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[3]: Comment by Drumhellar
by Alfman on Sat 18th May 2013 05:22 UTC in reply to "RE[2]: Comment by Drumhellar"
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You sound very knowledgeable, certainly more than me. What do you think about cpu cores eventually being replaced / enhanced with massive FPGAs?

The issue I have with current CPU architectures is how there's so much hardware and R&D being thrown at running sequential instruction sets in parallel rather than actually using native parallel instruction sets in the first place. We have undeniably seen dramatic gains for sequential code, and yet, all this focus on sequential code optimization seems to be a major detractor away from what could have been a much better overall strategy for maximizing parallel computation.

For illustrative purposes, take the case of bitcoin mining as good example of a parallel problem where performance is king and software compatibility isn't a factor. The next link contains a sizable dataset very roughly showing how different computational technologies compare:

Intel's latest processors top out at ~65Mhash/s for 6*2 hyperthreaded cores at 200W. As we'll see, the sequential programs running on these super-scalar CPUs cannot compete with real parallel algorithms.

The ARM processors listed top out at ~1Mhash/s running on .5W. If we ran 400 of these to match intel's power consumption, we'd get very roughly 400Mhash/s.

Now when we look at FPGAs, they have 400+ Mhash/s running on less than 20W. If we ran 10 of these to match 200W, we'd get 4000Mhash/s, or 62X the processing power of the x86 cpu.

There are ASICs that have 5000Mhash/s running on 30w (I mention it for comparison only, obviously it's not a reprogrammable part so it wouldn't have a place in a software programmable PC).

While I know CUDA is doing it's part to introduce parallel software to the PC via GPUs, it still fairs poorly compared to the FPGAs. In fact GPU bitcoin miners are throwing in the towel (like CPU miners before them) because electricity costs more than the value of the bitcoins earned.

So in your expert opinion, do you think we're bumping against the wall of diminishing returns with today's superscalar CPUs? Do you see FPGAs as a good contender for even higher performance PCs in the future (assuming we ever get away from sequentially based software programming practices)?

Edit: I realize the numbers are very imprecise and might not even be apples to apples. Never the less bitcoin was the best example I could come up with to compare parallel computation technologies.

Edited 2013-05-18 05:41 UTC

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