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The General Sieve Kernel (G6K)

G6K is a C++ and Python library that implements several Sieve algorithms to be used in more advanced lattice reduction tasks. It follows the stateful machine framework from:

Martin R. Albrecht and Léo Ducas and Gottfried Herold and Elena Kirshanova and Eamonn W. Postlethwaite and Marc Stevens, The General Sieve Kernel and New Records in Lattice Reduction.

The article is available in this repository and on eprint .

Building the library

You will need the current master of FPyLLL. See for creating (almost) all dependencies from scratch:

# once only: creates local python env, builds fplll, fpylll and G6K
./ [ -j # ]

# for every new shell: activates local python env
source ./activate

On systems with co-existing python2 and 3, you can force a specific version installation using PYTHON=<pythoncmd> ./ instead. The number of parallel compilation jobs can be controlled with -j #.

If building via `./` fails, then the script will return an error code. The error codes are documented in ``

Otherwise, you will need fplll and fpylll already installed and build the G6K Cython extension like so:

pip install -r requirements.txt
python build_ext --inplace [ -j # ]

This builds G6K in place. Alternatively, you can skip `--inplace` and run `python install` as usual after building.

It's possible to alter the C++ kernel build configuration as follows:

make clean
./configure [opts...]           # e.g. opts: --enable-native --enable-templated-dim --with-max-sieving-dim=128
                                # see ./configure --help for more options
python build_ext [ -j # ]


python -m pytest

Gathering test coverage

Uncomment the line extra_compile_args += ["-DCYTHON_TRACE=1"] in setup py. and recompile. Then run

py.test --cov=g6k

Reproducing experiments of the paper for the command line

3-sieve (Sec 5.1)

To recreate Figure 2, run (if you have 26 threads, otherwise change --threads and, possibly, decrease the dimension):

python ./ 100 --sieve hk3 --seed 23 --trials 2 --threads 26 --db_size_base 1.140174986570044 1.1414898159861084 1.1428031326523391 1.1441149417781413 1.14542524854309 1.146734058097168 1.1480413755610026 1.1493472060 1.153255825912013 1.154555758722808 1.1547005383

The whole experiment took ~15 h. If you do not want to wait that long, decrease the dimension. Note : Asymptotically, one would need to adjust the saturation_radius accordingly. However, at these dimensions, the default db_size_factor was large enough to accomodate saturation in practce.

Exact-SVP (Sec 6.1)

Before benchmarking for exact-SVP, one must first determine the length of the shortest vector. To do so on 3 lattices in each dimensions d ∈ {50, 52, 54, 56, 58}:

python ./ 50 -u 60 --workers 4 --challenge-seed 0 1 2

This will run 4 independent tasks in parrallel, and takes about 1 minute. Challenges will be downloaded from if not already present.

Then, run and obtain averaged timing:

python ./ 50 -u 60 --workers 3 --challenge-seed 0 1 2

Which will take around 10 seconds. To compare several algorithms, and average over 5 trials on each of the 3 lattices for d=50, you can run:

python ./ 50 --workers 3 --trials 5 --challenge-seed 0 1 2 --svp/alg workout enum

SVP-challenge (Sec 6.2)

You can here run a single instance on multiple cores, for example:

python ./ 100 --threads 4

The above may take between half a minute and 10 minutes depending on how lucky you are

BKZ (Sec 6.3)

To recreate the experiments in the paper run:

python 180 --bkz/betas 60:95:1 --bkz/pre_beta 59 --trials 8 --workers 8
python 180 --bkz/betas 60:93:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/extra_d4f 12
python 180 --bkz/betas 60:97:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/extra_d4f 12 --bkz/jump 3
python 180 --bkz/betas 60:85:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/alg naive
python 180 --bkz/betas 60:82:1 --bkz/pre_beta 59 --trials 8 --workers 8 --bkz/alg fpylll

LWE (Sec 6.4)

To automatically attempt to solve a Darmstadt LWE Challenge (n, alpha) run:

python n --lwe/alpha alpha

Other CLI programs and commands

It is also possible ot ask for HKZ reduction with and; the former really tries hard to get a HKZ basis (with no formal guarentees though) while the latter is providing something close to a HKZ basis significantly significantly faster than the former.

Other options: Each of the parameters PARAM listed in g6k/siever_param.pyx can be set-up to a value VAL from the command line


Though some of them may be overwritten by the call chain. A subset of reasonable parameter to play with are:

threads                         # Number of threads collaborating in a single g6k instance. Default=1
sample_by_sums                  # When increasing the db size, do that aggressively by sampling vectors as sums of existing vectors. Default=True
otf_lift                        # Lift vectors on the fly; slower per sieve, but highter probability to find a short vector in the lift context. Default=True
lift_radius                     # Bound (relative to squared-GH) to try to lift a vector on the fly. Default=1.7
saturation_ratio                # Stop the sieve when this ratio of vector has been found compared to the expected number of vector. Default=.5
saturation_radius               # Define the ball square-radius for the saturation_ratio condition. Default=1.333333333
dual_mode                       # Implicitly run all operations on the dual-basis (in reversed order).

Other parameters specific to subprograms SUBPRG∊{pump, workout, bkz} can be set-up to a value VAL form the CLI by adding the option


One can also specify a set of values, or a range of value, to iterate over


One can find all the available option by browsing through the programs in the g6k/algorithms/ subdirectory.

It is also possible to plot or to output the so called `profile', namely the logarithmic plot of the Gram-Schmidt norms, with the option

--profile filename.csv      #exporting raw data as column seperated values
--profile filename.EXT      #for EXT∊{png,pdf,...} plot in a file, requires matplotlib
--profile show              #plot in a pop-up window, requires matplotlib

Interactive use of G6K from Python

General Sieving Kernel. We start by importing the siever and FPYLLL

>>> from fpylll import IntegerMatrix, LLL, FPLLL
>>> from g6k import Siever

Construct a challenge instance

>>> FPLLL.set_random_seed(0x1337)
>>> A = IntegerMatrix.random(50, "qary", k=25, bits=20)
>>> A = LLL.reduction(A)

Construct the instance

>>> g6k = Siever(A)
>>> g6k.initialize_local(0, 0, 50)
>>> g6k(alg="gauss")

We recover the shortest vector found. Best lift returns the index, the squared norm and the vector expressed in base A:

>>> i, norm, coeffs = g6k.best_lifts()[0]
>>> l = int(round(norm))
>>> l < 3710000

To test the answer we compute:

>>> v = A.multiply_left(coeffs)
>>> sum(v_**2 for v_ in v) == l

More examples can be found in the folder examples.


This project was supported through the European Union PROMETHEUS project (Horizon 2020 Research and Innovation Program, grant 780701), EPSRC grant EP/P009417/1 and EPSRC grant EP/S020330/1.