Comment by xxs | Hacker News Reader

Comment by xxs | Hacker News Reader

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>Also, working with a ton of small files is a typical real-world task.

Yes, but not compressing them individually, and while staying in the userland (and having SHA1 on each, just for kicks). The real world scenarios tend to be make a big archive (e.g. tar) and compress that thing.

>and not about RAM and cache access efficiency

It's about the size of the cache, workloads that fit L2 vs such that don't, exhibit an amazing performance boost. Pretty much, the performance drops off a cliff when it doesn't fit the L2.

Overall microbenchmarks are extremely/notoriously difficult to get right, and more often than not, they are gamed. However the compression/decompression of geekbench is just bad.

I agree with all your points and also agree that honestly, these scenarios aren't far off from real world tasks.

I get the main issue, which is you could adjust the workload by 10% and achieve a 50% performance loss when you do this at the point where we cross the cache threshold and whatnot.

However I see CPUs unique in that I rank them _for_ these scenarios. A particular might be ranked unfairly, but as long as the test is equal, the better one is infact better, just not by the 50% the test might show but it's still going to be 5% better. I expect my GPU to be idle when it isn't training AI or rendering frames, but for the CPU, it's general purpose in real life, and anything goes.

>just not by the 50% the test might show but it's still going to be 5% better.

Sort of, indeed. Yet, when you see any promotional/marketing material - you see all those phallic bar graphs, and how much bigger it is. Other than that - heavy cache utilization hides inferior memory subsystem (latency/throughput), the latter tends to be quite important in the real world. Overall benchmarks/tests that feature handful of MB as datasets, and run in 100s of ms - should not be used as representative... for most use cases.

That was my initial point - 'don't trust'.

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