Specialized Division and Remainder Algorithms
Currently, this crate provides an alternative form of long division that is faster than the functions provided by Rust primitives on some CPUs.
Note that setting the the flag for compilation to a native cpu can make a significant performance improvement for these functions.
Additionally, there is an
asm feature flag, which uses assembly to improve the performance of some benchmarks.
Most division algorithms end up doing most of the work to get both the quotient and remainder, which is why these functions return both (and the compiler can inline and optimize away unused results and calculations).
On naming conventions:
_div functions should really be named
_quo (quotient) functions, and it would stop the name collision with
div for divisor, but to keep consistency with
std it is kept as
duo is named as such because the variable is kept around and subtracted from inside division functions until it becomes the remainder (so it works as both the dividend and the remainder). This is more apparent when working with allocated big integers (such as with the
apint crate) that use the input
duo for intermediate storages and ends up as the remainder.
Please file an issue or PR if you want to see these or others implemented.
asmfeature flag currently only makes a difference for
x86_64targets, but could be extended to include more architectures.
- Other algorithms could be included
- The algorithm could be extended to allow for faster 128 bit division on 32 bit hardware division capable computers, and more.
- Every feature added multiplies the number of functions that must be supplied, so maybe it makes more sense to make only the macros public so that users make only the functions they need.
- The algorithm is actually 3 different algorithms for different sized dividends that could be split up into their own specialized functions. Note: after inspecting assembly used in practice, it seems that the compiler likes to inline the division function with the right branches and returns, so the advantages would be minimal.
cargo bench on this library, it runs division operations 32 times on an array of random numbers.
The names of the benchmarks specify 4 things:
- the type of integer being operated on - whether the quotient (`_div`) or remainder (`_rem`) or both (`div_rem`) are calculated - the size of the numbers being entered (specifically, how many lower bits of the random integer are being kept) - whether Rust's current algorithm or the algorithm in this crate is being used
For example, the
u128_div_rem_126_64_new benchmark tests how long it takes to find 32 quotients
and remainders of a u128 random integer with the top 2 bits zeroed, divided by a u128 random integer
with the top 64 bits zeroed.
constant_u128_div_rem benchmark is a hardcoded benchmark working on a constant array of integers.
_baseline benchmarks are just there to make sure that the process of adding the 32 answers together (to prevent the compiler from optimizing away benchmarking code) does not take a lot of time.
_new benches are using the algorithm in this library and the
_std benches are using the algorithms Rust is using for
On an Intel i3-3240, the benchmarks look like this. Note that all of the 128 bit benches show improvement of the long division over the one Rust is using.
test constant_u128_div_rem_new ... bench: 1,391 ns/iter (+/- 1,486) test constant_u128_div_rem_std ... bench: 3,128 ns/iter (+/- 439) test i128_div_rem_128_96_new ... bench: 944 ns/iter (+/- 306) test i128_div_rem_128_96_std ... bench: 3,929 ns/iter (+/- 1,045) test u128_baseline ... bench: 67 ns/iter (+/- 0) test u128_div_128_96_new ... bench: 1,087 ns/iter (+/- 169) test u128_div_128_96_std ... bench: 3,611 ns/iter (+/- 336) test u128_div_rem_126_64_new ... bench: 1,245 ns/iter (+/- 583) test u128_div_rem_126_64_std ... bench: 6,492 ns/iter (+/- 138) test u128_div_rem_128_128_new ... bench: 446 ns/iter (+/- 106) test u128_div_rem_128_128_std ... bench: 887 ns/iter (+/- 144) test u128_div_rem_128_32_new ... bench: 1,157 ns/iter (+/- 560) test u128_div_rem_128_32_std ... bench: 9,582 ns/iter (+/- 889) test u128_div_rem_128_64_new ... bench: 1,504 ns/iter (+/- 1,056) test u128_div_rem_128_64_std ... bench: 6,587 ns/iter (+/- 173) test u128_div_rem_128_96_new ... bench: 993 ns/iter (+/- 1,067) test u128_div_rem_128_96_std ... bench: 3,741 ns/iter (+/- 2,889) test u128_rem_128_96_new ... bench: 1,017 ns/iter (+/- 1,075) test u128_rem_128_96_std ... bench: 3,691 ns/iter (+/- 172) (the 64 and 32 bit benches are not included here because the algorithm is not faster on this cpu)
On an AMD FX-9800P RADEON R7
test constant_u128_div_rem_new ... bench: 1,966 ns/iter (+/- 177) test constant_u128_div_rem_std ... bench: 4,756 ns/iter (+/- 611) test i128_div_rem_128_96_new ... bench: 1,422 ns/iter (+/- 128) test i128_div_rem_128_96_std ... bench: 6,366 ns/iter (+/- 827) test u128_baseline ... bench: 72 ns/iter (+/- 9) test u128_div_128_96_new ... bench: 3,030 ns/iter (+/- 690) test u128_div_128_96_std ... bench: 12,508 ns/iter (+/- 1,059) test u128_div_rem_126_64_new ... bench: 2,707 ns/iter (+/- 188) test u128_div_rem_126_64_std ... bench: 21,702 ns/iter (+/- 1,039) test u128_div_rem_128_128_new ... bench: 1,481 ns/iter (+/- 152) test u128_div_rem_128_128_std ... bench: 3,135 ns/iter (+/- 449) test u128_div_rem_128_32_new ... bench: 3,672 ns/iter (+/- 55) test u128_div_rem_128_32_std ... bench: 31,440 ns/iter (+/- 513) test u128_div_rem_128_64_new ... bench: 4,709 ns/iter (+/- 297) test u128_div_rem_128_64_std ... bench: 22,101 ns/iter (+/- 1,778) test u128_div_rem_128_96_new ... bench: 3,273 ns/iter (+/- 610) test u128_div_rem_128_96_std ... bench: 12,776 ns/iter (+/- 1,142) test u128_rem_128_96_new ... bench: 3,013 ns/iter (+/- 317) test u128_rem_128_96_std ... bench: 12,411 ns/iter (+/- 184 (the 64 and 32 bit benches are not included here because the algorithm is not faster on this cpu)