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author | Raymond Hettinger <rhettinger@users.noreply.github.com> | 2020-09-06 22:10:07 (GMT) |
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committer | GitHub <noreply@github.com> | 2020-09-06 22:10:07 (GMT) |
commit | 67c998de24985b36498b0fdac68d1beceb43aba7 (patch) | |
tree | 9c1010cb414b07a2c3244c0fbd9dcdddd77064a4 /Modules/mathmodule.c | |
parent | 63f102fe079ecb5cb7b921a1cf8bce4077a9d7e2 (diff) | |
download | cpython-67c998de24985b36498b0fdac68d1beceb43aba7.zip cpython-67c998de24985b36498b0fdac68d1beceb43aba7.tar.gz cpython-67c998de24985b36498b0fdac68d1beceb43aba7.tar.bz2 |
bpo-41513: Expand comments and add references for a better understanding (GH-22123)
Diffstat (limited to 'Modules/mathmodule.c')
-rw-r--r-- | Modules/mathmodule.c | 26 |
1 files changed, 21 insertions, 5 deletions
diff --git a/Modules/mathmodule.c b/Modules/mathmodule.c index d227a5d..29137ae 100644 --- a/Modules/mathmodule.c +++ b/Modules/mathmodule.c @@ -2419,9 +2419,9 @@ To avoid overflow/underflow and to achieve high accuracy giving results that are almost always correctly rounded, four techniques are used: * lossless scaling using a power-of-two scaling factor -* accurate squaring using Veltkamp-Dekker splitting -* compensated summation using a variant of the Neumaier algorithm -* differential correction of the square root +* accurate squaring using Veltkamp-Dekker splitting [1] +* compensated summation using a variant of the Neumaier algorithm [2] +* differential correction of the square root [3] The usual presentation of the Neumaier summation algorithm has an expensive branch depending on which operand has the larger @@ -2456,7 +2456,11 @@ Given that csum >= 1.0, we have: Since lo**2 is less than 1/2 ulp(csum), we have csum+lo*lo == csum. To minimize loss of information during the accumulation of fractional -values, each term has a separate accumulator. +values, each term has a separate accumulator. This also breaks up +sequential dependencies in the inner loop so the CPU can maximize +floating point throughput. [5] On a 2.6 GHz Haswell, adding one +dimension has an incremental cost of only 5ns -- for example when +moving from hypot(x,y) to hypot(x,y,z). The square root differential correction is needed because a correctly rounded square root of a correctly rounded sum of @@ -2466,7 +2470,7 @@ The differential correction starts with a value *x* that is the difference between the square of *h*, the possibly inaccurately rounded square root, and the accurately computed sum of squares. The correction is the first order term of the Maclaurin series -expansion of sqrt(h**2 + x) == h + x/(2*h) + O(x**2). +expansion of sqrt(h**2 + x) == h + x/(2*h) + O(x**2). [4] Essentially, this differential correction is equivalent to one refinement step in Newton's divide-and-average square root @@ -2474,12 +2478,24 @@ algorithm, effectively doubling the number of accurate bits. This technique is used in Dekker's SQRT2 algorithm and again in Borges' ALGORITHM 4 and 5. +Without proof for all cases, hypot() cannot claim to be always +correctly rounded. However for n <= 1000, prior to the final addition +that rounds the overall result, the internal accuracy of "h" together +with its correction of "x / (2.0 * h)" is at least 100 bits. [6] +Also, hypot() was tested against a Decimal implementation with +prec=300. After 100 million trials, no incorrectly rounded examples +were found. In addition, perfect commutativity (all permutations are +exactly equal) was verified for 1 billion random inputs with n=5. [7] + References: 1. Veltkamp-Dekker splitting: http://csclub.uwaterloo.ca/~pbarfuss/dekker1971.pdf 2. Compensated summation: http://www.ti3.tu-harburg.de/paper/rump/Ru08b.pdf 3. Square root differential correction: https://arxiv.org/pdf/1904.09481.pdf 4. https://www.wolframalpha.com/input/?i=Maclaurin+series+sqrt%28h**2+%2B+x%29+at+x%3D0 +5. https://bugs.python.org/file49439/hypot.png +6. https://bugs.python.org/file49435/best_frac.py +7. https://bugs.python.org/file49448/test_hypot_commutativity.py */ |