Round a direction vector to an 8-way compass(lemire.me)
lemire.me
Round a direction vector to an 8-way compass
https://lemire.me/blog/2022/07/24/round-a-direction-vector-to-the-nearest-8-way-compass/
9 comments
This is a good point; some controllers are neither circular nor square. Check out the mapping ranges for a couple of Nintendo’s old controllers: https://electromodder.co.uk/
That said, Lemire’s approach rewritten as dot products might work nicely and even look a bit more clean, even if it took 3ns instead of 1.5.
That said, Lemire’s approach rewritten as dot products might work nicely and even look a bit more clean, even if it took 3ns instead of 1.5.
> This is a good point; some controllers are neither circular nor square. Check out the mapping ranges for a couple of Nintendo’s old controllers: https://electromodder.co.uk/
Very interesting, thanks! I've noticed myself that sometimes if I'm holding the controller a bit skewed my natural impulse will be to push in directions that don't exactly map to what the controller wants. EG I think I'm pushing forward, but I'm moving forward and slightly to the left. And its not just stick drift, its my own user error.
Very interesting, thanks! I've noticed myself that sometimes if I'm holding the controller a bit skewed my natural impulse will be to push in directions that don't exactly map to what the controller wants. EG I think I'm pushing forward, but I'm moving forward and slightly to the left. And its not just stick drift, its my own user error.
I do the same. Some difficult and brutal platformers will really help you notice how much user error you bring to the table. :P Good point too, because for fast twitchy games where the directions really matter, it’s quite nice to be able to calibrate and/or bias the direction rounding to compensate for my own bias. This could be one good reason to go with the trig code instead - offsetting the angle becomes trivial. It could be done under the covers before the rounding code sees it, but it’d still be doing trig somewhere.
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Taking the dot product of the [x,y] with the 8 directions and picking the one with the largest magnitude might be faster in some cases.
You only need 4 directions, and can look at the sign of the dot-product.
Good idea. Only four inproducts and comparisons are needed: the other four have the same value, but negated. Might be SIMD-able.
I was thinking about a more complicated approach (edit2: that ends up simpler). A 4-way version of this can be done with two comparisons (if x > y to check on what side of one diagonal the point lies and if x > -y to check the same for the other diagonal, plus some bit twiddling to get the required two bits out).
One can do that, rotate by 45 degrees, do that again, and do some further bit manipulation to combine the two bits of the first 4-way version with those of the second into the three bits this should produce.
That would be four comparisons, a 2×2 matrix multiplication (edit: that’s a matrix-vector multiplication) with a known-ahead rotation matrix (naively, that would be 8 (edit: 4) multiplications and 4 (edit: 2) additions, but I think we can lose those multiplications because all elements of a 45 degree rotation matrix have absolute value 1/√2 and the problem is scale-invariant) and some bit twiddling.
Now, make that branchless…
Edit2: and of course, you can do both splits into quadrants by just looking at the signs of x and y. That rotates the four quadrants by 45 degrees, but that doesn’t matter.
That makes the 4-way version simply (x>0) + 2 × (y>0): two comparisons and a bit shift.
One can do that, rotate by 45 degrees, do that again, and do some further bit manipulation to combine the two bits of the first 4-way version with those of the second into the three bits this should produce.
That would be four comparisons, a 2×2 matrix multiplication (edit: that’s a matrix-vector multiplication) with a known-ahead rotation matrix (naively, that would be 8 (edit: 4) multiplications and 4 (edit: 2) additions, but I think we can lose those multiplications because all elements of a 45 degree rotation matrix have absolute value 1/√2 and the problem is scale-invariant) and some bit twiddling.
Now, make that branchless…
Edit2: and of course, you can do both splits into quadrants by just looking at the signs of x and y. That rotates the four quadrants by 45 degrees, but that doesn’t matter.
That makes the 4-way version simply (x>0) + 2 × (y>0): two comparisons and a bit shift.
twoBits = (x > 0) + 2 × (y > 0)
x’ = x + y
y’ = x - y
twoMoreBits = (x’ > 0) + 2 × (y’ > 0)
(Some bit twiddling with ‘twoBits’ and ‘twoMoreBits’
Feel free to compute those bit pairs differently if
it makes the bit twiddling easier)I wonder if some amount of hysteresis would be helpful to avoid switching back and forth on the edge.
Button debouncing is useful for all sorts of stuff like this, but it requires much trickier code to get right, including tracking some form of history.
What’s the motivation for optimising this? If it’s for user input, you only need to call this once per frame (or at most a few times).
It doesn’t matter for user input, the 40ns won’t make a difference even at 60fps or higher with 4 players. (Have written the controller processing loop for a console game engine.) I suspect it would be less than 40ns if float was used instead of double; there’s no good reason to use double here at all. But the proposed solution is better, and super easy (arguably easier than atan2+sincos), so maybe no reason not to use it a save a few microseconds if it happens to add up to that much.
I could imagine other uses of this code, like a UI wind map, or some kind of randomized maze, or something else. There might be reasons to round a vector of directions all at once.
I could imagine other uses of this code, like a UI wind map, or some kind of randomized maze, or something else. There might be reasons to round a vector of directions all at once.
Yeah, it’s a cute optimisation, and might make sense in a game engine; I just question whether this would ever be worthwhile writing as custom code in a single game.
I’m not sure I agree it’s more readable. The optimised version is reasonably regular, but it’s still longer and has more constants, hence more opportunities for sign errors and the like.
It feels sort of like jumping through hoops to avoid a matrix inversion, say. Sure, matrix inversion is “slow”, but unless you’re doing it millions of times a frame it doesn’t remotely matter. If matrix inversion or atan2 is the most direct way to express a transformation, just do it that way.
I’m not sure I agree it’s more readable. The optimised version is reasonably regular, but it’s still longer and has more constants, hence more opportunities for sign errors and the like.
It feels sort of like jumping through hoops to avoid a matrix inversion, say. Sure, matrix inversion is “slow”, but unless you’re doing it millions of times a frame it doesn’t remotely matter. If matrix inversion or atan2 is the most direct way to express a transformation, just do it that way.
I think the atan2+sincos version is more readable, so I guess we actually do agree there. But I wouldn’t (and didn’t) use that in a game engine. For rounding and readability in a real engine, I’d name 4 of the 8 directions and look for the largest absolute value dot product. That would at least corral the constants. And I’d use (did use) f16 or integer arithmetic.
One reason I have to support this kind of optimization and not reach for the trig method by default is the last time I wrote a controller system, it wasn’t limited to something as small as deciding on a single dpad-like direction, it was a much larger system that designers could author controller recognizers for, up to and including drawing shapes like a circle with the stick, or entering a time based sequence of inputs (think Contra’s famous up-down-up-down-etc-etc cheat code). If you have sequences and multiple players and high frame rate and many possible patterns to identify, that’s when efficiency of these small operations really starts to add up. I actually spent the most development time ensuring that the amount of time spent not recognizing controls was minimized, making sure that a no-op pass through the system was measured in nanoseconds and not microseconds.
One reason I have to support this kind of optimization and not reach for the trig method by default is the last time I wrote a controller system, it wasn’t limited to something as small as deciding on a single dpad-like direction, it was a much larger system that designers could author controller recognizers for, up to and including drawing shapes like a circle with the stick, or entering a time based sequence of inputs (think Contra’s famous up-down-up-down-etc-etc cheat code). If you have sequences and multiple players and high frame rate and many possible patterns to identify, that’s when efficiency of these small operations really starts to add up. I actually spent the most development time ensuring that the amount of time spent not recognizing controls was minimized, making sure that a no-op pass through the system was measured in nanoseconds and not microseconds.
More faster is more better; you only have 16 milliseconds per tick assuming you want to read input 60 times a second, some games poll even faster / have a higher frame rate.
It's still a difference of 38.5 nano seconds. You have a budget 16,000,000 nanoseconds per frame. It's like Jeff Bezos writing about how to save money at Walmart; just pick the readable and stupid approach and move on. Game loops are made fast or slow by the efficiency of their tight loops: the render loop, physics update loop, UI loop, etc. Something that happens once per frame is barely ever worth considering.
Agreed, 35ns x4 times per frame is less important than readability.
Ok, but 40ns of 16ms is 0.00025% of your frame time.
I can think of a few reasons:
1. writing a game that movement happens along 8 axes, and needs to trace lots of rays
2. several other reasons similar to (1)
3. had a clever idea to optimize a thing and went ham, for fun
1. writing a game that movement happens along 8 axes, and needs to trace lots of rays
2. several other reasons similar to (1)
3. had a clever idea to optimize a thing and went ham, for fun
faster parsing of user input means more time for doing graphics and game state!
Though in this case 1.5 nanoseconds vs 40 nanoseconds is small and won't matter for most games.
Though in this case 1.5 nanoseconds vs 40 nanoseconds is small and won't matter for most games.
Is it possible to just ask... xinput or whatever joystick driver is used nowadays... to produce the cardinal or 8-compass directions? I guess it seems plausible, at least, that maybe for some controllers we could skip the whole conversion to/from a pair of floats thing, depending on what signals are sent down the USB pipe.
It may be useful to read about the CORDIC algorithms. It just feels like there is an even simpler solution lurking here:
https://en.wikipedia.org/wiki/CORDIC
https://en.wikipedia.org/wiki/CORDIC
Is the problem with the sin/cos approach that it’s using double precision? That’s surely completely unnecessary precision. These days I don’t know what happens in x86 land with sin & cos, but on a gaming GPU the fp32 cosf() is typically 16 or 32 times faster than fp64 cos(). Is that true on CPUs too? (I’m sure the simple proposed solution is always faster even in fp32, I’m just curious if the gap is much narrower, smaller than 25x.)
The precision doesn't matter much on the CPU (roughly half the throughout if you're doing a lot of them). You have a data dependency on the arctangent, and transcendentals are slow anyway, so you're not going to have a good time. You can even compute the sin and cos together and still expect a bare minimum latency of 50 clock cycles for the arctangent followed by 50 for the sincos, usually implemented with a variable speed algorithm that might be slower (i.e., lower bound of 30ns of latency on a moderately fast single core, just for the trig).
Throughput can be a bit higher with vectorization and pipelining, but more or less any such optimization you can make to the transcendentals you can also make on the proposed solution.
Throughput can be a bit higher with vectorization and pipelining, but more or less any such optimization you can make to the transcendentals you can also make on the proposed solution.
Thanks, good to know! So that does suggest to me that if there are other threads in progress, the actual impact to the runtime in practice probably would be lower with fp32 - the half throughput rate does tell us how long we wait for any instructions dependent on the operation, right? I’m saying I suspect the total latency can be filled with other work, so this isn’t really 40ns that is fully devoted to atan2+sincos and nothing else. Is that accurate? (On the GPUs I use, trig functions use a different math pipe than FMA, and so they can be ‘free’ if you don’t use too many of them and there is enough logic or FMA to hide the trig latency.)
Aren’t the benefits of using fp32 bigger than the trig instruction throughput too? Doubles eat more register space, and double the bandwidth to memory if anything ends up in memory. I just don’t see any reason whatsoever to use doubles for this operation. If we’re going to worry about nanoseconds, then these extra bytes matter too, no?
Aren’t the benefits of using fp32 bigger than the trig instruction throughput too? Doubles eat more register space, and double the bandwidth to memory if anything ends up in memory. I just don’t see any reason whatsoever to use doubles for this operation. If we’re going to worry about nanoseconds, then these extra bytes matter too, no?
The throughput comment was just that we have fixed-width vector instructions which support being filled with and doing trig on either 32-bit or 64-bit floats. They both occupy the same floating point execution units (mostly, see the AMD bulldozer/steamdriver/... fiasco from a few years ago), so you're either filling them with N doubles or 2N floats. You'd be hard-pressed to squeeze other useful work into that space. The actual difference between float/double in the amount of work done is otherwise miniscule and mostly related to teasing out the extra precision. With fast-converging algorithms it doesn't wind up mattering much in terms of total latency.
"Latency" is what I was referring to with respect to how long you have to wait to execute a data-dependant instruction. "Throughput" refers to how many you can have in flight and how fast those execute.
Latency _can_ be filled with other work, but as I mentioned, it can be filled better with other work using the faster non-trig solution. Rule of thumb, you won't squeeze in more than 4 in-flight instructions per core, so you're probably not going to eek out more 10ns average throughput for the trig solution, worse than the proposed optimization even assuming it couldn't be pipelined at all. You're right that float/int execution units are usually separate, but they do have some shared resources. If you're doing enough logic relative to these computations then yes these could sometimes be basically free.
The register/cache/RAM/disk space and bandwidth very well might wind up dominating every other consideration. As always, it depends what else you're doing and what exactly you're doing with these computations. To give a useful counter-example, consider matrix multiplication. It can be structured such that even if you have to read/write the matrices from disk because of their size it's still a CPU-bound operation because you can efficiently use each data access many times. In that case you really do just have typically a 2x slowdown for doubles because you have half the elements per vector.
Might f32 be sufficient for this problem? Almost certainly IMO. It might not matter though (or it might -- as always, measure).
"Latency" is what I was referring to with respect to how long you have to wait to execute a data-dependant instruction. "Throughput" refers to how many you can have in flight and how fast those execute.
Latency _can_ be filled with other work, but as I mentioned, it can be filled better with other work using the faster non-trig solution. Rule of thumb, you won't squeeze in more than 4 in-flight instructions per core, so you're probably not going to eek out more 10ns average throughput for the trig solution, worse than the proposed optimization even assuming it couldn't be pipelined at all. You're right that float/int execution units are usually separate, but they do have some shared resources. If you're doing enough logic relative to these computations then yes these could sometimes be basically free.
The register/cache/RAM/disk space and bandwidth very well might wind up dominating every other consideration. As always, it depends what else you're doing and what exactly you're doing with these computations. To give a useful counter-example, consider matrix multiplication. It can be structured such that even if you have to read/write the matrices from disk because of their size it's still a CPU-bound operation because you can efficiently use each data access many times. In that case you really do just have typically a 2x slowdown for doubles because you have half the elements per vector.
Might f32 be sufficient for this problem? Almost certainly IMO. It might not matter though (or it might -- as always, measure).
FWIW, for a game controller, I’m absolutely certain that f32 is already overkill and that a half float or even a byte per axis, u16/u8, is sufficient. The only reason to need f32 or f64 is if you’re rounding direction vectors for some other purpose than a game controller. But in graphics we frequently use f16 for direction vectors even when f32 is needed for positions. Normals and unit sphere directions usually require less precision than other operations. Using f64 for a game controller stick input isn’t justifiable, even if it isn’t measurable in code speed, I don’t think it would fly or pass code review in a real game engine. My next concern would be whether using doubles willy-nilly leads to the compiler spilling registers to memory more often. That could easily undo the 40ns optimization, and it would be difficult to measure or blame the direction rounding code, it would diffuse the cost to other parts of the code.
This but in a 2-sphere space is what I need right now for a quantum computing simulator I'm coding for fun/learning.
I'm doing a different thing tho, dot product vs. four cardinal points.
I'm doing a different thing tho, dot product vs. four cardinal points.
Two more optimizations:
- Use else if to avoid uncessary condition checking.
- Only check for xneg / yneg if outx / outy are non-zero.
- Use else if to avoid uncessary condition checking.
- Only check for xneg / yneg if outx / outy are non-zero.
As written, the compiler can implement the "if"s with conditional move instructions and that would be faster than with conditional branches, as those would be hard to predict in this case.
Using "else if" is a valid optimization in general, but not necessarily when your target is a branchless program. Using "else if" may help in some cases, but you must have in mind exactly which CMOV instructions will be generated by the compiler, to be sure that there is any improvement, i.e. any reduction in the total number of CMOV instructions, and that you are not increasing the length of some chains of dependent instructions (leading to a greater latency).
Using "else if" is a valid optimization in general, but not necessarily when your target is a branchless program. Using "else if" may help in some cases, but you must have in mind exactly which CMOV instructions will be generated by the compiler, to be sure that there is any improvement, i.e. any reduction in the total number of CMOV instructions, and that you are not increasing the length of some chains of dependent instructions (leading to a greater latency).
Almost certainly makes it worse to add additional conditional dependencies, meaning branches upon branches.
Some examples:
1. Branches with dependencies.
3. Branchless
Last time I ran comparisons, Version 2 was 0.07x the runtime of Version 3, so Version 3 was not that much faster than Version 1, but still a good improvement.
As a final note, I should say I’m not a fan of relying on compiler auto-vectorization. If you depend on the performance of vectorized code (and vectorizing code is one of the best optimizations out there), then I’m a fan of explicitly vectorizing. SSE2 is in nearly 100% of relevant x86_64 systems (first coming out in 2003). If you’re targeting consumers, AVX is almost certainly worth it. If you’re writing server code, you should be taking full advantage of AVX2 and maybe 512. For arm, NEON is the current vector extension of relevance, and you can polyfill with DLTcollab/sse2neon or something like that, until you go native. Writing vectorized code using SIMD intrinsics (#include <immintrin.h>) is preferable. Especially since compilers just cannot know how to auto-vectorize a lot of floating point code, because of strict assumptions about requirements around non-commutativity, so they almost never generate packed FMADDs.
As for the article code, I wouldn’t make any assumptions about whether the compiler is generating branches or cmov-s. I’d guess cmov-s would be faster, but I haven’t really thought about it. All I’ll say is that turning a lot of `if`s into `else if`s would make it impossible to generate cmov-s, and chaining branches just to avoid assigning to a local that’s almost certainly in a register is only going to make branching slower.
Some examples:
1. Branches with dependencies.
static void minMax(u8* pxmin, u8* pxmax, const u8* xs, size_t n)
{
const u8 init = n > 0u ? xs[0] : (u8)0u;
u8 xmin = init
u8 xmax = init;
for (size_t i = 1u; i < n; ++i)
{
const u8 x = xs[i];
if (x < xmin) { xmin = x; }
else if (x > xmax) { xmax = x; }
}
*pxmin = xmin;
*pxmax = xmax;
}
static void minMax(u8* pxmin, u8* pxmax, const u8* xs, size_t n)
{
const u8 init = n > 0u ? xs[0] : (u8)0u;
u8 xmin = init
u8 xmax = init;
for (size_t i = 1u; i < n; ++i)
{
const u8 x = xs[i];
if (x < xmin) { xmin = x; }
if (x > xmax) { xmax = x; } /*not doing else if*/
}
*pxmin = xmin;
*pxmax = xmax;
}
3. Branchless
inline u8 minBranchless(u8 x, u8 y)
{
return x * (u8)(x <= y ? 1u : 0u)
+ y * (u8)(x > y ? 1u : 0u);
}
inline u8 maxBranchless(u8 x, u8 y)
{
return x * (u8)(x >= y ? 1u : 0u)
+ y * (u8)(x < y ? 1u : 0u);
}
static void minMax(u8* pxmin, u8* pxmax, const u8* xs, size_t n)
{
const u8 init = n > 0u ? xs[0] : (u8)0u;
u8 xmin = init
u8 xmax = init;
for (size_t i = 1u; i < n; ++i)
{
const u8 x = xs[i];
xmin = minBranchless(xmin, x);
xmax = maxBranchless(xmax, x);
}
*pxmin = xmin;
*pxmax = xmax;
}
Last time I ran comparisons, Version 2 was 0.07x the runtime of Version 3, so Version 3 was not that much faster than Version 1, but still a good improvement.
As a final note, I should say I’m not a fan of relying on compiler auto-vectorization. If you depend on the performance of vectorized code (and vectorizing code is one of the best optimizations out there), then I’m a fan of explicitly vectorizing. SSE2 is in nearly 100% of relevant x86_64 systems (first coming out in 2003). If you’re targeting consumers, AVX is almost certainly worth it. If you’re writing server code, you should be taking full advantage of AVX2 and maybe 512. For arm, NEON is the current vector extension of relevance, and you can polyfill with DLTcollab/sse2neon or something like that, until you go native. Writing vectorized code using SIMD intrinsics (#include <immintrin.h>) is preferable. Especially since compilers just cannot know how to auto-vectorize a lot of floating point code, because of strict assumptions about requirements around non-commutativity, so they almost never generate packed FMADDs.
As for the article code, I wouldn’t make any assumptions about whether the compiler is generating branches or cmov-s. I’d guess cmov-s would be faster, but I haven’t really thought about it. All I’ll say is that turning a lot of `if`s into `else if`s would make it impossible to generate cmov-s, and chaining branches just to avoid assigning to a local that’s almost certainly in a register is only going to make branching slower.
> SSE2 is in nearly 100% of relevant x86_64 systems (first coming out in 2003).
Not nearly, exactly 100%. The 64-bit ABI specifically requires SSE2 support.
Not nearly, exactly 100%. The 64-bit ABI specifically requires SSE2 support.
Last time I talked with someone about this, they convinced me there was some rare, possibly even research-only x86_64 chip that didn't support it, so my point is that if my hazy memory of someone else's argument is correct, that it doesn't matter, treat it as if it's exactly 100% anyway.
It's 100% of what matters, so in practice I'm in full agreement with you.
It's 100% of what matters, so in practice I'm in full agreement with you.
Loved this, thanks.
Isn't the ternary operator a branch? Could you just write (u8)(x <= y)?
In x86_64, a branch is typically considered to be a comparison and then a jump to different code (otherwise fall through to following code). Test sets some control flags, and the specific jump type jumps based on the value of a specific control flag. You might have something like:
Branches, even well-predicted ones, can hurt performance, especially if the jumped to code is not in cache (needs to be fetched). More detail can be found in this nice top-to-bottom video from Casey Muratori, which goes over modern CPU architecture (it's nothing too fancy, I think every person doing optimization should know this stuff): https://www.youtube.com/watch?v=8VakkEFOiJc
You can imagine the compiler generating either assembly from if style code or from ternary style code. Writing the arithmetic ternary style just happens to nudge the compiler into using cmov-s more often than other ways. For simple cases, a smart enough compiler could generate cmov-s from the if-else case as well, but it's not smart enough for even toy examples. Never mind that cmov-s are not unconditionally better -- a well-trained branch predictor is faster. The question is if your data is conducive to that or not -- something the compiler may never infer. So we're left to encode such information very indirectly via the code patterns, or via all sorts of __builtin_expect style intrinsics.
This all goes to show that C is no longer portable assembler, and there are all sorts of crucial resources on the CPU (cmov, SIMD, etc.) that may go unused based on slight differences in the C code (e.g., even little things that might seem correct, like using unsigned for an accumulator, can ruin its ability to generate cmov-s).
Compilers are really good at turning code that multiplies by a constant into a weird mov and lea that's cheaper than a mul, but they're still very spotty on everything else, and "everything else" covers a huge amount of the stuff that has made CPUs faster in the last 15+ years. Understanding your specific platform target(s) is still crucial if you want to do better than just "get lucky with the compiler sometimes".
mov rax,rbx
test rcx,rcx
jnz L1
mov rax,rdx
L1: ret
mov rax,rbx
test rcx,rcx
cmovz rax,rdx
ret
Branches, even well-predicted ones, can hurt performance, especially if the jumped to code is not in cache (needs to be fetched). More detail can be found in this nice top-to-bottom video from Casey Muratori, which goes over modern CPU architecture (it's nothing too fancy, I think every person doing optimization should know this stuff): https://www.youtube.com/watch?v=8VakkEFOiJc
You can imagine the compiler generating either assembly from if style code or from ternary style code. Writing the arithmetic ternary style just happens to nudge the compiler into using cmov-s more often than other ways. For simple cases, a smart enough compiler could generate cmov-s from the if-else case as well, but it's not smart enough for even toy examples. Never mind that cmov-s are not unconditionally better -- a well-trained branch predictor is faster. The question is if your data is conducive to that or not -- something the compiler may never infer. So we're left to encode such information very indirectly via the code patterns, or via all sorts of __builtin_expect style intrinsics.
This all goes to show that C is no longer portable assembler, and there are all sorts of crucial resources on the CPU (cmov, SIMD, etc.) that may go unused based on slight differences in the C code (e.g., even little things that might seem correct, like using unsigned for an accumulator, can ruin its ability to generate cmov-s).
Compilers are really good at turning code that multiplies by a constant into a weird mov and lea that's cheaper than a mul, but they're still very spotty on everything else, and "everything else" covers a huge amount of the stuff that has made CPUs faster in the last 15+ years. Understanding your specific platform target(s) is still crucial if you want to do better than just "get lucky with the compiler sometimes".
Would else if be actually faster on a modern CPU architecture?
Only measurement with real data would tell for sure, but I would bet "yes" if I had to. With the "else", the "if" predicate is being predicted over a smaller amount of relevant cases; without the "else", the prediction is going to have to cope with some amount of needless false results and is likely to be less accurate.
This approach effectively divides the ‘unit square’ of possible input vectors up into a grid, based on cos(pi/8) and cos(3pi/8) gridlines, with basically 9 possible areas in each quadrant mapped to corresponding directional vectors.
It may not be a bad strategy in general to use this kind of grid model, but it needs testing with actual controllers to see what the ‘feel’ ends up being. While this mathematical model makes sense in theory, in practice there’s no reason to suspect that these cosine values are necessarily good thresholds to use to create an intuitive compass direction control that actually feels intuitive.
In general in games, math needs to be a guide, but not a straitjacket for how you approach things.