] > mx[f] { mx[f] = prof_chunk_us[i] }
    i += 1
  }
  var k = 0
  while k < len(kinds) {
    print(`  kind {string(kinds[k])} band {string(bands[k])}: {string(cnt[k])} chunks, mean {string(tot[k] / cnt[k])} us, worst {string(mx[k])} us, total {string(tot[k] / 1000)} ms`)
    k += 1
  }
  # the per-frame distribution of generation time: this is the hitch itself
  if prof_gen_us == null or len(prof_gen_us) < 16 { return }
  let sorted = new []long
  var a = 8
  while a < len(prof_gen_us) { push(sorted, prof_gen_us[a]);  a += 1 }
  var x = 1
  while x < len(sorted) {
    let v = sorted[x]
    var y = x - 1
    while y >= 0 and sorted[y] > v { sorted[y + 1] = sorted[y];  y -= 1 }
    sorted[y + 1] = v
    x += 1
  }
  let n = len(sorted)
  var busy = 0
  var t: long = 0
  var z = 0
  while z < n { if sorted[z] > 0 { busy += 1 };  t += sorted[z];  z += 1 }
  print(`  generation per frame (us): p95 {string(sorted[(n * 95) / 100])}  p99 {string(sorted[(n * 99) / 100])}  worst {string(sorted[n - 1])}   frames that generated: {string(busy)} of {string(n)}  total {string(t / 1000)} ms`)
}

function prof_gen_frame() -> void {
  if not prof_on { return }
  if prof_gen == null {
    prof_gen = new []int;  prof_ft = new []long
    prof_gen_us = new []long;  prof_layer_us = new []long
    prof_bake_us = new []long;  prof_dt = new []long;  prof_up = new []long
    prof_cpu_us = new []long;  prof_game_us = new []long
    prof_mark_us = new []long;  prof_mark_who = new []pointer
  }
  push(prof_gen, prof_gen_cur)
  prof_gen_cur = 0
  let now = gl_now_us()
  var dtf: long = 0
  if prof_last_us != 0 { push(prof_ft, now - prof_last_us);  dtf = now - prof_last_us }
  prof_last_us = now
  # everything the frame just ended spent on work it only does sometimes
  push(prof_dt, dtf)
  push(prof_gen_us, prof_gen_us_cur)
  push(prof_layer_us, prof_layer_us_cur)
  push(prof_bake_us, prof_bake_us_cur)
  push(prof_up, prof_up_cur)
  var cpu: long = 0
  if prof_pre_swap != 0 and dtf != 0 { cpu = prof_pre_swap - (now - dtf) }
  push(prof_cpu_us, cpu)
  push(prof_game_us, prof_game_us_cur)
  push(prof_mark_us, prof_mark_best)
  if prof_mark_name == null { push(prof_mark_who, "-") } else { push(prof_mark_who, prof_mark_name) }
  prof_gen_us_cur = 0;  prof_layer_us_cur = 0;  prof_bake_us_cur = 0;  prof_up_cur = 0;  prof_game_us_cur = 0
}

# the distribution of REAL frame times: stutter lives in the tail, not the mean
function prof_ft_report() -> void {
  if not prof_on { return }
  if prof_ft == null or len(prof_ft) < 16 { return }
  let sorted = new []long
  var i = 8
  while i < len(prof_ft) { push(sorted, prof_ft[i]);  i += 1 }
  var a = 1
  while a < len(sorted) {
    let v = sorted[a]
    var b = a - 1
    while b >= 0 and sorted[b] > v { sorted[b + 1] = sorted[b];  b -= 1 }
    sorted[b + 1] = v
    a += 1
  }
  let n = len(sorted)
  let med = sorted[n / 2]
  var over = 0
  var j = 0
  while j < n { if sorted[j] > med * 2 { over += 1 };  j += 1 }
  print("")
  print("REAL frame time (us):")
  print(`  median {string(med)}  p95 {string(sorted[(n * 95) / 100])}  p99 {string(sorted[(n * 99) / 100])}  worst {string(sorted[n - 1])}`)
  # A hitch is not the mean moving: it is the count of frames that took noticeably
  # longer than the frame before them. 1.3x median is about where it stops being smooth.
  var o13 = 0
  var o15 = 0
  var q = 0
  while q < n {
    if sorted[q] * 10 > med * 13 { o13 += 1 }
    if sorted[q] * 2 > med * 3 { o15 += 1 }
    q += 1
  }
  # How much time the run spent being slower than itself: the sum of every frame's
  # excess over 1.2x the median. One number that goes down when hitching goes down, and
  # that a handful of unlucky frames cannot dominate the way a maximum can.
  var excess: long = 0
  q = 0
  while q < n {
    let lim = (med * 12) / 10
    if sorted[q] > lim { excess = excess + (sorted[q] - lim) }
    q += 1
  }
  print(`  fps at median {string(1000000 / med)}   frames over 1.3x median: {string(o13)}, over 1.5x: {string(o15)}, over 2x: {string(over)} — of {string(n)}`)
  print(`  stutter: {string(excess / 1000)} ms of frame time beyond 1.2x median over the run`)
  print(`  streaming totals over the run: walk {string(stream_us_walk / 1000)} ms  (generate {string(stream_us_gen / 1000)} ms, gather {string(stream_us_gather / 1000)} ms), {string(stream_walks)} stream-walks`)
}

# The slowest frames of the run, with the once-in-a-while CPU work that landed in them.
# An average never shows a hitch; this is the list of the frames you actually felt.
function prof_hitch_report() -> void {
  if not prof_on or prof_dt == null or len(prof_dt) < 32 { return }
  let n = len(prof_dt)
  # median, for a sense of what "slow" means here
  let sorted = new []long
  var i = 8
  while i < n { push(sorted, prof_dt[i]);  i += 1 }
  var a = 1
  while a < len(sorted) {
    let v = sorted[a]
    var b = a - 1
    while b >= 0 and sorted[b] > v { sorted[b + 1] = sorted[b];  b -= 1 }
    sorted[b + 1] = v
    a += 1
  }
  let med = sorted[len(sorted) / 2]
  print("")
  print(`the 20 slowest frames (median {string(med / 1000)}.{string((med / 100) % 10)} ms), and what was in them:`)
  print("   frame      dt      cpu   gpu-wait  cover-gen     game  uploaded   slowest CPU phase")
  var shown = 0
  var cut: long = sorted[len(sorted) - 1]
  while shown < 20 and cut > med {
    # the next slowest frame at or below `cut`
    var best = -1
    var bestv: long = -1
    var k = 8
    while k < n {
      if prof_dt[k] <= cut and prof_dt[k] > bestv { bestv = prof_dt[k];  best = k }
      k += 1
    }
    if best < 0 { return }
    print(`  {Text.pad_left(string(best), 6)}  {Text.pad_left(string(prof_dt[best]), 6)}us {Text.pad_left(string(prof_cpu_us[best]), 7)}us {Text.pad_left(string(prof_dt[best] - prof_cpu_us[best]), 8)}us  {Text.pad_left(string(prof_gen_us[best]), 7)}us {Text.pad_left(string(prof_game_us[best]), 7)}us {Text.pad_left(string(prof_up[best] / 1024), 7)}KB   {Text.pad_right(prof_mark_who[best], 18)} {Text.pad_left(string(prof_mark_us[best]), 7)}us`)
    cut = bestv - 1
    shown += 1
  }
}

function prof_gen_report() -> void {
  if not prof_on { return }
  if prof_gen == null or len(prof_gen) < 8 { return }
  let sorted = new []int
  var i = 8                       # skip the first frames: one-off initial fill
  while i < len(prof_gen) { push(sorted, prof_gen[i]);  i += 1 }
  var a = 1
  while a < len(sorted) {
    let v = sorted[a]
    var b = a - 1
    while b >= 0 and sorted[b] > v { sorted[b + 1] = sorted[b];  b -= 1 }
    sorted[b + 1] = v
    a += 1
  }
  let n = len(sorted)
  var total = 0
  var busy = 0
  var j = 0
  while j < n { total += sorted[j];  if sorted[j] > 0 { busy += 1 };  j += 1 }
  print("")
  print("ground cover generated per frame (the source of walking stutter):")
  print(`  frames that generated anything: {string(busy)} of {string(n)}`)
  print(`  median {string(sorted[n / 2])}   p95 {string(sorted[(n * 95) / 100])}   worst {string(sorted[n - 1])}   total {string(total)}`)
}