Bill Hart wrote:
On Apr 27, 8:55 pm, rjf <fate...@gmail.com> wrote:
Oh, just another note.

There are people who have made their whole careers on devising
asymptotically fast algorithms
which have never been implemented, or (if they have been implemented)
are not fast because
their overly-simplified analysis does not fit the currently available
computer efficiency profile.
Indeed, their analysis may not fit any past profile of computers, and
their algorithms were never
practical.

Interesting observation. Furer's algorithm is not practical, as are
some matrix multiplication algorithms. Do you have any other examples
in mind?

Note, for example, that memory access is far more expensive than
arithmetic, and that
many computers now can multiply or add in comparable time; and they
have several multipliers. Etc.

I would say that memory access if far more expensive than addition,
but not arithmetic in general. FFT multiplication is n log n
(sometimes with a log log n, depending on what you are multiplying)
and I would say the memory access perhaps adds a hidden log n factor.

Can you perhaps be more specific as to what you are hinting at?
On current microprocessors a multiply can be done in a few clock ticks (probably one apparent clock tick if the in-processor pipelines are fully overlapped). An L1 cache access is about 3 clock ticks, an L2 can be dozens, and a memory access can be several hundred or more (depending on DDR/2/3/..., inter-processor MESI cache, prefetch instructions, etc.)

By analogy, it is more expensive to peel an egg than to slice a carrot but the time is buried under the time it takes to get one out of the fridge (L1), the supermarket (L2), the farm
(main memory) or grow a new one (disk).

So if an FFT that is optimized for, say 128 byte cache lines with minimal cache collisions, then the multiply times will matter. But changing the FFT to ignore cache line sizes so the fetch breaks a cache line on each read and the multiply times are completely irrelevant.
RJF

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