# Why is division so much more complex than other arithmetic operations?

I recently encountered a case where I needed an integer division operation on a chip that lacked one (ARM Cortex-A8). While trying to research why that must be, I found out that in general division takes many more cycles than addition, subtraction or multiplication on pretty much any integer (or fixed-point) architecture. Why is this the case? Is it not representable with a two-layer AND-OR logic like everything else?

Division is an iterative algorithm where the result from the quotient must be shifted to the remainder using a Euclidean measure, see 2; whereas, multiplication can be reduced to a (fixed) series of bit manipulation tricks.

• It used to be that both multiplication and division were slow operations. Nowadays multiplication is a bit faster (but slightly slower than addition/subtraction), but division still is slower than the others. I believe Newton-Raphson is still used internally by most for reciprocating a number. Dec 3, 2011 at 0:36
• (Off-topic: "Inverse operations are usually hard. Just look at integration versus differentiation." - depends on whether what you're doing is symbolic or numeric. Differentiation is symbolically easy, but numerically hard; integration is symbolically hard, but numerically easy.) Dec 3, 2011 at 0:37
• Okay, I'll cop out by saying that cubature is a different can of worms; but at least in the one-dimensional case, quadrature is easier than differentiation. Dec 3, 2011 at 2:32
• In any case, inverses always come in pairs. Why would you call one the "operation" and the other the "inverse"? Dec 3, 2011 at 4:33
• Neither iteration nor inverse makes it harder. Hardness of division comes from the fact that you have to shift the result from quotient to remainder using a Euclidean measure. See the division algorithm theorem.
– user182
Dec 3, 2011 at 21:42

While all current CPU's appear to use an iterative approach as aterrel suggests, there has been some work done on non-iterative approaches. Variable Precision Floating Point Division and Square Root talks about a non-iterative implementation of floating point division and square root in an FPGA, using lookup tables and taylor series expansion.

I suspect that the same techniques may make it possible to get these operations down to a single cycle (throughput, if not latency), but you are likely to need huge lookup tables, and thus infeasibly large areas of silicon real-estate to do it.

### Why would it not be feasible?

In designing CPU's there are many trade-offs to make. Functionality, complexity (number of transistors), speed and power consumption are all interrelated and the decisions made during design can make a huge impact on performance.

A modern processor probably could have a main floating point unit which dedicates enough transistors on the silicon to perform a floating point division in a single cycle, but it would be unlikely to be an efficient use of those transistors.

The floating point multiply made this transition from iterative to non-iterative a decade ago. These days, single cycle multiply and even multiply-accumulate are commonplace, even in mobile processors.

Before it became an efficient use of transistor budget, multiply, like division, was often performed by an iterative method. Back then, dedicated DSP processors might dedicate most of their silicon to a single fast multiply accumulate (MAC) unit. A Core2duo CPU has a floating point multiply latency of 3 (the value comes out of the pipeline 3 cycle after it went in), but can have 3 multiplies in flight at once, resulting in a single-cycle throughput, meanwhile it's SSE2 unit can pump out multiple FP multiplies in a single cycle.

Instead of dedicating huge areas of silicon to a single-cycle divide unit, modern CPU's have multiple units, each of which can perform operations in parallel, but are optimised for their own specific situations. In fact, once you take into account SIMD instructions such as SSE or the CPU integrated graphics of the Sandy Bridge or later CPU's, there may be many such floating-point divide units on your CPU.

If generic floating point division were more important to modern CPU's then it might make sense to dedicate enough silicon area to make it single cycle, however most chip makers have obviously decided that they can make better use of that silicon by using those gates for other things. Thus one operation is slower, but overall (for typical usage scenarios) the CPU is faster and/or consumes less power.

• To my knowledge, no chips have single-cycle divide latencies for floating point. For example, Agner Fog's instruction tables for Intel, AMD, and VIA CPUs lists DIVPS (SSE packed floating-point divide) as 10-14 cycles. I can't find any hardware with single-cycle divide instructions, but I'd be willing to be proved wrong. It's not common as far as I can tell. Dec 6, 2011 at 15:06
• @Bill - Thanks, you're right. I'm sure I've seen single-cycle division operations in DSP chips before, so assumed it would have made it's way to the desktop, just as single-cycle multiply did, but I can't find any references now. I've updated my answer and added some relevant information on non iterative methods which might allow it in the future though. It's amazing to think that division is no more efficient per cycle now than back when I was using transputers. Dec 6, 2011 at 19:48
• I think DSPs do that by limiting the range in which they are accurate. This is the same strategy used for lookup+interpolation for square root. Dec 7, 2011 at 11:27
• I am not sure what the latency of such a division would be, though. At 4 GHz, making a round-trip to the look-up table within N cycles severely limits the potential size of said table (for example, the L1 caches have been stagnating at 32K each). Going 3D would help increasing this (but is challenging wrt. cooling). Do you have any idea what latency could be reached for modern 4GHz/5GHz CPUs? Jan 11, 2017 at 13:59
• For divps / divpd vs. mulps / mulpd latency and throughput numbers, see Floating point division vs floating point multiplication. I took data from Agner Fog's instruction tables and formatted it into a summary across uarches of div and mul throughput and latency, for single vs. double and for different SIMD vector widths. (Intel chips typically have a SIMD divider that's only half the width of the other vector ALUs.) Nov 27, 2018 at 23:32

I'll try to give a simplistic, vaguely correct answer.

The slow Division algo is essentially iterative subtract, (with some extra steps.) It's intrinsically more time-consuming than multiply.

(turns out it's not a great idea to think I could sum this up in a couple of lines.)

• Division algorithms implemented in silicon do not multiply. Aug 12, 2023 at 15:31
• They are also far more complex than multiply, which is the point I'm trying to make. Obviously trying to squeeze useful information on this subject into 1 line is a bit of a foot-gun on my part. Aug 13, 2023 at 11:29
• @BjörnLindqvist - Goldschmidt would disagree. Aug 13, 2023 at 11:41