scale-test

#!/usr/bin/env python
#
# -*- python -*-
#
# Runs a .gyb scale-testing file repeatedly through swiftc while varying a
# scaling variable 'N', collects json stats from the compiler, transforms the
# problem to log-space and runs a linear regression to estimate the exponent on
# the stat's growth curve relative to N.
#
# The estimate will be more accurate as N increases, so if you get a
# not-terribly-convincing estimate, try increasing --begin and --end to larger
# values.
#

from __future__ import print_function

import argparse
import functools
import json
import math
import os
import os.path
import random
import shutil
import subprocess
import sys
import tempfile

from collections import namedtuple
from operator import attrgetter

import gyb

from jobstats import load_stats_dir, merge_all_jobstats


def find_which(p):
    for d in os.environ["PATH"].split(os.pathsep):
        full = os.path.join(d, p)
        if os.path.isfile(full) and os.access(full, os.X_OK):
            return full
    return p


# Evidently the debug-symbol reader in dtrace is sufficiently slow and/or buggy
# that attempting to inject probes into a binary w/ debuginfo is asking for a
# failed run (possibly racing with probe insertion, or probing the stabs
# entries, see rdar://problem/7037927 or rdar://problem/11490861 respectively),
# so we sniff the presence of debug symbols here.
def has_debuginfo(swiftc):
    swiftc = find_which(swiftc)
    for line in subprocess.check_output(
            ["dwarfdump", "--file-stats", swiftc]).splitlines():
        if '%' not in line:
            continue
        fields = line.split()
        if fields[8] != '0.00%' or fields[10] != '0.00%':
            return True
    return False


def write_input_file(args, ast, d, n):
    fname = "in%d.swift" % n
    pathname = os.path.join(d, fname)
    with open(pathname, 'w+') as f:
        f.write(gyb.execute_template(ast, '', N=n))
    return fname


def ensure_tmpdir(d):
    if d is not None and not os.path.exists(d):
        os.makedirs(d, 0700)
    return tempfile.mkdtemp(dir=d)


# In newer compilers, we can use -stats-output-dir and get both more
# counters, plus counters that are enabled in non-assert builds. Check
# to see if we have support for that.
def supports_stats_output_dir(args):
    d = ensure_tmpdir(args.tmpdir)
    sd = os.path.join(d, "stats-probe")

    try:
        os.makedirs(sd, 0700)
        # Write a trivial test program and try running with
        # -stats-output-dir
        testpath = os.path.join(sd, "test.swift")
        with open(testpath, 'w+') as f:
            f.write("print(1)\n")
        command = [args.swiftc_binary, '-frontend',
                   '-typecheck',
                   '-stats-output-dir', sd, testpath]
        subprocess.check_call(command)
        stats = load_stats_dir(sd)
        return len(stats) != 0
    except subprocess.CalledProcessError:
        return False
    finally:
        shutil.rmtree(sd)


def run_once_with_primary(args, ast, rng, primary_idx):
    r = {}
    try:
        d = ensure_tmpdir(args.tmpdir)
        inputs = [write_input_file(args, ast, d, i) for i in rng]
        primary = inputs[primary_idx]
        # frontend no longer accepts duplicate inputs
        del inputs[primary_idx]
        ofile = "out.o"

        mode = "-c"
        if args.typecheck:
            mode = "-typecheck"
        if args.parse:
            mode = "-parse"

        focus = ["-primary-file", primary]
        if args.whole_module_optimization:
            focus = ['-whole-module-optimization']

        opts = []
        if args.optimize:
            opts = ['-O']
        elif args.optimize_none:
            opts = ['-Onone']
        elif args.optimize_unchecked:
            opts = ['-Ounchecked']

        extra = args.Xfrontend[:]
        if args.debuginfo:
            extra.append('-g')

        command = [args.swiftc_binary,
                   "-frontend", mode,
                   "-o", ofile] + opts + focus + extra + inputs

        if args.trace:
            print("running: " + " ".join(command))

        if args.dtrace:
            trace = "trace.txt"
            script = ("pid$target:swiftc:*%s*:entry { @[probefunc] = count() }"
                      % args.select)
            try:
                subprocess.check_call(
                    ["sudo", "dtrace", "-q",
                     "-o", trace,
                     "-b", "256",
                     "-n", script,
                     "-c", " ".join(command)], cwd=d)
            except subprocess.CalledProcessError as e:
                if e.returncode != args.expected_exit_code:
                    raise

            r = {fields[0]: int(fields[1]) for fields in
                 [line.split() for line in open(os.path.join(d, trace))]
                 if len(fields) == 2}
        else:
            if args.debug:
                command = ["lldb", "--"] + command
            stats = "stats.json"
            if args.llvm_stat_reporter:
                argv = command + ["-Xllvm", "-stats",
                                  "-Xllvm", "-stats-json",
                                  "-Xllvm", "-info-output-file=" + stats]
            else:
                argv = command + ["-stats-output-dir", d]
            try:
                subprocess.check_call(argv, cwd=d)
            except subprocess.CalledProcessError as e:
                if e.returncode != args.expected_exit_code:
                    raise

            if args.llvm_stat_reporter:
                with open(os.path.join(d, stats)) as f:
                    r = json.load(f)
            else:
                r = merge_all_jobstats(load_stats_dir(d)).stats
    finally:
        shutil.rmtree(d)

    return {k: v for (k, v) in r.items() if args.select in k}


def run_once(args, ast, rng):
    if args.sum_multi:
        cumulative = {}
        for i in range(len(rng)):
            tmp = run_once_with_primary(args, ast, rng, i)
            for (k, v) in tmp.items():
                if k in cumulative:
                    cumulative[k] += v
                else:
                    cumulative[k] = v
        return cumulative
    else:
        return run_once_with_primary(args, ast, rng, -1)


def run_many(args):

    if args.dtrace and has_debuginfo(args.swiftc_binary):
        print("")
        print("**************************************************")
        print("")
        print("dtrace is unreliable on binaries w/ debug symbols")
        print("please run 'strip -S %s'" % args.swiftc_binary)
        print("or pass a different --swiftc-binary")
        print("")
        print("**************************************************")
        print("")
        exit(1)

    if not args.llvm_stat_reporter:
        if not supports_stats_output_dir(args):
            print("**************************************************")
            print("")
            print("unable to use new-style -stats-output-dir reporting,")
            print("falling back to old-style -Xllvm -stats-json reporting")
            print("(run with --llvm-stat-reporter to silence this warning)")
            print("")
            print("**************************************************")
            args.llvm_stat_reporter = True

    ast = gyb.parse_template(args.file.name, args.file.read())
    rng = range(args.begin, args.end, args.step)
    if args.step > (args.end - args.begin):
        print("Step value", args.step,
              "is too large for the range", str((args.begin, args.end)) + ".",
              "Have you forgotten to override it?")
        exit(1)
    if args.multi_file or args.sum_multi:
        return (rng, [run_once(args, ast, range(i)) for i in rng])
    else:
        return (rng, [run_once(args, ast, [r]) for r in rng])


somewhat_small = 1e-4


def is_somewhat_small(x):
    return abs(x) < somewhat_small


def tup_add(t1, t2):
    return tuple(a + b for (a, b) in zip(t1, t2))


def tup_sub(t1, t2):
    return tuple(a - b for (a, b) in zip(t1, t2))


def tup_mul(s, t):
    return tuple(s * v for v in t)


def tup_distance(t1, t2):
    return math.sqrt(sum((a - b) ** 2 for (a, b) in zip(t1, t2)))


def centroid(tuples):
    n = len(tuples)
    if n == 0:
        return 0.0
    tupsz = len(tuples[0])
    zero = (0,) * tupsz
    s = functools.reduce(tup_add, tuples, zero)
    return tup_mul(1.0 / float(n), s)


def converged(ctr, simplex, epsilon):
    return max(tup_distance(ctr, p.loc) for p in simplex) < epsilon


def Nelder_Mead_simplex(objective, params, bounds, epsilon=1.0e-6):
    # By the book: https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
    ndim = len(params)
    assert(ndim >= 2)

    def named(tup):
        return params.__new__(params.__class__, *tup)

    def f(tup):
        return objective(named(tup))

    locs = [tuple(random.uniform(*b) for b in bounds)
            for _ in range(ndim + 1)]
    SimplexPoint = namedtuple("SimplexPoint", ["loc", "val"])
    simplex = [SimplexPoint(loc=l, val=f(l)) for l in locs]

    # Algorithm parameters
    alpha = 1.0
    gamma = 2.0
    rho = 0.5
    sigma = 0.5
    max_iter = 1024

    while True:

        # 1. Order
        simplex.sort(key=attrgetter('val'))

        # 2. Centroid
        x0 = centroid([s.loc for s in simplex[:-1]])

        max_iter -= 1
        if max_iter < 0 or converged(x0, simplex, epsilon):
            return (named(simplex[0].loc), simplex[0].val)

        # (convenient names for best-point and value)
        xb = simplex[0].loc
        vb = simplex[0].val

        # (convenient names for worst-point and value)
        xw = simplex[-1].loc
        vw = simplex[-1].val

        # 3. Reflection
        xr = tup_add(x0, tup_mul(alpha, tup_sub(x0, xw)))
        vr = f(xr)
        if vb <= vr and vr < simplex[-2].val:
            simplex[-1] = SimplexPoint(loc=xr, val=vr)
            continue

        # 4. Expansion
        if vr < vb:
            xe = tup_add(x0, tup_mul(gamma, tup_sub(xr, x0)))
            ve = f(xe)
            if ve < vr:
                simplex[-1] = SimplexPoint(loc=xe, val=ve)
            else:
                simplex[-1] = SimplexPoint(loc=xr, val=vr)
            continue

        # 5. Contraction
        assert(vr >= simplex[-2].val)
        xc = tup_add(x0, tup_mul(rho, tup_sub(xw, x0)))
        vc = f(xc)
        if vc < vw:
            simplex[-1] = SimplexPoint(loc=xc, val=vc)
            continue

        # 6. Shrink
        simplex = (simplex[:1] +
                   [SimplexPoint(loc=l, val=f(l))
                    for l in [tup_add(xb, tup_mul(sigma, tup_sub(p.loc, xb)))
                              for p in simplex[1:]]])


# Nonlinear regression entrypoint
#
# Takes an objective function of type
#
#   objective: (params:namedtuple, x:float) -> y:float
#
# Along with a set of parameters, bounds on the parameters, and some xs and
# ys that make up a dataset. Creates a local function (over _just_
# parameters) that calculates the sum-of-squares-of-residuals between the
# objective-at-those-params and the data. Then runs a simple
# coordinate_descent nonlinear optimization on the parameter space until it
# converges. Then calculates the r_squared (coefficient of determination
# a.k.a. goodness-of-fit, a number betwee 0 and 1 with 1 meaning "fits
# perfectly") and finally returns (fit_params, r_squared).
def fit_function_to_data_by_least_squares(objective, params, bounds, xs, ys):

    assert(len(ys) > 0)
    mean_y = sum(ys) / len(ys)
    ss_total = sum((y - mean_y) ** 2 for y in ys)
    data = zip(xs, ys)

    def inner(ps):
        s = 0.0
        for (x, y) in data:
            s += (y - objective(ps, x)) ** 2
        return s

    retries = 100
    for _ in range(retries):
        (fit_params, ss_residuals) = Nelder_Mead_simplex(inner, params, bounds)
        if is_somewhat_small(ss_total):
            ss_total = somewhat_small
        if is_somewhat_small(ss_residuals / ss_total):
            r_squared = 1.0 - (ss_residuals / ss_total)
            return (fit_params, r_squared)
        else:
            # Bad fit, restart
            pass
    raise ValueError("Nelder-Mead failed %d retries" % retries)


# Fit a 2-parameter linear model f(x) = const + coeff * x to a set
# of data (lists of xs and ys). Returns (coeff, const, fit).
def fit_linear_model(xs, ys):
    # By the book: https://en.wikipedia.org/wiki/Simple_linear_regression
    n = float(len(xs))
    assert n == len(ys)
    if n == 0:
        return 0, 0, 1.0

    # Don't bother with anything fancy if function is constant.
    if all(y == ys[0] for y in ys):
        return (0.0, ys[0], 1.0)

    sum_x = sum(xs)
    sum_y = sum(ys)
    sum_prod = sum(a * b for a, b in zip(xs, ys))
    sum_x_sq = sum(a ** 2 for a in xs)
    sum_y_sq = sum(b ** 2 for b in ys)
    mean_x = sum_x / n
    mean_y = sum_y / n
    mean_prod = sum_prod / n
    mean_x_sq = sum_x_sq / n
    mean_y_sq = sum_y_sq / n
    covar_xy = mean_prod - mean_x * mean_y
    var_x = mean_x_sq - mean_x**2
    var_y = mean_y_sq - mean_y**2
    slope = covar_xy / var_x
    inter = mean_y - slope * mean_x

    # Compute the correlation coefficient aka r^2, to compare goodness-of-fit.
    if is_somewhat_small(var_y):
        # all of the outputs are the same, so this is a perfect fit
        assert is_somewhat_small(covar_xy)
        cor_coeff_sq = 1.0
    elif is_somewhat_small(var_x):
        # all of the inputs are the same, and the outputs are different, so
        # this is a completely imperfect fit
        assert is_somewhat_small(covar_xy)
        cor_coeff_sq = 0.0
    else:
        cor_coeff_sq = covar_xy**2 / (var_x * var_y)

    return slope, inter, cor_coeff_sq


# Fit a 3-parameter polynomial model f(x) = const + coeff * x^exp to a set
# of data (lists of xs and ys). Returns (exp, coeff, fit).
def fit_polynomial_model(xs, ys):

    PolynomialParams = namedtuple('PolynomialParams',
                                  ['const', 'coeff', 'exp'])
    params = PolynomialParams(const=0.0, coeff=1.0, exp=1.0)
    mag = max(abs(y) for y in ys)
    bounds = PolynomialParams(const=(0, mag),
                              coeff=(0, mag),
                              exp=(0.25, 8.0))

    def objective(params, x):
        return params.const + params.coeff * (x ** params.exp)

    (p, f) = fit_function_to_data_by_least_squares(objective,
                                                   params, bounds,
                                                   xs, ys)
    e = p.exp
    if is_somewhat_small(p.coeff):
        e = 0.0
    return (e, p.coeff, f)


# Fit a 3-parameter exponential model f(x) = const + coeff * base^x to
# a set of data (lists of xs and ys). Returns (base, coeff, fit).
def fit_exponential_model(xs, ys):
    ExponentialParams = namedtuple('ExponentialParams',
                                   ['base', 'coeff', 'const'])
    params = ExponentialParams(base=1.0, const=1.0, coeff=1.0)
    mag = max(abs(y) for y in ys)
    bounds = ExponentialParams(base=(0.0, 10.0),
                               coeff=(-mag, mag),
                               const=(-mag, mag))

    def objective(params, x):
        return params.const + params.coeff * (params.base ** x)

    (p, f) = fit_function_to_data_by_least_squares(objective,
                                                   params, bounds,
                                                   xs, ys)
    b = p.base
    if is_somewhat_small(p.coeff):
        b = 0.0
    return (b, p.coeff, f)


def self_test():
    import unittest

    class Tests(unittest.TestCase):

        def check_linearfit(self, xs, ys, lin, fit=1.0):
            (m, _, f) = fit_linear_model(xs, ys)
            print("linearfit(xs, ys, lin=%f, fit=%f) = (%f, %f)" %
                  (lin, fit, m, f))
            self.assertAlmostEqual(m, lin, places=1)
            self.assertAlmostEqual(f, fit, places=1)
            return f

        def check_polyfit(self, xs, ys, exp, fit=1.0):
            (e, _, f) = fit_polynomial_model(xs, ys)
            print("polyfit(xs, ys, exp=%f, fit=%f) = (%f, %f)" %
                  (exp, fit, e, f))
            self.assertAlmostEqual(e, exp, places=1)
            self.assertAlmostEqual(f, fit, places=1)
            return f

        def check_expfit(self, xs, ys, base, fit=1.0):
            (b, _, f) = fit_exponential_model(xs, ys)
            print("expfit(xs, ys, base=%f, fit=%f) = (%f, %f)" %
                  (base, fit, b, f))
            self.assertAlmostEqual(b, base, places=1)
            self.assertAlmostEqual(f, fit, places=1)
            return f

        def test_tuples(self):
            self.assertEqual(tup_distance((1, 0, 0), (0, 0, 0)), 1.0)
            self.assertEqual(tup_distance((1, 0, 0), (1, 0, 0)), 0.0)
            self.assertEqual(tup_distance((2, 0, 2, 0),
                                          (0, 2, 0, 2)), 4.0)
            self.assertEqual(tup_add((1, 0, 0), (1, 0, 0)), (2, 0, 0))
            self.assertEqual(tup_add((1, 3, 1), (1, 2, 5)), (2, 5, 6))
            self.assertEqual(centroid([(1, 0),
                                       (0, 1)]), (0.5, 0.5))
            self.assertEqual(centroid([(1, 0, 0, 0),
                                       (0, 1, 0, 0),
                                       (0, 0, 1, 0),
                                       (0, 0, 0, 1)]),
                             (0.25, 0.25, 0.25, 0.25))

        def test_constant(self):
            self.check_polyfit([1, 2, 3, 4, 5, 6],
                               [5, 5, 5, 5, 5, 5], 0)

        def test_linear1(self):
            self.check_polyfit([1, 2, 3, 4, 5, 6],
                               [1, 2, 3, 4, 5, 6], 1)

        def test_linear2(self):
            self.check_polyfit([1, 2, 3, 4, 5, 6],
                               [100, 200, 300, 400, 500, 600], 1)

        def test_linear3(self):
            self.check_polyfit([5, 10, 15],
                               [307, 632, 957], 1)

        # "Basically linear", with a little nonlinearity in the first
        # point. Polynomial-fit fails here because the simplex algorithm
        # keeps trying to account for the first point by admitting a
        # nonzero nonlinear term, thus bending the whole line instead of
        # focusing on the linear and constant terms. So we run an
        # independent fit on a "strictly linear" model too.
        def test_eventually_linear(self):
            self.check_linearfit([1, 2, 3, 4, 5, 6, 7, 8],
                                 [15, 20, 30, 40, 50, 60, 70, 80],
                                 9.6)

        # Double check that linear-fit (which "always fits") isn't
        # preferred over good nonlinear fits.
        def test_linear_model_of_poly(self):
            xs = [10, 20, 30, 40, 50, 60]
            ys = [100, 400, 900, 1600, 2500, 3600]
            lf = self.check_linearfit(xs, ys, 70)
            pf = self.check_polyfit(xs, ys, 2)
            self.assertGreater(pf, lf)

        def test_linear_model_of_poly_2(self):
            xs = [10, 20, 30, 40, 50, 60]
            ys = [1000, 8000, 27000, 64000, 125000, 216000]
            lf = self.check_linearfit(xs, ys, 4180, 0.87)
            pf = self.check_polyfit(xs, ys, 3)
            self.assertGreater(pf, lf)

        def test_linear_model_of_poly_3(self):
            xs = [1, 2, 3, 4, 5]
            ys = [1.0, 2.3, 3.74, 5.28, 6.9]
            lf = self.check_linearfit(xs, ys, 1.47)
            pf = self.check_polyfit(xs, ys, 1.2)
            self.assertGreater(pf, lf)

        def test_linear_model_of_poly_offset(self):
            xs = [10, 20, 30, 40, 50, 60]
            ys = [1100, 1400, 1900, 2600, 3500, 4600]
            lf = self.check_linearfit(xs, ys, 70)
            pf = self.check_polyfit(xs, ys, 2)
            self.assertGreater(pf, lf)

        def test_linear_offset(self):
            self.check_polyfit([1, 2, 3, 4, 5, 6],
                               [1000 + i for i in range(1, 7)], 1)

        def test_linear_offset_scaled(self):
            self.check_polyfit([1, 2, 3, 4, 5, 6],
                               [1000 + 2 * i for i in range(1, 7)], 1)

        def test_quadratic2(self):
            self.check_polyfit([10, 20, 30, 40, 50, 60],
                               [100, 400, 900, 1600, 2500, 3600], 2)

        def test_exp_model_of_quadratic(self):
            with self.assertRaises(ValueError):
                self.check_expfit([10, 20, 30, 40, 50, 60],
                                  [100, 400, 900, 1600, 2500, 3600], 2)

        def test_poly_model_of_exp(self):
            with self.assertRaises(ValueError):
                self.check_polyfit([10, 20, 30, 40, 50, 60],
                                   [1002, 1004, 1008, 1016, 1032], 2)

        def test_quadratic_offset(self):
            self.check_polyfit([10, 20, 30, 40, 50, 60],
                               [1100, 1400, 1900, 2600, 3500, 4600], 2)

        def test_expt(self):
            self.check_expfit([1, 2, 3, 4, 5],
                              [2, 4, 8, 16, 32], 2)

        def test_expt_offset(self):
            self.check_expfit([1, 2, 3, 4, 5],
                              [1002, 1004, 1008, 1016, 1032], 2)

        def test_expt_scale_offset(self):
            self.check_expfit([1, 2, 3, 4, 5],
                              [2004, 2008, 2016, 2032, 2064], 2)

    suite = unittest.TestLoader().loadTestsFromTestCase(Tests)
    return unittest.TextTestRunner(verbosity=2).run(suite)


def report(args, rng, runs):
    bad = False
    keys = set.intersection(*[set(j.keys()) for j in runs])
    if len(keys) == 0:
        print("No data found")
        if len(args.select) != 0:
            "(perhaps try a different --select?)"
        return True
    rows = []
    for k in keys:
        vals = [r[k] for r in runs]
        bounded = [max(v, 1) for v in vals]
        one_fit = False
        perfect_fit = False
        fit_r2_thresh = 0.99
        lin_b, lin_a, lin_r2 = fit_linear_model(rng, bounded)
        if lin_r2 > fit_r2_thresh:
            one_fit = True
        if lin_r2 == 1.0:
            perfect_fit = True
        p_b, p_a, p_r2 = (1.0, 1.0, 0.0)
        e_b, e_a, e_r2 = (1.0, 1.0, 0.0)
        try:
            if not perfect_fit:
                p_b, p_a, p_r2 = fit_polynomial_model(rng, bounded)
                if p_r2 > fit_r2_thresh:
                    one_fit = True
                if p_r2 == 1.0:
                    perfect_fit = True
        except ValueError:
            pass
        try:
            if not perfect_fit:
                e_b, e_a, e_r2 = fit_exponential_model(rng, bounded)
                if e_r2 > fit_r2_thresh:
                    one_fit = True
        except ValueError:
            pass
        if not one_fit:
            print("failed to fit model to " + repr(vals))
            return True
        if lin_r2 >= e_r2 and lin_r2 >= p_r2:
            # strict-linear is best
            rows.append((False, 0.0 if lin_b == 0 else 1.0, k, vals))
        elif p_r2 >= e_r2:
            # polynomial is best
            rows.append((False, p_b, k, vals))
        else:
            # exponential is best
            rows.append((True, e_b, k, vals))
    # Exponential fits always go after polynomial fits.
    rows.sort()
    for (is_exp, b, k, vals) in rows:
        # same threshold for both the polynomial exponent or the exponential
        # base.
        if is_exp:
            this_is_bad = b >= args.exponential_threshold
            formatted = '%1.1f^n' % b
        else:
            this_is_bad = b >= args.polynomial_threshold
            formatted = 'n^%1.1f' % b

        if this_is_bad:
            bad = True
        if not args.quiet or this_is_bad:
            print("O(%s) : %s" % (formatted, k))
            if args.values:
                print("                = ", vals)

    if args.invert_result:
        bad = not bad
    return bad


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        'file', type=argparse.FileType(),
        help='Path to GYB template file (defaults to stdin)', nargs='?',
        default=sys.stdin)
    parser.add_argument(
        '--values', action='store_true',
        default=False, help='print stat values')
    parser.add_argument(
        '--trace', action='store_true',
        default=False, help='trace compiler invocations')
    parser.add_argument(
        '--quiet', action='store_true',
        default=False, help='only print superlinear stats')
    parser.add_argument(
        '--polynomial-threshold', type=float,
        default=1.2,
        help='minimum exponent for polynomial fit to consider "bad scaling"')
    parser.add_argument(
        '--exponential-threshold', type=float,
        default=1.2,
        help='minimum base for exponential fit to consider "bad scaling"')
    parser.add_argument(
        '-parse', '--parse', action='store_true',
        default=False, help='only run compiler with -parse')
    parser.add_argument(
        '-typecheck', '--typecheck', action='store_true',
        default=False, help='only run compiler with -typecheck')
    parser.add_argument(
        '-g', '--debuginfo', action='store_true',
        default=False, help='run compiler with -g')
    parser.add_argument(
        '-wmo', '--whole-module-optimization', action='store_true',
        default=False, help='run compiler with -whole-module-optimization')
    parser.add_argument(
        '--dtrace', action='store_true',
        default=False, help='use dtrace to sample all functions')
    parser.add_argument(
        '-Xfrontend', action='append',
        default=[], help='pass additional args to frontend jobs')
    parser.add_argument(
        '--begin', type=int,
        default=10, help='first value for N')
    parser.add_argument(
        '--end', type=int,
        default=100, help='last value for N')
    parser.add_argument(
        '--step', type=int,
        default=10, help='step value for N')
    parser.add_argument(
        '--swiftc-binary',
        default="swiftc", help='swift binary to execute')
    parser.add_argument(
        '--tmpdir', type=str,
        default=None, help='directory to create tempfiles in')
    parser.add_argument(
        '--select',
        default="", help='substring of counters/symbols to limit attention to')
    parser.add_argument(
        '--debug', action='store_true',
        default=False, help='invoke lldb on each scale test')
    parser.add_argument(
        '--llvm-stat-reporter', action='store_true',
        default=False, help='only collect stats via old-style LLVM reporter')
    parser.add_argument(
        '--self-test', action='store_true',
        default=False, help='run arithmetic unit-tests of scale-test itself')
    parser.add_argument(
        '--expected-exit-code', type=int, default=0,
        help='exit code expected from the compiler invocation')
    parser.add_argument(
        '--invert-result', action='store_true',
        default=False, help='invert the result of the data fitting')

    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        '-O', '--optimize', action='store_true',
        default=False, help='run compiler with -O')
    group.add_argument(
        '-Onone', '--optimize-none', action='store_true',
        default=False, help='run compiler with -Onone')
    group.add_argument(
        '-Ounchecked', '--optimize-unchecked', action='store_true',
        default=False, help='run compiler with -Ounchecked')

    group = parser.add_mutually_exclusive_group()
    group.add_argument(
        '--multi-file', action='store_true',
        default=False, help='vary number of input files as well')
    group.add_argument(
        '--sum-multi', action='store_true',
        default=False, help='simulate a multi-primary run and sum stats')

    args = parser.parse_args(sys.argv[1:])
    if args.self_test:
        exit(self_test())
    (rng, runs) = run_many(args)
    if report(args, rng, runs):
        exit(1)
    exit(0)


if __name__ == '__main__':
    main()