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What's worked in Computer Science: 1999 vs. 2015 (2015)

danluu.com

21–30 of 155 posts

Re: What's worked in Computer Science: 1999 vs. 2015 (2015)

#21
I guess from some sense, all web apps and all GUI apps using the GPU are using parallelism. That's almost everything. Those are both leveraging shared-nothing type architectures so you can think in a single thread and apply it to concurrent work loads for parallelism.

Does that not count? What would satisfy this category?

Re: What's worked in Computer Science: 1999 vs. 2015 (2015)

#23
post #17
post #9

> Fancy type systems We are taking steps to this direction. By adding optional typing to dynamic languages Python and JavaScript/TypeScript. And then type checker tools and local programming style guides are making using these maybe less optional, and more mandatory.

IMO the day python types become mandatory there will be a fork. It would be such a total betrayal of it's reason for existing that we would have to invent another untyped or duck-typed language again.

I mean, your company or your project may require you to write type hints and use a type checker tool. Not that Python the language makes typing mandatory.

Re: What's worked in Computer Science: 1999 vs. 2015 (2015)

#25
post #17
post #9

> Fancy type systems We are taking steps to this direction. By adding optional typing to dynamic languages Python and JavaScript/TypeScript. And then type checker tools and local programming style guides are making using these maybe less optional, and more mandatory.

IMO the day python types become mandatory there will be a fork. It would be such a total betrayal of it's reason for existing that we would have to invent another untyped or duck-typed language again.

nagware like Typescript have turned type bolt-ons into a religion almost. It has its place but really gets in the way in others. Hence, the reason untyped languages exist in the first place.

Re: What's worked in Computer Science: 1999 vs. 2015 (2015)

#29

I would like to suggest that the classical taxonomy of RISC/CISC dichotomy is basically non-existent nowadays -- namely because both sides have influenced each other. It is well known that CISC has taken a lot of inspirations from RISC designs (such as having a lot more registers in x64), and RISC designs also taken some inspirations from CISC (such as having SIMD/vectorization units). In other words, the line betwee…

Yep. RISC was interesting when gate budgets for CPU pipelines were seriously limited. It was interesting because before RISC the industry had been merrily spending the gate budget increase on adding lots of use-specific instructions. The RISC people pointed out that if you removed support for all the fancy instructions you had enough gate budget for the ALU to be nicely pipelined, and then you could wind up the clock rate greatly and this was worth much more than the fancy instructions.

For decades now we've had enough gate budget to have nicely pipelined designs with complex instruction sets, so that's what everyone does. RISC solves a problem that no longer exists.

Re: What's worked in Computer Science: 1999 vs. 2015 (2015)

#30
post #2

The phone networks and basic infrastructure for the internet in many countries wouldn’t work without Erlang. Which is functional… but maybe that doesn’t count?

On the other hand, the banking infrastructure in many countries wouldn't work without COBOL, scientific research wouldn't work without MATLAB, and healthcare wouldn't work without MUMPS. Many languages and paradigms, good and bad, end up entrenched within an industry.
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