What's the relative speed of floating point add vs. floating point multiply

Question

A decade or two ago, it was worthwhile to write numerical code to avoid using multiplies and divides and use addition and subtraction instead. A good example is using forwa

Answer 1

The best way to answer this question is to actually write a benchmark/profile of the processing you need to do. Empirical should be used over theoretical when ever possible. Especially when it easy to attain.

If you already know different implementations of the Math you need to do, you could write a a few different code transfermations of the math and see where your performance peaks. This will allow the processor/compiler to generate different execution streams to fill the processor pipelines and give you a concrete answer to your answer.

If you are interest in specifically the performance of DIV/MUL/ADD/SUB type instructions you could even toss in some inline assembly to control specifically which variants of these instruction are executed. However you need to make sure you're keeping multilple execution units busy to get a good idea of the performance the system is capable of.

Also doing something like this would allow you to compare performance on multiple variations of the processor by simply running the same program on them, and could also allow you to factor in the motherboard differences.

Edit:

Basic architecture of a +- is identical. So they logically take the same time to compute. * on the other hand, require multiple layers, typically constructed out of "full adders" to complete a single operation. This garentees that while a * can be issued to the pipeline every cycle it will have a higher latency than an add/subtract circuit. A fp / operation is typically implemented using an approximation method which iteratively converges towards the correct answer over time. These types of approximations are typically implemented via multiplication. So for floating point you can generally assume division will take longer because it's impractical to "unroll" the multiplications (which is already a large circuit in and of it's self) into pipeline of a multitude of multiplier circuits. Still the performance of a given system is best measured via testing.