[Beowulf] Multicore Is Bad News For Supercomputers
richard.walsh at comcast.net
richard.walsh at comcast.net
Fri Dec 5 20:52:35 PST 2008
Yes, the stacked DRAM stuff is interesting. Anyone visit the siXis booth at
SC08? They are stacking DRAM and FPGA dies directly onto SiCBs (Silicon
Circuits Boards). This allows for dramatically more IOs per chip and finer
traces throughout the board which is small, but made entirely of silicon. They
promise better byte/flop ratios and more total memory per unit volume.
----- Original Message -----
From: "Eugen Leitl" <eugen at leitl.org>
To: info at postbiota.org, Beowulf at beowulf.org
Sent: Friday, December 5, 2008 7:48:43 AM GMT -05:00 US/Canada Eastern
Subject: [Beowulf] Multicore Is Bad News For Supercomputers
Multicore Is Bad News For Supercomputers
By Samuel K. Moore
Trouble Ahead: More cores per chip will slow some programs [red] unless
there’s a big boost in memory bandwidth [yellow
With no other way to improve the performance of processors further, chip
makers have staked their future on putting more and more processor cores on
the same chip. Engineers at Sandia National Laboratories, in New Mexico, have
simulated future high-performance computers containing the 8-core, 16‑core,
and 32-core microprocessors that chip makers say are the future of the
industry. The results are distressing. Because of limited memory bandwidth
and memory-management schemes that are poorly suited to supercomputers, the
performance of these machines would level off or even decline with more
cores. The performance is especially bad for informatics
applications—data-intensive programs that are increasingly crucial to the
labs’ national security function.
High-performance computing has historically focused on solving differential
equations describing physical systems, such as Earth’s atmosphere or a
hydrogen bomb’s fission trigger. These systems lend themselves to being
divided up into grids, so the physical system can, to a degree, be mapped to
the physical location of processors or processor cores, thus minimizing
delays in moving data.
But an increasing number of important science and engineering problems—not to
mention national security problems—are of a different sort. These fall under
the general category of informatics and include calculating what happens to a
transportation network during a natural disaster and searching for patterns
that predict terrorist attacks or nuclear proliferation failures. These
operations often require sifting through enormous databases of information.
For informatics, more cores doesn’t mean better performance [see red line in
“Trouble Ahead”], according to Sandia’s simulation. “After about 8 cores,
there’s no improvement,” says James Peery, director of computation,
computers, information, and mathematics at Sandia. “At 16 cores, it looks
like 2.” Over the past year, the Sandia team has discussed the results widely
with chip makers, supercomputer designers, and users of high-performance
computers. Unless computer architects find a solution, Peery and others
expect that supercomputer programmers will either turn off the extra cores or
use them for something ancillary to the main problem.
At the heart of the trouble is the so-called memory wall—the growing
disparity between how fast a CPU can operate on data and how fast it can get
the data it needs. Although the number of cores per processor is increasing,
the number of connections from the chip to the rest of the computer is not.
So keeping all the cores fed with data is a problem. In informatics
applications, the problem is worse, explains Richard C. Murphy, a senior
member of the technical staff at Sandia, because there is no physical
relationship between what a processor may be working on and where the next
set of data it needs may reside. Instead of being in the cache of the core
next door, the data may be on a DRAM chip in a rack 20 meters away and need
to leave the chip, pass through one or more routers and optical fibers, and
find its way onto the processor.
In an effort to get things back on track, this year the U.S. Department of
Energy formed the Institute for Advanced Architectures and Algorithms.
Located at Sandia and at Oak Ridge National Laboratory, in Tennessee, the
institute’s work will be to figure out what high-performance computer
architectures will be needed five to 10 years from now and help steer the
industry in that direction.
“The key to solving this bottleneck is tighter, and maybe smarter,
integration of memory and processors,” says Peery. For its part, Sandia is
exploring the impact of stacking memory chips atop processors to improve
The results, in simulation at least, are promising [see yellow line in
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