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Twitter finds Ruby faster with gcc optimizing for size than speed (evanweaver.com)
63 points by jim-greer 2676 days ago | hide | past | web | 15 comments | favorite

Linux (the kernel) has been aware of this result for a long time. Here's a thread from 2001, where Linus says

"I would, for example, suspect that a "correct" optimization strategy for 99% of all real-world cases (not benchmarks) is: if it doesn't have floating point, optimize for the smallest size possible. Never do loop unrolling or anything fancy like that. "


and a similar thread http://lkml.indiana.edu/hypermail/linux/kernel/0302.0/1068.h...

And the 2.6.15 (2005) changelog which exposes a configuration option to compile the kernel optimized for size http://lkml.org/lkml/2005/12/18/139

Unrolling loops made sense when memory access was cheap. That is, until about mid 80s. Since the advent of caches, making your code (and as much data as possible) fit inside them was the way to go.

> This is an unusual phenomenon

I don't think that's unusual at all. I've found it to often be the case. Cache misses will kill performance.

Jep, the most impressive thing I have seen about this was when a coworker was able to squeeze the processing of (small) images into the CPU-cache only (and improving the steps used to get the image there). The speedup was a factor around 15.

Apple's default optimization level is also for size, not speed, and that ends up being faster there as well. They ran an entire session a couple of years ago at WWDC, explaining how and why -Os ends up being a superior solution--basically, you blow out the cache less, which ends up mattering far more than forcing the processor to do as much as possible on every cycle.

This is an interesting source of "death-of-a-thousand-cuts" performance problems in C++ codebases, as well. Code you never even execute can slow you down; stack-unwinding code for unhandled exceptions takes up precious icache bytes between the bodies of useful functions. Good luck finding this with a profiler; every function executes epsilon slower, since a cache miss is that much more likely to fetch its body.

The stack unwinder should be a separate function altogether, not interspersed with your code.

Activation record cleanup, on the other hand, will be inside your routines, and will be executed both in cases of normal and abnormal exit. But this isn't the responsibility of exceptions; you have to do this cleanup even on normal exit.

Code that runs only in exceptional cases is comparatively rare. But with exceptions, you can move that code somewhere else entirely; and with PC-based exception handling, the space cost is only borne when an exception is thrown, when the PC lookup tables need to be paged in. In this scenario, the tradeoff between exceptions and error codes becomes relevant: exceptions let your code get smaller at the cost of a hit when an exception is actually thrown, while error codes bloat your code.

Of course, all of the above is not specific to C++. C++ has other deficiencies which can lead to suboptimal pathologies in practice.

Can anyone mention (or point to) tips on what kind of code makes good use of cache? I'm looking for interesting details. "Use less memory" is a bit general.

This is far and away the most detailed explanation I've seen: http://people.redhat.com/drepper/cpumemory.pdf

On the graph, how can there be more replies per second (the y axis) than requests (the x axis)? Look at the graph at x=80 (actually, it's slightly under, maybe 79). There are up to 85 replies.

Hm... I wonder when REE 1.87 will be made publicly available? My team is looking to move to 1.87, and I'd rather like to hold off for REE 1.87 if possible.

REE 1.8.7 should be released very soon. I tried a private pre-release and it worked like a charm.

Surely icc would help more?

Why would icc help more?

Not only is gcc -Os faster for every benchmark I've ever tried, ICC is also some 10% faster again at running ruby interpreter than GCC.

However, none of these speedups is as large as upgrading to Ruby 1.9

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