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Yeah, I think the dream was more like, “The compiler looks at a map or filter operation and figures out whether it’s worth the overhead to parallelize it automatically.” And that turns out to be pretty hard, with potentially painful (and nondeterministic!) consequences for failure.

Maybe it would have been easier if CPU performance didn’t end up outstripping memory performance so much, or if cache coherency between cores weren’t so difficult.

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eptcyka 11 hours ago | parent | next [-]

Spawning threads or using a thread pool implicitly would be pretty bad - it would be difficult to reason about performance if the compiler was to make these choices for you.

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lazide 12 hours ago | parent | prev [-]

I think it has shaken out the way it has, is because compile time optimizations to this extent require knowing runtime constraints/data at compile time. Which for non-trivial situations is impossible, as the code will be run with too many different types of input data, with too many different cache sizes, etc.

The CPU has better visibility into the actual runtime situation, so can do runtime optimization better.

In some ways, it’s like a bytecode/JVM type situation.

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PinkSheep 7 hours ago | parent [-]

If we can write code to dispatch different code paths (like has been used for decades for SSE, later AVX support within one binary), then we can write code to parallelize large array execution based on heuristics. Not much different from busy spins falling back to sleep/other mechanisms when the fast path fails after ca. 100-1000 attempts to secure a lock.

For the trivial example of 2+2 like above, of course, this is a moot discussion. The commenter should've lead with a better example.

	▲	lazide 7 hours ago \| parent [-]
		Sure, but it’s a rare situation (by code path) where it will beat the CPU’s auto optimization, eh? And when that happens, almost always the developer knows it is that type of situation and will want to tune things themselves anyway.