Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- This sort of analysis is great.
Now why can't compilers do this sort of thing automatically?
Almost any problem seems to be possible to speed up 1000x in AVX512+days of thought compared to the naive version written in a python loop. If we could automate that whole process for big codebases the performance gains could be huge.
- > Now why can't compilers do this sort of thing automatically?
Because they are not query compilers, ie: They don't know the data.
For example a query compiler could swap index to full scan because it "see" (by runtime statistics) the data not benefit for it.
In the other hand, an optimization here can pessimism there. So optimizers in general should be very conservative because butterfly effects!
by mamcx - it is not easy for a compiler to vectorize
a pragmatic approach: write in a high level interpreted language that rhymes with modern CPUs, vector extensions, memory bandwidth
e.g. apl [0], bqn [1], k [2], kiwi [3]
[0] https://www.dyalog.com [1] https://mlochbaum.github.io/BQN/ [2] https://kx.com [3] https://kiwilang.com- vectors are dense (not boxed) - optimized internal representation (e.g. bitpacked bool vectors) - primitives act on vectors + use avx, neon if possiblegreat article by marshall on BQN performance compared to C and how to think about it
https://mlochbaum.github.io/BQN/implementation/versusc.html
related:
- columnar databases: kdb, duckdb, clickhouse - machine learning frameworks: pytorch, keras, jax, mlxby tosh - Intents matter. Compilers can't see through your skull to infer your intents and thus behave very conservatively unless you override that behavior somehow. This inference, alas, also takes (much) time, so compilers have to balance the compilation time with quality of intents guessed as well. (This is why we can't exactly use LLMs in mainstream compilers, by the way.) So go and make a programming language that preserves your intents by every means; but making it practical would be very difficult.by lifthrasiir
- > Now why can't compilers do this sort of thing automatically?
They do - they just can't assume GFNI instructions are present unless you explicitly say so: https://godbolt.org/z/eYasbKsse
- > 1000x in AVX512+days of thought compared to the naive version written in a python loop
Out of this 1000x speedup you get 100x by just not using python though ;)
Also IIRC the main problem specifically with AVX512 was that mainstream CPUs simply didn't have it, so a smart compiler won't be of much use when the output code only runs on a handful devices.
by flohofwoe - Compilers can’t really, in a meaningful way, change the layout of your data in memory. And you do need to think about your memory layout to get any benefit from SIMD. You’ll notice a lot of compiler auto vectorization insert many instructions just to shuffle data around to get to a usable layout, which negates much of the benefit.by cmovq
- Is the matrix for bit shifting upside down or am I momentarily making a really dumb mistake here? Edit: nvm I missed the footnote which clarifies that for whatever reason the instruction populates the matrix from bottom to top.by fc417fc802
- Zigzag encodings are a common compression scheme used in the Parquet format. It is fun to speculate that these kind of tricks could be applied there in something so commonly under the hood of a lot of data processing and analyticsby wood_spirit
- While we could utilize zigzag encoding (i>>31) ^ (i<<1) to convert SLEB128-encoded type/addend to use ULEB128 instead, the generate code is inferior to or on par with SLEB128 for one-byte encodings on x86, AArch64, and RISC-V. Haven't tried wider values - but zigzag encoding is likely slower as well
// One-byte case for SLEB128 int64_t from_signext(uint64_t v) { return v < 64 ? v - 128 : v; }
// One-byte case for ULEB128 with zig-zag encoding int64_t from_zigzag(uint64_t z) { return (z >> 1) ^ -(z & 1); }
by MaskRay - Worth mentioning that MeshOptimizer (https://github.com/zeux/meshoptimizer) has become one of a handful 'hidden champion' pillar libraries that probably carries half of the gaming industry.
Basically the curl of asset pipelines ;)
by flohofwoe