colesbury
diff --git a/‎Tools/ftscalingbench/ftscalingbench.py
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+# This script runs a set of small benchmarks to help identify scaling
+# bottlenecks in the free-threaded interpreter. The benchmarks consists
+# of patterns that ought to scale well, but haven't in the past. This is
+# typically due to reference count contention or lock contention.
+#
+# This is not intended to be a general multithreading benchmark suite, nor
+# are the benchmarks intended to be representative of real-world workloads.
+#
+# On Linux, to avoid confounding hardware effects, the script attempts to:
+# * Use a single CPU socket (to avoid NUMA effects)
+# * Use distinct physical cores (to avoid hyperthreading/SMT effects)
+# * Use "performance" cores (Intel, ARM) on CPUs that have performance and
+#   efficiency cores
+#
+# It also helps to disable dynamic frequency scaling (i.e., "Turbo Boost")
+#
+# Intel:
+# > echo "1" | sudo tee /sys/devices/system/cpu/intel_pstate/no_turbo
+#
+# AMD:
+# > echo "0" | sudo tee /sys/devices/system/cpu/cpufreq/boost
+#
+
+import math
+import threading
+import time
+import sys
+import os
+import queue
+import gc
+
+# The iterations in individual benchmarks are scaled by this factor.
+WORK_SCALE = 100
+
+ALL_BENCHMARKS = {}
+
+threads = []
+in_queues = []
+out_queues = []
+
+
+def register_benchmark(func):
+    ALL_BENCHMARKS[func.__name__] = func
+    return func
+
+@register_benchmark
+def object_cfunction():
+    accu = 0
+    tab = [1] * 100
+    for i in range(1000 * WORK_SCALE):
+        tab.pop(0)
+        tab.append(i)
+        accu += tab[50]
+    return accu
+
+@register_benchmark
+def cmodule_function():
+    for i in range(1000 * WORK_SCALE):
+        math.floor(i * i)
+
+@register_benchmark
+def mult_constant():
+    x = 1.0
+    for i in range(3000 * WORK_SCALE):
+        x *= 1.01
+
+def simple_gen():
+    for i in range(10):
+        yield i
+
+@register_benchmark
+def generator():
+    accu = 0
+    for i in range(100 * WORK_SCALE):
+        for v in simple_gen():
+            accu += v
+    return accu
+
+class Counter:
+    def __init__(self):
+        self.i = 0
+
+    def next_number(self):
+        self.i += 1
+        return self.i
+
+@register_benchmark
+def pymethod():
+    c = Counter()
+    for i in range(1000 * WORK_SCALE):
+        c.next_number()
+    return c.i
+
+def next_number(i):
+    return i + 1
+
+@register_benchmark
+def pyfunction():
+    accu = 0
+    for i in range(1000 * WORK_SCALE):
+        accu = next_number(i)
+    return accu
+
+def double(x):
+    return x + x
+
+module = sys.modules[__name__]
+
+@register_benchmark
+def module_function():
+    total = 0
+    for i in range(1000 * WORK_SCALE):
+        total += module.double(i)
+    return total
+
+class MyObject:
+    pass
+
+@register_benchmark
+def load_string_const():
+    accu = 0
+    for i in range(1000 * WORK_SCALE):
+        if i == 'a string':
+            accu += 7
+        else:
+            accu += 1
+    return accu
+
+@register_benchmark
+def load_tuple_const():
+    accu = 0
+    for i in range(1000 * WORK_SCALE):
+        if i == (1, 2):
+            accu += 7
+        else:
+            accu += 1
+    return accu
+
+@register_benchmark
+def create_pyobject():
+    for i in range(1000 * WORK_SCALE):
+        o = MyObject()
+
+@register_benchmark
+def create_closure():
+    for i in range(1000 * WORK_SCALE):
+        def foo(x):
+            return x
+        foo(i)
+
+@register_benchmark
+def create_dict():
+    for i in range(1000 * WORK_SCALE):
+        d = {
+            "key": "value",
+        }
+
+thread_local = threading.local()
+
+@register_benchmark
+def thread_local_read():
+    tmp = thread_local
+    tmp.x = 10
+    for i in range(500 * WORK_SCALE):
+        _ = tmp.x
+        _ = tmp.x
+        _ = tmp.x
+        _ = tmp.x
+        _ = tmp.x
+
+
+def bench_one_thread(func):
+    t0 = time.perf_counter_ns()
+    func()
+    t1 = time.perf_counter_ns()
+    return t1 - t0
+
+
+def bench_parallel(func):
+    t0 = time.perf_counter_ns()
+    for inq in in_queues:
+        inq.put(func)
+    for outq in out_queues:
+        outq.get()
+    t1 = time.perf_counter_ns()
+    return t1 - t0
+
+
+def benchmark(func):
+    delta_one_thread = bench_one_thread(func)
+    delta_many_threads = bench_parallel(func)
+
+    speedup = delta_one_thread * len(threads) / delta_many_threads
+    if speedup >= 1:
+        factor = speedup
+        direction = "faster"
+    else:
+        factor = 1 / speedup
+        direction = "slower"
+
+    color = ""
+    if use_color:
+        if speedup <= 1.1:
+            color = "\x1b[31m"  # red
+        elif speedup < len(threads)/2:
+            color = "\x1b[33m"  # yellow
+
+    print(f"{color}{func.__name__:<18} {round(factor, 1):>4}x {direction}{color and '\x1b[0m'}")
+
+def determine_num_threads_and_affinity():
+    if sys.platform != "linux":
+        return [None] * os.cpu_count()
+
+    # Try to use `lscpu -p` on Linux
+    import subprocess
+    try:
+        output = subprocess.check_output(["lscpu", "-p=cpu,node,core,MAXMHZ"], text=True)
+    except (FileNotFoundError, subprocess.CalledProcessError):
+        return [None] * os.cpu_count()
+
+    table = []
+    for line in output.splitlines():
+        if line.startswith("#"):
+            continue
+        cpu, node, core, maxhz = line.split(",")
+        table.append((int(cpu), int(node), int(core), float(maxhz)))
+
+    cpus = []
+    cores = set()
+    max_mhz_all = max(row[3] for row in table)
+    for cpu, node, core, maxmhz in table:
+        # Choose only CPUs on the same node, unique cores, and try to avoid
+        # "efficiency" cores.
+        if node == 0 and core not in cores and maxmhz == max_mhz_all:
+            cpus.append(cpu)
+            cores.add(core)
+    return cpus
+
+
+def thread_run(cpu, in_queue, out_queue):
+    if cpu is not None and hasattr(os, "sched_setaffinity"):
+        # Set the affinity for the current thread
+        os.sched_setaffinity(0, (cpu,))
+
+    while True:
+        func = in_queue.get()
+        if func is None:
+            break
+        func()
+        out_queue.put(None)
+
+
+def initialize_threads(opts):
+    if opts.threads == -1:
+        cpus = determine_num_threads_and_affinity()
+    else:
+        cpus = [None] * opts.threads  # don't set affinity
+
+    print(f"Running benchmarks with {len(cpus)} threads")
+    for cpu in cpus:
+        inq = queue.Queue()
+        outq = queue.Queue()
+        in_queues.append(inq)
+        out_queues.append(outq)
+        t = threading.Thread(target=thread_run, args=(cpu, inq, outq), daemon=True)
+        threads.append(t)
+        t.start()
+
+
+def main(opts):
+    global WORK_SCALE
+    if not hasattr(sys, "_is_gil_enabled") or sys._is_gil_enabled():
+        sys.stderr.write("expected to be run with the  GIL disabled\n")
+
+    benchmark_names = opts.benchmarks
+    if benchmark_names:
+        for name in benchmark_names:
+            if name not in ALL_BENCHMARKS:
+                sys.stderr.write(f"Unknown benchmark: {name}\n")
+                sys.exit(1)
+    else:
+        benchmark_names = ALL_BENCHMARKS.keys()
+
+    WORK_SCALE = opts.scale
+
+    if not opts.baseline_only:
+        initialize_threads(opts)
+
+    do_bench = not opts.baseline_only and not opts.parallel_only
+    for name in benchmark_names:
+        func = ALL_BENCHMARKS[name]
+        if do_bench:
+            benchmark(func)
+            continue
+
+        if opts.parallel_only:
+            delta_ns = bench_parallel(func)
+        else:
+            delta_ns = bench_one_thread(func)
+
+        time_ms = delta_ns / 1_000_000
+        print(f"{func.__name__:<18} {time_ms:.1f} ms")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-t", "--threads", type=int, default=-1,
+                        help="number of threads to use")
+    parser.add_argument("--scale", type=int, default=100,
+                        help="work scale factor for the benchmark (default=100)")
+    parser.add_argument("--baseline-only", default=False, action="store_true",
+                        help="only run the baseline benchmarks (single thread)")
+    parser.add_argument("--parallel-only", default=False, action="store_true",
+                        help="only run the parallel benchmark (many threads)")
+    parser.add_argument("benchmarks", nargs="*",
+                        help="benchmarks to run")
+    options = parser.parse_args()
+    main(options)