_remote_debugging: quadratic replay time for RLE records with alternating status

Bug report

Bug description

The binary profile reader in _remote_debugging (used by python -m profiling.sampling replay and --diff-flamegraph) reconstructs
run-length-encoded (STACK_REPEAT) samples in binary_reader_replay. On every
status change within a repeat record it allocates a fresh timestamp list sized to
the whole remaining sample count (PyList_New(count - i)) and later trims it with
PyList_SetSlice. When the per-sample status byte alternates, this allocates and
trims an ~count-sized list for every sample, so replaying a single repeat record
is O(count**2) in time.

count is bounded only by the file size (remaining_data / 2), so a large but
otherwise valid .pyb file reaches the quadratic regime. Memory stays bounded;
only CPU time is unbounded (a ~2 MB profile takes minutes, and it scales
quadratically from there).

Reproducer

import os, tempfile, time
from _remote_debugging import (
    FrameInfo, LocationInfo, ThreadInfo, InterpreterInfo, THREAD_STATUS_HAS_GIL,
)
from profiling.sampling.binary_collector import BinaryCollector
from profiling.sampling.binary_reader import BinaryReader

class NullCollector:
    def collect(self, samples, timestamps): pass
    def export(self, filename): pass

def timed(n):
    fn = tempfile.mktemp(suffix=".pyb")
    frame = FrameInfo(("rle.py", LocationInfo((1, 1, 0, 0)), "f", None))
    writer = BinaryCollector(fn, 1000, compression="none")
    for i in range(n):
        status = THREAD_STATUS_HAS_GIL if i % 2 else 0
        interp = InterpreterInfo((0, [ThreadInfo((1, status, [frame]))]))
        writer.collect([interp], timestamp_us=1000 + i)   # same stack -> one repeat record
    writer.export(None)
    t0 = time.perf_counter()
    with BinaryReader(fn) as reader:
        reader.replay_samples(NullCollector())
    os.unlink(fn)
    return time.perf_counter() - t0

for n in (50_000, 100_000, 200_000):
    print(n, f"{timed(n):.2f}s")
# Doubling n roughly quadruples the time (quadratic).

Expected behavior

Replay time should be linear in the number of samples. The list for each
status run should be built to its exact length (e.g. PyList_New(0) +
PyList_Append) instead of over-allocating to the remaining count and trimming
per status change.

Linked PRs

• gh-152722

Inizia da profiling/sampling/binary_reader.py e dall’entry point binary_reader_replay, quindi esamina la PR collegata gh-152722. Riproduci il problema con il profilo fornito a stato alternato e verifica che la riproduzione rimanga lineare rispetto al numero di campioni, preservando i campioni e i timestamp riprodotti.