How sqrt() of GCC works after compiled? Which method of root is used? Newton-Raphson?

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Just curiosity about the standard sqrt() from math.h on GCC works. I coded my own sqrt() using Newton-Raphson to do it!

 


yeah, I know fsqrt. But how the CPU does it? I can't debug hardware

Typical div/sqrt hardware in modern CPUs uses a power of 2 radix to calculate multiple result bits at once. e.g. http://www.imm.dtu.dk/~alna/pubs/ARITH20.pdf presents details of a design for a Radix-16 div/sqrt ALU, and compares it against the design in Penryn. (They claim lower latency and less power.) I looked at the pictures; looks like the general idea is to do something and feed a result back through a multiplier and adder iteratively, basically like long division. And I think similar to how you'd do bit-at-a-time division in software.

Intel Broadwell introduced a Radix-1024 div/sqrt unit. This discussion on RWT asks about changes between Penryn (Radix-16) and Broadwell. e.g. widening the SIMD vector dividers so 256-bit division was less slow vs. 128-bit, as well as increasing radix.

Maybe also see


But however the hardware works, IEEE requires sqrt (and mul/div/add/sub) to give a correctly rounded result, i.e. error <= 0.5 ulp, so you don't need to know how it works, just the performance. These operations are special, other functions like log and sin do not have this requirement, and real library implementations usually aren't that accurate. (And x87 fsin is definitely not that accurate for inputs near Pi/2 where catastrophic cancellation in range-reduction leads to potentially huge relative errors.)

See https://agner.org/optimize/ for x86 instruction tables including throughput and latency for scalar and SIMD sqrtsd / sqrtss and their wider versions. I collected up the results in Floating point division vs floating point multiplication

For non-x86 hardware sqrt, you'd have to look at data published by other vendors, or results from people who have tested it.

Unlike most instructions, sqrt performance is typically data-dependent. (Usually more significant bits or larger magnitude of the result takes longer).

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