Commit Graph

4 Commits

Author SHA1 Message Date
Paul Eggert
dff8da6b3e Update copyright dates with scripts/update-copyrights 2024-01-01 10:53:40 -08:00
Joseph Myers
6d7e8eda9b Update copyright dates with scripts/update-copyrights 2023-01-06 21:14:39 +00:00
Adhemerval Zanella
c622ac1b86 stdlib: Simplify arc4random_uniform
It uses the bitmask with rejection [1], which calculates a mask
being the lowest power of two bounding the request upper bound,
successively queries new random values, and rejects values
outside the requested range.

Performance-wise, there is no much gain in trying to conserve
bits since arc4random is wrapper on getrandom syscall.  It should
be cheaper to just query a uint32_t value.  The algorithm also
avoids modulo and divide operations, which might be costly
depending of the architecture.

[1] https://www.pcg-random.org/posts/bounded-rands.html

Reviewed-by: Yann Droneaud <ydroneaud@opteya.com>
2022-08-01 14:37:24 -03:00
Adhemerval Zanella Netto
6f4e0fcfa2 stdlib: Add arc4random, arc4random_buf, and arc4random_uniform (BZ #4417)
The implementation is based on scalar Chacha20 with per-thread cache.
It uses getrandom or /dev/urandom as fallback to get the initial entropy,
and reseeds the internal state on every 16MB of consumed buffer.

To improve performance and lower memory consumption the per-thread cache
is allocated lazily on first arc4random functions call, and if the
memory allocation fails getentropy or /dev/urandom is used as fallback.
The cache is also cleared on thread exit iff it was initialized (so if
arc4random is not called it is not touched).

Although it is lock-free, arc4random is still not async-signal-safe
(the per thread state is not updated atomically).

The ChaCha20 implementation is based on RFC8439 [1], omitting the final
XOR of the keystream with the plaintext because the plaintext is a
stream of zeros.  This strategy is similar to what OpenBSD arc4random
does.

The arc4random_uniform is based on previous work by Florian Weimer,
where the algorithm is based on Jérémie Lumbroso paper Optimal Discrete
Uniform Generation from Coin Flips, and Applications (2013) [2], who
credits Donald E. Knuth and Andrew C. Yao, The complexity of nonuniform
random number generation (1976), for solving the general case.

The main advantage of this method is the that the unit of randomness is not
the uniform random variable (uint32_t), but a random bit.  It optimizes the
internal buffer sampling by initially consuming a 32-bit random variable
and then sampling byte per byte.  Depending of the upper bound requested,
it might lead to better CPU utilization.

Checked on x86_64-linux-gnu, aarch64-linux, and powerpc64le-linux-gnu.

Co-authored-by: Florian Weimer <fweimer@redhat.com>
Reviewed-by: Yann Droneaud <ydroneaud@opteya.com>

[1] https://datatracker.ietf.org/doc/html/rfc8439
[2] https://arxiv.org/pdf/1304.1916.pdf
2022-07-22 11:58:27 -03:00