[wrsh07]

I don't think this is right. Thinking about operations on matrices is not complex. Defining how a single agent should act on its environment is not complex

When you say "without further details or specifics" you're saying "using my default framework of a c/ c descendent world"

The author's point is that sequential programming is one type of simple programming, but it's not the only type, and it doesn't map easily to modern hardware

[Gazoo101]

The author's article generally focuses on C (and possibly descendant languages), but the phrase I am critical of, does not. Furthermore, I explicitly consider a very broad selection of programming languages (many not C-derived) in my opinion. The author's phrasing, I'd argue, paints the entire concept of parallel programming as not hard.

There's some irony to the fact that you re-interpret my opinion as being very specific to C and (indirectly) posit that - in that specific case - parallel programming is hard, and then yourself go on to select a very specific case where parallel programming is not hard, because some matrix operations are independent.

I agree that there are languages that are explicitly built to make parallel programming easy. But in general, and not just related to c or c descendant languages, parallel programming is hard.

[wrsh07]

My point (and I think the points of others responding to you) is that parallel programming is not always hard. That's also what the author is saying.

The common myth - you're doing parallel programming? That sounds hard

It's not always hard. It really isn't! You don't need to be a genius or an expert to write parallel code.

Maybe where we're getting caught up is Cassie K's comment on ml engineering. You don't need to know how to build a microwave to use a microwave. In the same way, you don't need to be a genius or some deep expert in distributed systems to use abstractions that parallelize your programs

To write a parallel program does not require that you know what a mutex is. It just needs you to understand some simple algebraic (6-8th grade) properties about your functions (and, in fact, for library functions, they can be annotated as associative)

There is a broad spectrum of parallel programs. Somebody using a web server implementation? They've made a parallel application

Somebody running tensorflow or pytorch? Also parallel! Even for simple stuff!

You could be a beginner programmer and be taught to make parallel programs without understanding distributed systems. It's not always hard. It's not generally hard. The complex bits are hard. The simple bits use 8th grade math.

[Gazoo101]

> My point (and I think the points of others responding to you) is that parallel programming is not always hard.

Sure, and even more people commenting appear to be of the mind that it is generally hard.

> That's also what the author is saying.

It's not what author is explicitly saying in the statement I'm addressing if you re-read my original comment. There, the author isn't saying that it's not always hard, they're implying that it 'in general' isn't hard.

From your arguments, it would seem you think anything that actually runs in parallel (regardless of whether it programmed as such) can be considered 'parallel programming' and from that perspective, sure, it is super easy. But with that kind of reasoning, you can argue that anyone who only knows how to drive cars with automatic gears is actually a gear-shifting expert and shifting gears is really easy, because it happens automatically for them.