Honestly, this just looks like one of those lingo-heavy-but-surface-level blog posts that used to make functional programming spaces so insufferable to everyone on the outside
These things are so divorced from the reality of programming, even when they involve actual code instead of fancy lingo. Like in Scala, not a pure functional language, tutorials used to find the most convoluted higher-order functional way to do simple things.
At least in JVM land, it's pretty easy to thwart that optimization. Particularly if the condition is on a mutable yet unchanged in the loop value.
For example:
var map = new HashMap<String, String>();
map.put("foo", "bar");
for (var i : items) {
if ("bar".equals(map.get("foo")) {
doStuff(i);
}
}
Even though `map` isn't mutated, it's hard enough for the JVM to detect and the underlying `get` functions are complex enough that it'll run the `get("foo")` every time, which can be quiet expensive.
Didn’t see it mentioned in the article but isn’t leading with if-statement called a “guard clause”.
I like that pattern but it’s just general best practice I thought.
I think it's sort of obvious that the limit to this general rule is when data dependencies between fors and ifs forbid you from pushing things further up/down.
"the loop runs without a branch, and is a candidate for vectorization".
That's it, that's the article. This matters a lot in huge-scale / scientific computing / HPF, where if you can express something as an operation on vectors on matrices, you win big (those ops parallelize well, can be run on GPUs, clusters, what have you).
I am continually impressed by the ability of LLMs to take trivial ideas and turn them into lengthy and obtuse blog posts with unnecessary analogies.
Honestly, this just looks like one of those lingo-heavy-but-surface-level blog posts that used to make functional programming spaces so insufferable to everyone on the outside
These things are so divorced from the reality of programming, even when they involve actual code instead of fancy lingo. Like in Scala, not a pure functional language, tutorials used to find the most convoluted higher-order functional way to do simple things.
Yet another encroachment on traditionally human activity.
I am the
I've done this for years. Not every time of course but where it makes the code easier to understand and maintain.
Speed was almost never the reason.
I take it you never rewrote a Matlab for loop as a vector/matrix op for insane speedups then :)
What is missing here is any benchmarks backing up this argument for code structure.
Of note, as of C#9 (and maybe prior), the dotnet runtime does this automatically whenever it is deemed safe. https://devblogs.microsoft.com/dotnet/performance-improvemen...
The same technique is applied as an optimization, when deemed safe, in all current gen c compilers (gcc, llvm, etc).
I'm very confused why neither measurements nor references to when this is done automatically in most modern languages is included in the article.
At least in JVM land, it's pretty easy to thwart that optimization. Particularly if the condition is on a mutable yet unchanged in the loop value.
For example:
Even though `map` isn't mutated, it's hard enough for the JVM to detect and the underlying `get` functions are complex enough that it'll run the `get("foo")` every time, which can be quiet expensive.Didn’t see it mentioned in the article but isn’t leading with if-statement called a “guard clause”. I like that pattern but it’s just general best practice I thought.
Swift explicitly has a guard statement for this. Rust's let .. else { ... } is also very similar.
https://docs.swift.org/latest/documentation/the-swift-progra...
I have always phrased this as "Never do one of something".
I like it, but to do fizzbuzz in this way, you'd have to separate what's inside the loop into a reused function.
I think it's sort of obvious that the limit to this general rule is when data dependencies between fors and ifs forbid you from pushing things further up/down.
TL;DR in one sentence:
"the loop runs without a branch, and is a candidate for vectorization".
That's it, that's the article. This matters a lot in huge-scale / scientific computing / HPF, where if you can express something as an operation on vectors on matrices, you win big (those ops parallelize well, can be run on GPUs, clusters, what have you).