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Comment by rramadass | Hacker News Reader

This is the right approach. Without (2) trying to learn (1) will just lead to "confusion worse confounded". I also suggest a book recommendation here - https://news.ycombinator.com/item?id=44216478

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I think it depends on your learning style. For me, learning something with a concrete implementation and code that you can play around with is a lot easier than trying to study the abstract general concepts first. Once you have some experience with the code, you start asking why things are done a certain way, and that naturally leads to the more general concepts.

It has got nothing to do with "learning styles". Parallel Computing needs knowledge of three things; a) Certain crucial architectural aspects (logical and physical) of the hardware b) Decomposing a problem correctly to map to that hardware c) Algorithms using a specific language/framework to combine the above two. CUDA (and other similar frameworks) only come in the last step and so a knowledge of the first two is a prerequisite.

This one was my go-to for HPC, but it may be a bit dated by now: https://www.amazon.com/Introduction-Performance-Computing-Sc...

That's a good book too (i have it) but more general than the Ridgway Scott book which uses examples from Numerical Computation domains. Here is an overview of the chapters; example domains start from chapter 10 onwards - https://www.jstor.org/stable/j.ctv1ddcxfs

These sort of books are only "dated" when it comes to specific languages/frameworks/libraries. The methods/techniques are evergreen and often conceptually better explained in these older books.

For recent up to date works on HPC, the free multi-volume The Art of High Performance Computing by Victor Eijkhout can't be beat - https://news.ycombinator.com/item?id=38815334

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