parallelism vs. efficiency

By: hobold (hobold.delete@this.vectorizer.org), August 13, 2019 4:43 am
Room: Moderated Discussions
Travis Downs (travis.downs.delete@this.gmail.com) on August 12, 2019 9:15 am wrote:
>
[...]
> You really have to go out of your way to find a load that truly can't be parallelized: something
> like gz decompression of a single large file: but that's just a consequence of the way gzip is
> designed - another more or less equivalent format could support it with a few changes..
>
Pragmatically you are correct: any and all serial dependencies can be forcibly broken, if you can accept that this changes the algorithm, i.e. that the parallelized variant no longer delivers bit identical results.

But conceptually - and this is more of a philosophical tangent - a hypothetical "best" algorithm is the one that manages to extract the largest benefit of all available information that has been computed earlier. Thus, maximal re-use of intermediate results, and consequently strong serial dependency on prior values/decisions.

So if you tune for, say, energy efficiency, or compression ratio, there is a "natural" tendency for algorithms to acquire more and more serial dependencies. I don't think this is an inherently unbreakable natural law; but IMHO it is a very compelling statistical tendency, similar to "entropy always increases". Of course, in practice we don't program like that; neither the informational perfection, nor the explicit ignorance of parallelism.
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