Программист доказал возможность замены медленных целочисленных делений на быстрые операции с плавающей точкой

August 10th, 2026 Integer division q=(x/y) and remainder (of Euclidean division) r=(x%y) hardware operations are very sad on current hardware. Typically very long latency and poor throughput. In contrast floating-point division is pretty happy: shorter latency, higher throughput and often more execution units to perform the operation. So there are cases it could be interesting to move some integer div/mod operations to floating point. But it’s PITA right? Actually I think it’s easy. The math is pretty straightforward so if I’ve made a mistake I expect to find out rather soon. My claim is: for two integers x & y (signed or unsigned) that fit in 53/24 bits for double/single precision respectively, with both promoted to floating point then: // floating-point: // d is the same integer as integer divide x / y // m is the same integer as integer remainder x % y // (in standard rounding mode: round-to-nearest, ties to even) d = trunc(x/y); // floor works for unsigned m = -fma(d,y,-x); // fma required // NOTE: if only want 'd' and it's being converted to an // integer then the truncate or floor operation is // free in the float to integer conversion. in standard rounding mode. From here on I will only consider unsigned integers since it’s the harder case (signed have a smaller max magnitude) but recall that floating point effective stores a signed magnitude quantity. Some practical points: this works by setting rounding-mode to TOWARD_ZERO and then back once done but hitting control words is often very expensive some hardware have division opcodes that allow choosing the rounding mode on some operations like division. Example AVX-512 has instrinsic: _mm_div_round_sd with example listed latency of 14 cycles. converting int to float and back has a cost. (NOTE: the majority of x64 CPUs don’t have an op for unsigned to float and back. So this is an implemenation concern.) obviously the floating point precision $p$ places the upper bound on integer width. If this works for $p$ then all smaller widths trivally work. Examples 32-bit integers in doubles ($p=53$) and 16-bit integers in singles ($p=24$). In this case it’s possible that there’s some “trickery” to side-step the int to float. obviously the most promising case is for working in SIMD to amortize overhead. for constant divisors your compiler should eliminate. for runtime known divisors that are going to be reused a small number of times there’s software solutions like libdivide. Note that there are proven methods (SEE: Formally verified 32- and 64-bit integer division using double-precision floating-point arithmetic) The standard rounding mode is: round-to-nearest (ties to even) and the first important observation is the ties part. A tie happens when the exact result of an operation is exactly at the midpoint between two floating point numbers. Floating point division (in our case of base-2 and same working precision) has zero midpoints (SEE: Midpoints and exact points of some algebraic functions in floating-point arithmetic section 6.1, corollary 1). Therefore there’s never a tie and only round-to-nearest portion applies. Let’s look an example using a 4-bit precision floating point format with all possible configurations where the exact result is less than one but as close as possible to rounding up: |GRS x = don't care .1111|0xx .1111 (no rounding) .1111|100 tie case is impossible .1111|101 1.000 (round up) .1111|11x 1.000 (round up) Prior to rounding the hardware computes three extra digits: guard bit (G), round bit (R) and sticky bit (S) but since the tie case is impossible we only need to know G. Therefore in any format where the exact result has $r$ bits for the fractional part then for rounding to the next integer to occur requires the fractional part to have at least $r+1$ leading ones. I’m claiming that this is impossible with legal inputs. Since we only need to consider what happens with the fractional part let’s breakdown the exact result of $x/y$ into its integer $n$ and fractional parts: \[\frac{x}{y} = n + \frac{a}{b}\] (where $b=y$ just to be less of an eyesore) so obviously: \[\frac{a}{b} \in \left[0,1\right)\] Given a $p$ precision binary floating point format the division produces a $d$-bit integer leaving $r$-bits for the remainder: \[\begin{align*} d & = \left \lfloor \log_2 \left(n \right)+1 \right \rfloor \\ r & = p-d \\ \end{align*}\] For a $r$-bit fractional part we can define the lower and upper bound on it’s range of value for $b$: \[\begin{align*} \func{b_l}{r} & = 2^r \\ \func{b_u}{r} & = 2^{r+1}-1 \end{align*}\] At this point we’re pretty much done with $p$, $n$ and its derived $d$. We want to know the maximum fractional part possible for $r$-bits. The closest value to $1$ we can create with the lower is: \[\begin{align*} \frac{2^r-1}{2^r} = \sum_{i=1}^r \frac{1}{2^i} \end{align*}\] Rewritten as the sum tells us that this is the smallest number that produces $r$ leading ones in it’s binary expansion. Now repeating with the upper bound’s closest to $1$ value and compare to the smallest number with $r+1$ leading ones (the lower bound of $r+1$): \[\begin{align*} \frac{2^{r+1}-2}{2^{r+1}-1} &< \frac{2^{r+1}-1}{2^{r+1}-0} \end{align*}\] where the minus zero is to align the expressions and highlight the pattern. Anyway this inequality means that the upper bound is limited to having $r$ leading ones. We can actually be specific about the binary expansion of bounds. The lower is $r$ ones and terminating. The upper’s exact value is purely cyclic with a period of $r+1$ with $r$ ones followed by one zero. Here’s a table for the first four: $r$ lower binary expansion upper binary expansion 1 $\frac{1}{2}$ .1 $\frac{2}{3} $ .(10) 2 $\frac{3}{4}$ .11 $\frac{6}{7} $ .(110) 3 $\frac{7}{8}$ .111 $\frac{14}{15}$ .(1110) 4 $\frac{15}{16}$ .1111 $\frac{30}{31}$ .(11110) But anyway the important part: For legal input: $\frac{x}{y}$ cannot produce a fractional part large enough to cause rounding to the next integer so the integer part of the floating point result is identical to integer result. Let’s briefly tie the parts together by considering some divisors. Dividing by: $1$ is correct $2$ only modifies the exponent and no rounding of any kind occurs. $3$ which is $r=1$, the largest x % 3 value is $2$ so max fraction is $\frac{2}{3}$. Can’t round to next integer. $\set{4,5,6,7}$ have $r=2$. The exact divisor doesn’t matter because they all start with .110x. And so on all possible divisors. Code that test worst case $a$,$b$ pairs: godbolt for both binary32 and binary64. 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