|
|
|
|
|
|
by taeric
17 hours ago
|
|
|
Kudos on providing the numbers. I wasn't confident in the numbers that I remembered and with how pipelined everything is, I didn't know how much to lean on them. Not exactly my standard workflow to care about this level of speed. My gut would still be that it is typically a wash for most everyone as far as speeds go? If default libraries supported it more directly, I would think it would largely be a win for a lot of reasoning. In particular, silly stuff like 1e32 + 1e1 would not be nearly as surprising to most people. And the entire class of bugs around stuff like doing something until it reaches 0.9 would almost certainly go away if we guaranteed precision to a set number decimal places. Alas, default libraries do not support this, though. So the above is admittedly wishful thinking on my end. And I could as easily describe a world where people insist on arbitrary numbers of fixed point values and how that would be its own set of landmines. |
|
|
Commonly enough that I think it's some fundamental reason (given available/current hardware as opposed to hypothetical).
Two reasons I can think of, though granted, they only apply in certain niches:
1) Floating-point SIMD extensions are far more common than integer ones. This means you can compute N (often N=4 or 8) float operations in one instruction, vs 1 integer operation.
2) For any GPGPU processing: GPUs far prefer floats, to the point where you didn't even use to have integers available (somewhat ironically, the platforms that prefer float much more strongly to int are mobile/embedded ones nowadays --- which is the exact opposite of the CPU situation). To this day, you have a `mul24` intrinsic for integer multiplication in some languages ... which converts two integers to floats, multiplies them as floats, and then converts back to integers. Yes, that was faster than direct multiplication. I'm sure many GPUs do it directly nowadays though.
It's also worth considering that a typical fixed-point multiply (as opposed to integer) is an integer multiply followed by a shift; often to a 2×-bit intermediate, if you want to preserve precision. That's a cost.