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- Hacker News
- The algorithm description was a bit confusing for me.
The SIMD part is just in the last step, where it uses SIMD to search the last 16 elements.
The Quad part is that it checks 3 points to create 4 paths, but also it's searching for the right block, not just the right key.
The details are a bit interesting. The author chooses to use the last element in each block for the quad search. I'm curious how the algorithm would change if you used the first element in each block instead, or even an arbitrary element.
by srcreigh - The classical canonical Comp Sci algorithms are effectively "designed" for CPUs with no parallelism (either across multiple cores, via Hyper-threading technology, or "just" SIMD style instructions), and also where all memory accesses take the same amount of time (so no concept of L1/L2/L3/etc caches of varying latencies). And all working on general/random data.
As soon as you move away from either (or both) of these assumptions then there are likely to be many tweaks you can make to get better performance.
What the classical algorithms do offer is a very good starting point for developing a more optimal/efficient solution once you know more about the specific shape of data or quirks/features of a specific CPU.
When you start to get at the pointy end of optimising things then you generally end up looking at how the data is stored and accessed in memory, and whether any changes you can make to improve this don't hurt things further down the line. In a job many many years ago I remember someone who spent way too long optimising a specific part of some code only to find that the overall application ran slower as the optimisations meant that a lot more information needed later on had been evicted from the cache.
(This is probably just another way of stating Rob Pike's 5th rule of programming which was itself a restatement of something by Fred Brooks in _The Mythical Man Month_. Ref: https://www.cs.unc.edu/~stotts/COMP590-059-f24/robsrules.htm...)
by alexfoo - If you are talking smaller arrays, linear search with a sentinel value at the end is already tough to beat. The thing that sucks about that claim, is that "smaller" is such a nebulous qualifier that it is really hard to internalize.by taeric
- I'd like to point out that all the graphs only go to 4,000 elements, which is basically non-data. Basically it'd be like measuring which car wins a 1cm race.
For small workloads binary search is slower than just checking every element.
To add to this, I think people can forget how small log(n) is... it can practically be seen as constant (as the log base 2 of ATOMS IN THE UNIVERSE is ~300).
by zug_zug - Since the cpu always accesses a full cache line (64 bytes) at a time, you might as well search the entire cache line (it’s practically free once the data is on-cpu). So I’d like to try a ‘binary’ search that tests all the values in the ‘middle cache line’ and then chooses to go left or right if none match. You can do the cache line search as a single 512bit simd instruction. A cache line is 64 bytes (or 32 16-bit integers); such a search might well be almost 32 times faster than simple binary search; at least it’ll do 32x less memory accesses, which will dominate in most realistic programs.by drkrab
- I also wrote recently [1] about Exponential Search [2] which is another algorithm if you need to repeatedly binary search in an array where the elements you're searching are themselves are sorted. It allowed for an 8x speedup in our workload!
[1] https://lalitm.com/post/exponential-search/ [2] https://en.wikipedia.org/wiki/Exponential_search
by lalitmaganti - Isn't "quaternary" just sort of unrolling the binary search loop by one level? I mean, to find the partition in which the item is located, you still do roughly the same rough number of comparisons. You're just taking them 4 at a time, not 2 at a time. Seems like loop unrolling would give you the same.by drob518
- Daniel Lemire's points about low-level hardware optimization notwithstanding, it's worth pointing out that binary search (or low-level implementation variants) is the best only if you know nothing about the data beyond the fact that it is sorted / monotonic.
If you have priors about the data distribution, then it's possible to design algorithms which use that extra information to perform MUCH better. eg: a human searching a physical paper dictionary can zoom into the right bunch of pages faster than pure idealized binary search; it's a separate matter it's hard for humans to continue binary search till the very end and we might default to scanning linearly for the last few iterations (cognitive convenience / affordances of human wetware / etc).
In mathematical language, searching a sorted list is basically inverting a monotonic function, by using a closed-loop control algorithm. Often, we could very well construct a suitable cost function and use gradient descent or its accelerated cousins.
More generally, the best bet to solving a problem more efficiently is always to use more information about the specific problem you want to solve, instead of pulling up the solution for an overly abstract representations. That can offer scalable orders of magnitude speedup compared to constant factor speedups from just using hardware better.
by ssivark