Linked by MOS6510 on Fri 17th May 2013 22:22 UTC
Hardware, Embedded Systems "It is good for programmers to understand what goes on inside a processor. The CPU is at the heart of our career. What goes on inside the CPU? How long does it take for one instruction to run? What does it mean when a new CPU has a 12-stage pipeline, or 18-stage pipeline, or even a 'deep' 31-stage pipeline? Programs generally treat the CPU as a black box. Instructions go into the box in order, instructions come out of the box in order, and some processing magic happens inside. As a programmer, it is useful to learn what happens inside the box. This is especially true if you will be working on tasks like program optimization. If you don't know what is going on inside the CPU, how can you optimize for it? This article is about what goes on inside the x86 processor's deep pipeline."
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RE[5]: Comment by Drumhellar
by Alfman on Mon 20th May 2013 04:03 UTC in reply to "RE[4]: Comment by Drumhellar"
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I'd like to say software engineers could figure it out given widely accessible hardware, but I might be overestimating our abilities ;) Most CS grads these days just end up becoming ridiculously overqualified web devs since that's where most jobs are.

"The holy grail is being able to convert software source code into logic gates. There's plenty of work on that, but the results aren't necessarily all that great. There's a huge difference in performance between a custom-designed FPGA circuit (i.e. knowing what you're doing) versus something that came out of an automatic translator."

This surprises me a bit. Even though the human mind is an incredible analytical machine, it has it's limits whereas computers just keep getting better. In the Kasparov vs Deep Blue chess championship, it was inevitable that the brute force capabilities of the computer would ultimately overtake the best humans, the only question was when.

At university I made a realtime 3d java program to place components on a virtual circuit board using genetic algorithms and a fitness function. It was just a fun project I presented for an undergrad GA course I was taking, to be honest I don't know if it's solutions were any good since it was never compared against expert solutions. But in any case my gut instinct tells me that given enough computing power, even a naive algorithm should be able to brute force the finite solution space and consistently beat the best humans. I do believe you when you say automatic solutions aren't as good as experts, however do you think that could change if there were more computing power thrown at the FPGA problem?

I'm interested in what you have to say about it because I don't have expertise with FPGAs and I don't personally know anyone else who does either.

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