Re: Large RAM sizes in embedded systems
- From: shoppa@xxxxxxxxxxxxxxxxx
- Date: 28 Dec 2006 10:05:10 -0800
larwe wrote:
I'd be curious to hear from anyone who has worked on embedded systems
with relatively large amounts of directly-addressable RAM.
I'm discussing with a colleague/client an application that would work
on a nominally 740GB data set. The nature of the data and the required
processing is such that it's a "must-have" performance improvement to
hold the entire data set in RAM rather than swapping in from some
secondary storage mechanism. It's perfectly acceptable for the machine
to take an entire week to cold-boot. It's not acceptable, once booted,
for it to wait several seconds to page in data from a hard disk :)
You're going to have to be more specific.
Is there some way to frame up the data access patterns? Like, any
natural frame size, any typical pattern between frames?
A hard drive array will have no problem to BEGIN serving up a purely
randomly chosen frame in the 10ms ballpark, which is a lot faster than
your "several seconds".
If your frames are truly huge then each frame would be split up among
multiple disks so that you get the sum of all the bandwidths.
740GB data sets are not all that unusual. Treating it as "I gotta have
it all in memory at once" is unusual.
Others have already remarked on tools for associative searches. There
are specialized RAID arrays with built-in knowledge of a few very very
specific associative searches. Indeed it is beginning to sound like
this is what you want to build :-). In the 1970's these specialized
drive arrays were sometimes called "rotating associative array
memories", Googling for terms like that is difficult because now all
those terms have been corrupted for other meanings. I think a more
current term might be "content addressable memory".
If the data set is fixed, and the thing you are searching for is
two-dimensional and of known size, then there are some really funky
photographic techniques that use optics and holograms instead of
computers.
Tim.
.
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