def __call__(cls, M, predicate, block_size, invalidate_cache=lambda: None, threads=1, max_loops=8, **kwds): params = SieverParams(threads=threads) g6k = Siever(M, params) tracer = SieveTreeTracer(g6k, root_label="bkz-sieve", start_clocks=True) for b in range(20, block_size + 1, 10): pump_n_jump_bkz_tour(g6k, tracer, b, pump_params={"down_sieve": True}) auto_abort = BKZ.AutoAbort(M, M.d) found, ntests, solution = False, 0, None for tour in range(max_loops): pump_n_jump_bkz_tour(g6k, tracer, block_size, pump_params={"down_sieve": True}) invalidate_cache() if auto_abort.test_abort(): break with tracer.context("check"): for i, v in enumerate(M.B): ntests += 1 if predicate(v, standard_basis=True): solution = tuple([int(v_) for v_ in v]) found = True break if found: break tracer.exit() b0, b0e = M.get_r_exp(0, 0) return USVPPredSolverResults( success=found, ntests=ntests, solution=solution, b0=b0**(0.5) * 2**(b0e / 2.0), cputime=tracer.trace.data["cputime"], walltime=tracer.trace.data["walltime"], data=tracer.trace, )
def bkz_kernel(arg0, params=None, seed=None): """ Run the BKZ algorithm with different parameters. :param d: the dimension of the lattices to BKZ reduce :param params: parameters for BKZ: - bkz/alg: choose the underlying BKZ from {fpylll, naive, pump_n_jump, slide} - bkz/blocksizes: given as low:high:inc perform BKZ reduction with blocksizes in range(low, high, inc) (after some light) prereduction - bkz/pre_blocksize: prereduce lattice with fpylll BKZ up to this blocksize - bkz/tours: the number of tours to do for each blocksize - bkz/extra_dim4free: lift to indices extra_dim4free earlier in the lattice than the currently sieved block - bkz/jump: the number of blocks to jump in a BKZ tour after each pump - bkz/dim4free_fun: in blocksize x, try f(x) dimensions for free, give as 'lambda x: f(x)', e.g. 'lambda x: 11.5 + 0.075*x' - pump/down_sieve: sieve after each insert in the pump-down phase of the pump - slide/overlap: shift of the dual blocks when running slide reduction - challenge_seed: a seed to randomise the generated lattice - dummy_tracer: use a dummy tracer which capture less information - verbose: print tracer information throughout BKZ run """ # Pool.map only supports a single parameter if params is None and seed is None: d, params, seed = arg0 else: d = arg0 # params for underlying BKZ/workout/pump dim4free_fun = params.pop("bkz/dim4free_fun") extra_dim4free = params.pop("bkz/extra_dim4free") jump = params.pop("bkz/jump") overlap = params.pop("slide/overlap") pump_params = pop_prefixed_params("pump", params) workout_params = pop_prefixed_params("workout", params) # flow of the bkz experiment algbkz = params.pop("bkz/alg") blocksizes = params.pop("bkz/blocksizes") blocksizes = eval("range(%s)" % re.sub(":", ",", blocksizes)) pre_blocksize = params.pop("bkz/pre_blocksize") tours = params.pop("bkz/tours") # misc verbose = params.pop("verbose") dont_trace = params.pop("dummy_tracer", False) if blocksizes[-1] > d: print('set a smaller maximum blocksize with --blocksizes') return challenge_seed = params.pop("challenge_seed") A, bkz = load_prebkz(d, s=challenge_seed, blocksize=pre_blocksize) MM = GSO.Mat(A, float_type="double", U=IntegerMatrix.identity(A.nrows, int_type=A.int_type), UinvT=IntegerMatrix.identity(A.nrows, int_type=A.int_type)) g6k = Siever(MM, params, seed=seed) if dont_trace: tracer = dummy_tracer else: tracer = SieveTreeTracer(g6k, root_label=("bkz", d), start_clocks=True) if algbkz == "fpylll": M = bkz.M else: M = g6k.M T0 = time.time() for blocksize in blocksizes: for t in range(tours): with tracer.context("tour", t, dump_gso=True): if algbkz == "fpylll": par = BKZ_FPYLLL.Param( blocksize, strategies=BKZ_FPYLLL.DEFAULT_STRATEGY, max_loops=1) bkz(par) elif algbkz == "naive": naive_bkz_tour(g6k, tracer, blocksize, extra_dim4free=extra_dim4free, dim4free_fun=dim4free_fun, workout_params=workout_params, pump_params=pump_params) elif algbkz == "pump_and_jump": pump_n_jump_bkz_tour(g6k, tracer, blocksize, jump=jump, dim4free_fun=dim4free_fun, extra_dim4free=extra_dim4free, pump_params=pump_params) elif algbkz == "slide": slide_tour(g6k, dummy_tracer, blocksize, overlap=overlap, dim4free_fun=dim4free_fun, extra_dim4free=extra_dim4free, workout_params=workout_params, pump_params=pump_params) else: raise ValueError("bkz/alg=%s not recognized." % algbkz) if verbose: slope = basis_quality(M)["/"] fmt = "{'alg': '%25s', 'jump':%2d, 'pds':%d, 'extra_d4f': %2d, 'beta': %2d, 'slope': %.5f, 'total walltime': %.3f}" # noqa print(fmt % (algbkz + "+" + ("enum" if algbkz == "fpylll" else g6k.params.default_sieve), jump, pump_params["down_sieve"], extra_dim4free, blocksize, slope, time.time() - T0)) tracer.exit() slope = basis_quality(M)["/"] stat = tracer.trace try: stat.data["slope"] = np.array(slope) return stat except AttributeError: return None
def __call__(cls, M, predicate, invalidate_cache=lambda: None, preproc_offset=20, threads=1, ph=0, **kwds): # TODO bkz_sieve would be neater here if preproc_offset and M.d >= 40: bkz_res = usvp_pred_bkz_enum_solve( M, predicate, block_size=max(M.d - preproc_offset, 2), max_loops=8, threads=threads, invalidate_cache=invalidate_cache, ) ntests = bkz_res.ntests if bkz_res.success: # this might be enough return bkz_res else: bkz_res = None ntests = 0 from fpylll import IntegerMatrix # reduce size of entries B = IntegerMatrix(M.B.nrows, M.B.ncols) for i in range(M.B.nrows): for j in range(M.B.ncols): B[i, j] = M.B[i, j] // 2**ph params = SieverParams(reserved_n=M.d, otf_lift=False, threads=threads) g6k = Siever(B, params) tracer = SieveTreeTracer(g6k, root_label="sieve", start_clocks=True) g6k.initialize_local(0, M.d // 2, M.d) while g6k.l: g6k.extend_left() with tracer.context("sieve"): try: g6k() except SaturationError: pass # fill the database with g6k.temp_params(**kwds): g6k() invalidate_cache() found, solution = False, None with tracer.context("check"): for v in g6k.itervalues(): # heuristic: v has very small entries ntests += 1 if predicate(v, standard_basis=False): found = True solution = tuple( [int(v_) for v_ in g6k.M.B.multiply_left(v)]) break tracer.exit() cputime = tracer.trace.data[ "cputime"] + bkz_res.cputime if bkz_res else 0 walltime = tracer.trace.data[ "walltime"] + bkz_res.walltime if bkz_res else 0 b0, b0e = M.get_r_exp(0, 0) return USVPPredSolverResults( success=found, ntests=ntests, solution=solution, b0=b0**(0.5) * 2**(b0e / 2.0), cputime=cputime, walltime=walltime, data=tracer.trace, )
def __call__(cls, M, predicate, invalidate_cache=lambda: None, preproc_offset=20, threads=1, **kwds): if preproc_offset and M.d >= 40: bkz_res = usvp_pred_bkz_sieve_solve( M, predicate, block_size=max(M.d - preproc_offset, 2), max_loops=8, threads=threads, invalidate_cache=invalidate_cache, ) ntests = bkz_res.ntests if bkz_res.success: # this might be enough return bkz_res else: bkz_res = None ntests = 0 params = SieverParams(reserved_n=M.d, otf_lift=False, threads=threads) g6k = Siever(M, params) tracer = SieveTreeTracer(g6k, root_label="sieve", start_clocks=True) workout(g6k, tracer, 0, M.d, dim4free_min=0, dim4free_dec=15) invalidate_cache() found, solution = False, None with tracer.context("check"): # check if the workout solved it for us for i in range(g6k.M.d): ntests += 1 if predicate(g6k.M.B[i], standard_basis=True): found = True solution = tuple([int(v_) for v_ in g6k.M.B[i]]) break if found: tracer.exit() b0, b0e = M.get_r_exp(0, 0) return USVPPredSolverResults( success=found, ntests=ntests, solution=solution, b0=b0**(0.5) * 2**(b0e / 2.0), cputime=tracer.trace.data["cputime"], walltime=tracer.trace.data["walltime"], data=tracer.trace, ) with tracer.context("sieve"): try: g6k() except SaturationError: pass while g6k.l: g6k.extend_left() with tracer.context("sieve"): try: g6k() except SaturationError: pass # fill the database with g6k.temp_params(**kwds): g6k() invalidate_cache() with tracer.context("check"): for i in range(g6k.M.d): ntests += 1 if predicate(g6k.M.B[i], standard_basis=True): found = True solution = tuple([int(v_) for v_ in g6k.M.B[i]]) break if not found: for v in g6k.itervalues(): ntests += 1 if predicate(v, standard_basis=False): found = True solution = tuple( [int(v_) for v_ in g6k.M.B.multiply_left(v)]) break tracer.exit() cputime = tracer.trace.data[ "cputime"] + bkz_res.cputime if bkz_res else 0 walltime = tracer.trace.data[ "walltime"] + bkz_res.walltime if bkz_res else 0 b0, b0e = M.get_r_exp(0, 0) return USVPPredSolverResults( success=found, ntests=ntests, solution=solution, b0=b0**(0.5) * 2**(b0e / 2.0), cputime=cputime, walltime=walltime, data=tracer.trace, )