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There's No Such Thing As a General-Purpose Processor
CowboyRobot writes: David Chisnall of the University of Cambridge argues that despite the current trend of categorizing processors and accelerators as "general purpose," there really is no such thing and believing in such a device is harmful.
"The problem of dark silicon (the portion of a chip that must be left unpowered) means that it is going to be increasingly viable to have lots of different cores on the same die, as long as most of them are not constantly powered. Efficient designs in such a world will require admitting that there is no one-size-fits-all processor design and that there is a large spectrum, with different trade-offs at different points."
"The problem of dark silicon (the portion of a chip that must be left unpowered) means that it is going to be increasingly viable to have lots of different cores on the same die, as long as most of them are not constantly powered. Efficient designs in such a world will require admitting that there is no one-size-fits-all processor design and that there is a large spectrum, with different trade-offs at different points."
David Chisnall says : parallel is not the same as in series. World gasps.
If a "general purpose" processor solves your problems fast enough, it's good enough.
How the fuck is that "harmful"?
Geez, you'd think TFA is just a blowhard looking for page hits.
100 watts corresponds to 2000 Calories per day
Also, watt represents momentary consumption and calories are a fixed mass of energy
Calories and calories per day are not the same unit. A calorie is 4.18 kJ, and a calorie per day is 4.18 kJ / 86.4 ks = 48.4 mW. Multiply this by 2000 and you'll end up very close to 100 W.
From TFA:
It's therefore not enough for a processor to be Turing complete in order to be classified as general purpose; it must be able to run all programs efficiently. [emphasis added]
Um, nope.
A general-purpose anything is rarely as efficient at a given task as a special-purpose version of the same thing. Sometimes you really do want your computer chip to be a "Jack of all trades, master of none."
Knowledge is how to play a game, intelligence is how to win, wisdom is knowing what game to play.
It isn't all that many years ago that the floating point was handled by either software emulation or a co-processor. Now we're using GPUs as co-processors. There are also audio designs that act as co-processors. Several enterprise systems have encryption co-processors. IBM is notorious for putting specialized processors in their mainframes. Several chips have the GPUs embedded on-chip already.
I'd argue that putting specialized chips on-die doesn't affect the general-purpose nature of the compute core that controls those resources at all. The whole article is red herring trying to establish a distinction between on-chip and off-chip processing that has to do more with the scalability of silicon manufacturing techniques than it does any distinguishing feature of the designs.
Let's face it -- if you want to really accelerate a task, you design silicon specifically for that task and interface it to a general purpose core. The article discusses nothing new in the world of computing.
I do not fail; I succeed at finding out what does not work.
A whole woman consumes 100 watts.
D'oh! Human, not woman.
I got myself an nvidia woman. She takes 400 watts.
I should use this sig to advertise my book ISBN-13 : 978-1501515132.
A lot of the value in your article is lost by trying to shoehorn "general purpose processors" into an argument about task-optimisation. The difference between properties relating to computational power and those relating to performance is really basic textbook stuff that we teach to undergraduates. Being able to run any program, and being able to run any program efficiently, is a difference taught in undergraduate architecture courses.
The parts of your article that are interesting and valuable would have been better served by a narrative that does not rely on a straw man. Cleanly separating the issue of power / performance and explaining that task-neutral optimisation is impossible would have been a better article, and one that would have been easier to write. There is a natural analogy with representation-bias in machine learning that would have provided more explanatory power without the unnecessary rhetoric. I know its the queue, but even so I am a little disappointed in your reviewers.
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