[0]: https://mathr.co.uk/mandelbrot/book-draft-2017-11-10.pdf
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xor eax, eax ; z_real = 0
xor ebx, ebx ; z_imag = 0
mov ecx, 0 ; iteration counter loop $All that said, I’ve recently started a RISC-V port, and I’ve found that porting bits of low-level init code from x86 (NASM) to RISC-V (GAS) is actually quite good - I guess because it’s largely a simple translation job and it already has the logic to work from.
Edit: that -> than
Have you tried using a coding agent that can run the compiler itself and fix any errors in a loop?
The first version I got here didn't compile. Firing up Claude Code and letting it debug in a loop fixed that.
I’m still working on a good way to integrate more feedback for this kind of workflow, e.g. for the attempt it made at AP bootstrap - debugging that is just hard, and giving an agent enough control over the running code and the ability to extract the information it would need to debug the resulting triple fault is an interesting challenge (even if probably not all that generally useful).
I have a bunch of pretty ad-hoc test harnesses and the like that I use for general hosted testing, but that can only get you so far in this kind of low-level code.
And in your experience, how often is that ?
If so, why?
However, in many old but not very old CPUs, e.g. in many from the last decade of the 20th century and from the first decade of this century, floating-point multiplication and division were much faster than integer multiplication and division.
So for those CPUs, which include models like Pentium 4, or even the first Intel Core models (those older than Nehalem, which got a dedicated integer multiplier), there were many cases when converting an integer computation into a floating-point computation could increase the speed many times.
Integers are much faster to process than floating point, so if you have a fixed acceptable lower level of precision it is usually a good optimisation to scale your values so they exist in integer space, so 12345678 instead of 1.2345678, perform the mass calculations on those integers, and scale back down for subsequent display.
As well as speed, this also (assuming your scaling to ints gives enough precision for your use) removes the rounding issues inherent with floating point (see https://floating-point-gui.de/), which can balloon over many iterations. This is less important to fractal calculations then the speed issue, though can be visible at very high magnifications, and a bigger issue for things like monetary work. Monetary values are often stored as pennies (or other local minimum subdivision) rather than the nominal currency, so £123.45 is stored and processed as 12345, or for thing like calcs where factions of pennies may be significant, thousandths of pennies (£122.45 -> 12345000).
This was common and accepted knowledge circa 1990-2005.
Is that still the case?
In 2025, I'm not so sure, and if speed is indeed what you're after, what you typically want is to pump calculations into both the floating point units and the integer units in parallel using SIMD instructions, something that should be easy on an embarrassingly parallel problem like Mandelbrot.
And regarding precision ... I was under the impression that proving with 100% certainty that a point (x,y) actually belongs to the Mandelbrot set is basically impossible unless you prove some sort of theorem about that specific point.
The numerical method is always something along the lines of "as long as the iteration hasn't diverged after N iterations we consider the point to be inside".
And both N and the definition of "diverged" are usually completely arbitrary ... so precision, meh, unless you're going to try draw something with a zoom of 1e+20 on the frontier.
About performance: floating point is definitely much faster than integer arithmetic these days, for most comparable operations.
One more point about precision: integer arithmetic is exact, and so if you can work with a reasonably small bound (e.g. [-2,2]^2 for Mandelbrot) then integer arithmetic actually becomes more accurate than FP for the same number of bits.
Is that true of code running on CPUs? My GPU can run rings around the CPU at floating point, and probably itself at integer arithmetic, but I find it hard to believe any CPU is faster at a floating point version of an algorithm than an integer version of the same (assuming both are well, or equally badly, optimised).
Already in 2018, with Cannon Lake, Intel has introduced instructions (IFMA) which can reuse for integer computations the same vector multipliers and adders that are provided for floating-point computations. However, later Intel has removed AVX-512 from its consumer CPUs. The latest models of Intel CPUs have introduced a slower AVX-encoded variant of IFMA, but this is too little and too late, in comparison with what AMD offers.
Using IFMA and the other AVX-512 integer vector instructions, where supported, it is possible to obtain very similar throughputs, regardless if the computation is done with integer or floating-point numbers.
Hopefully, with the growing installed base of Zen 4 and Zen 5 based computers there will be the incentive to update many software packages to take advantage of their capabilities.
First try worked but didn't use correct terminal size.
It is fine to treat it as a better autocompletion tool.
If it’s an ARM Mac, under Rosetta. Otherwise directly.
piker•18h ago
askl•15h ago