Communications - August 2011

parallel supercomputing. Kahaner be-
lieves China’s relative isolation from
Western influences may have led to
economics that favor such innovations.
“They’re not so tightly connected with
U.S. vendors who have their own per-
ception of things,” he says. “Potential
bang for the buck is very strong in Asia,
especially in places like China or India,
which are very price-sensitive markets.
If your applications work effectively on
those kinds of accelerator technolo-
gies, they can be very cost effective.”
According to Satoshi Matsuoka, di-
rector of the Computing Infrastructure
Division at the Global Scientific Infor-
mation and Computing Center of the
Tokyo Institute of Technology, China’s
comparatively recent entry into HPC
may help them in this regard. “Six
years ago, they were nowhere, almost at
zero,” he says. “They’ve had less legacy
to deal with.” By contrast, Gupta says,
programmers in more experienced
countries have to undergo re-educa-
tion. “Young programmers have been
tainted into thinking sequentially,”
he notes. “Now that parallel program-
ming is becoming popular, everybody
is having to retrain themselves.”
These issues will only get more
complicated as time progresses. Horst
Simon, deputy laboratory director of
Lawrence Berkeley National Laborato-
ry, says a high level of parallelism is nec-
essary to progress past the 3GHz–4GHz
physical limit on individual proces-
sors. “The typical one-petaflop system
of today has maybe 100,000 to 200,000
cores,” says Simon. “We can’t get those
cores to go faster, so we’d have to get a
thousand times as many cores to get to
an exaflop system. We’re talking about
100 million to a billion cores. That will
require some very significant concep-
tual changes in how we think about ap-
plications and programming.”

matters of energy

Hybrid architectures have historically had another advantage besides their parallelism. They have also usually used less energy than comparable CPU-only systems. In the November 2010 list, hybrid systems generally delivered flops more efficiently than the CPU-only systems.

But the new Top500 list shows that the architectural battle over energy efficiency is still raging. The CPU-based

in today’s hybrid
CPu/GPu
supercomputers,
GPus provide the
brute calculation
power, but rely
heavily on CPus
for other tasks.

K Computer attains an impressive 825 megaflops (Mflops) per watt even as the third-place, CPU-based Jaguar ekes out a so-so 250 Mflops/watt. By comparison, the hybrid Tianhe-1A achieves 640 Mflops/watt, Nebulae gets about 490 Mflops/watt, and Tsubame 2.0 gets 850 Mflops/watt. (The list’s average is 248 Mflops/watt.)

The most energy-efficient system is the U.S.’s CPU-based IBM BlueGene/Q Prototype supercomputer, which entered the Top500 in 109th place, with an efficiency of 1,680 Mflops/watt. The IBM BlueGene/Q tops the Green500, a list derived from the Top500 that ranks supercomputers based on energy efficiency. But despite BlueGene/Q’s supremacy, eight of the Green500’s top 10 are GPU-accelerated machines.

Energy is no small matter. The K Computer consumes enough energy to power nearly 10,000 homes, and costs $10 million a year to operate. These costs would significantly increase in an exaflop world, notes Simon.

Looking ahead

Despite the headlines and U.S. senators’ statements, Dongarra and colleagues are quick to dismiss the supercomputing competition as a “race.” At the same time, he expects to see an increase in Top500 scores, and notes that several projects are aiming for the 10-petaflop target, which could be realized by the end of 2012. But the real prize is the exaflop, which the U.S. government, among others, hopes to achieve by 2020.

Matsuoka believes this goal is possi-
ble, but it will be “a very difficult target,”
especially when compared with tradi-
tional expectations. “Look at Moore’s
law,” he says. “Computers will get about
100 times faster in 10 years. But going
from petascale to exascale in 10 years is
a multiple of a thousand.” Having said
that, he notes that it is been done be-
fore—twice. “We went from gigaflops in
1990 to teraflops in about 10 years, and
then to petaflops in another 10 years.
Extrapolating from this, we could go to
exascale in the next 10 years.”
But Dongarra warns that we won’t
reach that stage solely by focusing on
hardware. “We need to ensure that
the ecosystem has some balance in it.
Major changes in the hardware will re-
quire major changes in the algorithms
and software,” he says. “We’re look-
ing at machines in the next few years
that could potentially have billions of
operations at once. How do we exploit
billion-way parallelism?”
The payoffs could be enormous. Su-
percomputing is already widely used in
fields as diverse as weather modeling,
financial predictions, animation, fluid
dynamics, and data searches. Each of
these fields embodies several applica-
tions. By way of example, Matsuoka
says, “You can’t do genomics without
very large supercomputers. Because of
genomics, we have new drugs, ways of
diagnosing disease, and crime investi-
gation techniques.” While exaflop com-
puters will spawn now-unimagined
uses, any current increases in speed
as we race toward that goal will greatly
benefit many existing applications.