28 Jun
2018
28 Jun
'18
2:43 p.m.
On Thu, 28 Jun 2018 10:15:38 -0400 "Theodore Y. Ts'o"
<tytso(a)mit.edu>
wrote:
I'll note that Sun made a big bet (one of its
last failed bets) on
this architecture in the form of the Niagra architecture, with a
large number of super "wimpy" cores. It was the same basic idea
--- we can't make big fast cores (since that would lead to high
ILP's, complex register rewriting, and lead to cache-oriented
security vulnerabilities like Spectre and Meltdown) --- so instead,
let's make lots of tiny wimpy cores, and let programmers write
highly threaded programs! They essentially made a bet on the
web-based microservice model which you are promoting.
And the Market spoke. And shortly thereafter, Java fell under the
control of Oracle.... And Intel would proceed to further dominate
the landscape.
I'll be contrary for a moment.
Huge numbers of wimpy cores is the model already dominating the
world. Clock rates aren't rising any longer, but (in spite of claims
to the contrary) Moore's law continues, very slightly with shrinkage
of feature size (which is about to end) and more dominantly with
increasing the number of transistors per square mil by going into
3D. Dynamic power usage also scales as the square of clock rate, so
larger numbers of lower clocked cores save a boatload of heat, and at
some point you have too many transistors in too small an area to take
heat out if you're generating too much.
Some data points:
1. All the largest compute platforms out there (Google, Amazon, etc.)
are based on vast numbers of processors integrated into a giant
distributed system. You might not see this as evidence for the
trend, but it is. No one can make a single processor that's much
faster than what you get for a few hundred bucks from Intel or AMD,
so the only way to get more compute is to scale out, and this is
now so common that no one even thinks of it as odd.
2. The most powerful compute engines out there within in a single box
aren't Intel microprocessors, they're GPUs, and anyone doing really
serious computing now uses GPUs to do it. Machine learning,
scientific computing, etc. has become dependent on the things, and
they're basically giant bunches of tiny processors. Ways to program
these things have become very important.
Oh, and your iPhone or Android device is now pretty lopsided. By
far most of the compute power in it comes from its GPUs, though
there are a ridiculous number of general purpose CPUs in these
things too.
3. Even "normal" hacking on "normal" CPUs on a singe box now runs on
lots of fairly wimpy processors. I do lots of compiler hacking
these days, and my normal lab machine has 64 cores, 128
hyperthreads, and a half T of RAM. It rebuilds one system I need to
recompile a lot, which takes like 45 minutes to build on my laptop,
in two minutes. Note that this box is both on the older side, the
better ones in the lab have a lot more RAM, newer and better
processors and more of them, etc.
This box also costs a ridiculously small fraction of what I cost, a
serious inversion of the old days when I started out and a machine
cost a whole lot more compared to a human.
Sadly my laptop is stalled out and hasn't gotten any better in
forever, but the machines in the lab still keep getting
better. However, the only way to take advantage of that is
parallelism. Luckily parallel builds work pretty well.
Perry
--
Perry E. Metzger perry(a)piermont.com