Thank you so much for attempting a reproduction! (I posted this on Reddit and most commenters didn't even click the link)

For the baseline you need SIMDe headers: https://github.com/simd-everywhere/simde/tree/master/simde. These alias x86 intrinsics to ARM intrinsics. The baseline is based on the previous State-of-The-Art (https://arxiv.org/abs/1209.2137) which happens to be x86-based; using SIMDe to compile was the highest-integrity way I could think of to compare with the previous SOTA.

Note: M1 chips specifically have notoriously bad small-shift performance, so the benchmark results will be very bad on your machine. M3 partially fixed this, M4 fixed completely. My primary target is server-class rather than consumer-class hardware so I'm not too worried about this.

The benchmark results were cpy-pasted from the terminal. The README prose was AI generated from my rough notes (I'm confident when communicating with other experts/researchers, but less-so with communication to a general audience).

  $ ./out/bytepack_eval
  Bytepack Bench — 16 KiB, reps=20000 (pinned if available)
  Throughput GB/s

  K  NEON pack   NEON unpack  Baseline pack   Baseline unpack
  1  94.77       84.05        45.01           63.12          
  2  123.63      94.74        52.70           66.63          
  3  94.62       83.89        45.32           68.43          
  4  112.68      77.91        58.10           78.20          
  5  86.96       80.02        44.32           60.77          
  6  93.50       92.08        51.22           67.20          
  7  87.10       79.53        43.94           57.95          
  8  90.49       92.36        68.99           83.88

Maybe this could help you: https://github.com/simd-everywhere/simde/issues/1099

I never understood why they couldn't just include a movmskb instruction to begin with. It's massively useful for integer tasks, not expensive to implement as far as I know, and the vshrn+mov trick often requires an extra instruction either in front or after (in addition to just being, well, pretty obscure).

NEON in general is a bit sad, really; it's built around the idea of being implementable with a 64-bit ALU, and it shows. And SVE support is pretty much non-existent on the client.

Nice article! I personally find the ARM ISA far more cohesive than x86's: far less historical quirks. I also really appreciate the ubiquity of support for 8-bit elements in ARM and the absence of SMT (make performance much more predictable).

Good work, wish I saw it earlier as it overlaps with a lot of my recent work. I'm actually planning to release new SOTAs on zigzag/delta/delta-of-delta/xor-with-previous coding next week. Some areas the work doesn't give enough attention to (IMO): register pressure, kernel fusion, cache locality (wrt multi-pass). They also fudge a lot of the numbers and comparisons eg, pitching themselves against Parquet (storage-optimised) when Arrow (compute-optimised) is the most-comparable tech and obvious target to beat. They definitely improve on current best work, but only by a modest margin.

I'm also skeptical of the "unified" paradigm: performance improvements are often realised by stepping away from generalisation and exploiting the specifics of a given problem; under a unified paradigm there's definite room for improvement vs Arrow, but that's very unlikely to bring you all the way to theoretically optimal performance.

From the working set size and knowledge of hardware cache behaviour. Whenever you access data from memory not already in-cache it's copied four times: to L3, L2, L1 and to CPU registers. As you access data, the hardware evicts old cache entries to make space for it.

If you loop through an array once, and then iterate through it again you can figure out where it will be cached based on the array size.

Does it fit 32K? Does it have some weird aliasing issue because you caused cache extinction with too many power of two sizes? And if you don't know the answer to these just check L1d hitrate with perf.

If you have an array of numbers with a known upper-bound, such as enums with 8 possible values (representable with 3 bits), and a memory-bound operation on those numbers eg, for (int i; i < n; i++) if (user_category[i] == 0) filtered.push_pack(i), which is common in data warehouses, using my code can more than 2x performance by allowing more efficient usage of the DRAM<->CPU bus.

I'm assuming you're referring to BFM/EXTR? NEON absolutely improves here.

The core I developed on (Neoverse V2) has 4 SIMD ports and 6 scalar integer ports, however only 2 of those scalar ports support multicycle integer operations like the insert variant of BFM (essential for scalar packing).

More importantly, NEON progresses 16 elements per instruction instead of 1.