def plain_sieve():
"""
Run a a plain sieve in the full dimension directly.
"""
description = plain_sieve.__doc__
args, all_params = parse_args(description,)
stats = run_all(plain_sieve_kernel, list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed)
inverse_all_params = OrderedDict([(v, k) for (k, v) in six.iteritems(all_params)])
inverse_all_params = OrderedDict([(v, k) for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, |db|: 2^{avg_max:.2f}"
profiles = print_stats(fmt, stats, ("cputime", "walltime", "avg_max"),
extractf={"avg_max": lambda n, params, stat: db_stats(stat)[0]})
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(stats, open("plain-sieve-%d-%d-%d-%d.sobj" %
(args.lower_bound, args.upper_bound, args.step_size, args.trials), "wb"))
def bkz_tour():
"""
Run bkz tours.
.. note :: that by default no information is printed.
To enable set ``--dummy-tracer False`` and ``--verbose``.
"""
description = bkz_tour.__doc__
args, all_params = parse_args(
description,
bkz__alg="pump_and_jump",
bkz__blocksizes="40:51:2",
bkz__pre_blocksize=39,
bkz__tours=1,
bkz__extra_dim4free=0,
bkz__jump=1,
bkz__dim4free_fun="default_dim4free_fun",
slide__overlap=1,
pump__down_sieve=True,
challenge_seed=0,
dummy_tracer=False, # set to control memory
verbose=False)
stats = run_all(bkz_kernel,
list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed)
inverse_all_params = OrderedDict([(v, k)
for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, slope: {slope:1.5f}, |db|: 2^{avg_max:.2f}"
profiles = print_stats(fmt,
stats, ("cputime", "walltime", "slope", "avg_max"),
extractf={
"avg_max":
lambda n, params, stat: db_stats(stat)[0]
})
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(
stats,
open(
"bkz-%d-%d-%d-%d.sobj" % (args.lower_bound, args.upper_bound,
args.step_size, args.trials), "wb"))
def asvp():
"""
Run a Workout until 1.05-approx-SVP on matrices with dimensions in ``range(lower_bound, upper_bound, step_size)``.
"""
description = asvp.__doc__
args, all_params = parse_args(
description,
load_matrix=None,
verbose=True,
challenge_seed=0,
workout__dim4free_dec=3,
)
stats = run_all(
asvp_kernel,
list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed,
)
inverse_all_params = OrderedDict([(v, k)
for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, flast: {flast:3.2f}, |db|: 2^{avg_max:.2f}"
profiles = print_stats(
fmt,
stats,
("cputime", "walltime", "flast", "avg_max"),
extractf={
"avg_max": lambda n, params, stat: db_stats(stat)[0]
},
)
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(
stats,
open(
"svp-challenge-%d-%d-%d-%d.sobj" %
(args.lower_bound, args.upper_bound, args.step_size,
args.trials),
"wb",
),
)
def svp():
"""
Run a progressive until exact-SVP is solved.
The exact-SVP length must have been priorly determined using ./svp_exact_find_norm.py
"""
description = svp.__doc__
args, all_params = parse_args(description,
challenge_seed=0,
svp__alg="workout")
stats = run_all(
svp_kernel,
list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed,
)
inverse_all_params = OrderedDict([(v, k)
for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, flast: {flast:3.2f}, |db|: 2^{avg_max:.2f}"
profiles = print_stats(
fmt,
stats,
("cputime", "walltime", "flast", "avg_max"),
extractf={
"avg_max": lambda n, params, stat: db_stats(stat)[0]
},
)
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(
stats,
open(
"svp-exact-%d-%d-%d-%d.sobj" %
(args.lower_bound, args.upper_bound, args.step_size,
args.trials),
"wb",
),
)
def full_sieve():
"""
Run a a full sieve (with some partial sieve as precomputation).
"""
description = full_sieve.__doc__
args, all_params = parse_args(description, challenge_seed=0)
stats = run_all(
full_sieve_kernel,
list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed,
)
inverse_all_params = OrderedDict([(v, k)
for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, |db|: 2^{avg_max:.2f}"
profiles = print_stats(
fmt,
stats,
("cputime", "walltime", "avg_max"),
extractf={
"avg_max": lambda n, params, stat: db_stats(stat)[0]
},
)
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(
stats,
open(
"full-sieve-%d-%d-%d-%d.sobj" %
(args.lower_bound, args.upper_bound, args.step_size,
args.trials),
"wb",
),
)
def hkz():
"""
Attempt HKZ reduction.
"""
description = hkz.__doc__
args, all_params = parse_args(description,
challenge_seed=0,
pump__down_sieve=True,
pump__down_stop=9999,
saturation_ratio=.8,
pump__prefer_left_insert=10,
workout__dim4free_min=0,
workout__dim4free_dec=15)
stats = run_all(hkz_kernel,
list(all_params.values()),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed)
inverse_all_params = OrderedDict([(v, k)
for (k, v) in six.iteritems(all_params)])
stats = sanitize_params_names(stats, inverse_all_params)
fmt = "{name:50s} :: n: {n:2d}, cputime {cputime:7.4f}s, walltime: {walltime:7.4f}s, |db|: 2^{avg_max:.2f}"
profiles = print_stats(fmt,
stats, ("cputime", "walltime", "avg_max"),
extractf={
"avg_max":
lambda n, params, stat: db_stats(stat)[0]
})
output_profiles(args.profile, profiles)
if args.pickle:
pickler.dump(
stats,
open(
"hkz-%d-%d-%d-%d.sobj" % (args.lower_bound, args.upper_bound,
args.step_size, args.trials), "wb"))