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flintlib
arb
C2016–2023
flintlib / arb

arb

Arb has been merged into FLINT -- use https://github.com/flintlib/flint/ instead

Science & Engineering
Explore on GitHubRead Hacker News discussions
6Distinct people
6Mentions
0Explicit recommenders
482GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Arb has been merged into FLINT -- use https://github.com/flintlib/flint/ instead

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2016-02-16 16:43:02 UTC to 2023-03-31 12:50:02 UTC.

0122016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20162017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101

What Hacker News says

6 sources
whyever·Jan 18, 2018
> “In the current art, static error analysis requires significant mathematical analysis and cannot determine actual error in real time,” reads a section of the patent. “This work must be done by highly skilled mathematician programmers. Therefore, error analysis is only used for critical projects because of the greatly increased cost and time required. In contrast, the present invention provides error computation in real time with, at most, a small increase in computation time and a small increase in the maximum number of bits available for the significand.” I'm not sure how much it increases computation time, but software for exactly this is freely available, see for instance Arb: https://github.com/fredrik-johansson/arb
In Inventor Claims to Have Solved Floating Point Error Problem
fdej·Feb 16, 2016
Author of mpmath here. It depends a lot on what you're doing. A single floating-point arithmetic operation in mpmath at low precision involves something like 100 "Python cycles" (bytecode ops), each of which takes perhaps 100 machine cycles. That makes it, roughly: 100 times slower than machine arithmetic in Python. 10000 times slower than machine arithmetic in C (or NumPy if you can vectorize your code fully). 100 times slower than arbitrary precision floating-point arithmetic implemented in C (~100 machine cycles). These are obviously just order of magnitude estimates. However, low precision is the worst case, relatively speaking. mpmath uses Python longs internally, and it can also use GMPY when available. At sufficiently high precision, the time is dominated the Python/GMP kernel for multiplying integers and performance is close to ot
Read the rest
her bignum implementations. Also, for computing transcendental functions, mpmath uses fixed-point arithmetic internally, which reduces overhead a lot. The biggest problem with mpmath is that it doesn't implement algorithms that scale optimally for all operations, and a lot of the error analysis is completely nonrigorous. Since 2012, I have been developing a C library (https://github.com/fredrik-johansson/arb/) that solves many of the shortcomings of mpmath. It is obviously much faster at low precision (the factor 100 mentioned above), it generally uses much better algorithms, and it tracks error bounds automatically using interval arithmetic.
In Mpmath – Python library for arbitrary-precision floating-point arithmetic

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