This is the code to accompany our eponymous paper: Sanders, P., Lamm, S., Hübschle-Schneider, L., Schrade, E. and Dachsbacher, C., 2018. Efficient Parallel Random Sampling—Vectorized, Cache-Efficient, and Online. ACM Transactions on Mathematical Software (TOMS), 44.3 (2018): 29.
You can find the freely accessible author's version in the arXiv.
If you use this library in the context of an academic publication, we ask that you cite our paper:
@article{sanders2018sampling,
title = {Efficient Parallel Random Sampling---Vectorized, Cache-Efficient, and Online},
author = {Sanders, Peter and Lamm, Sebastian and H{\"u}bschle-Schneider, Lorenz
and Schrade, Emanuel and Dachsbacher, Carsten},
journal = {ACM Trans. Math. Softw.},
year = {2018},
issn = {0098-3500},
volume = {44},
number = {3},
articleno = {29},
pages = {29:1--29:14},
issue_date = {April 2018},
publisher = {ACM},
doi = {10.1145/3157734}
}Build with cmake (version 2.8.12 or later is required). Remember to fetch the submodules before compiling: git submodule update --init. A compiler compatible with C++11 is required.
Optional Dependencies: An MPI library for Algorithm P's test runner (Algorithm P itself is implemented independently of MPI), Intel Math Kernel Library (MKL) for faster random variate generation
To run the tests, set the cmake variable SAMPLING_BUILD_TESTS to ON. Make sure that the extlib/googletest submodule is present. Build, then execute the tests with ctest.
We plan to make the test suite more comprehensive in the future.
License: 2-clause BSD
