technical Perspective
computer Science takes on
molecular Dynamics
by bob Colwell
PUT THIS ON your to-do list: read the following paper by researcher David Shaw
and colleagues that describes their Anton molecular dynamics (MD) engine.
Shaw’s Anton engine applies leading-edge computer science concepts to the
biologically crucial problem of modeling molecular interactions. In an
era when much of our most advanced
computer technology is spent creating
ever more horrible creatures that we
can shoot ever bigger virtual holes in,
the idea of productively using this technology to explore nature at its most
up-close-and-personal is both exciting
and reassuring.
The nature of the computational
problem Anton aims to solve, and the
unique aspects of the resulting design,
are fascinating peeks into a corner of
the computer design space we seldom
get to visit—even though each of us
is a biological machine that relies on
the correct functioning of molecular
mechanisms. When diseases cause
these mechanisms to go awry, medical
researchers try to infer the causes and
possible remedies from very indirect
and error-prone evidence, as they lack
direct means of measuring or simulating the molecular underpinnings.
David Shaw calls his new instrument
a “computational microscope,” and if
successful it stands to make the same
kind of game-changing impact that
Anton van Leeuwenhoek’s original
optical microscope once did. (Shaw’s
machine was named in van Leeuwenhoek’s honor.)
To appreciate what Shaw’s machine
is attempting, consider a system containing a realistic protein molecule
together with a few layers of water
molecules, which might together encompass tens of thousands of atoms.
If calculation of the force between any
two atoms takes 10 computer operations, then the total ops required per
time step would be ( 104 atoms) × ( 104
atoms) × 10 ops/atom = 109 ops. Time
slices are on the order of femtoseconds
( 10-15 seconds), and simulations must
run for milliseconds ( 10-3 seconds) to
capture the biology being modeled. So
we’ll need to run those 109 ops for 1012
slices to reach a simulated millisecond—that’s 31,000 years. We need six
orders of magnitude speedup, roughly
three orders of magnitude beyond today’s fastest supercomputers.
But even if you weren’t a biological
unit with a vested interest in this effort,
you could still appreciate the Anton design from a computer system perspective. General-purpose computer systems aspire to run everything well, but
no one thing spectacularly well. Anton
is designed to run a specific molecular
dynamics workload spectacularly well.
While a well-designed general system
can bottleneck 100 different ways on
100 different benchmarks, Anton must
try, in essence, to bottleneck everywhere, all at once, on its one workload.
This balancing act must be attempted in the face of imperfect knowledge
of that one workload. For example,
electrostatic interactions between two
atoms that aren’t sharing any electrons
are considered to be well understood,
and are the most numerous, so Anton
applies very specific, very parallel, and
very inflexible hardware to handling
them. Less is known about the infrequent bonded interactions, so those
calculations are allocated to a much
more flexible subsystem that will allow
experimentation with various “force
field” models and algorithms.
What might go wrong with the Anton effort? Subtle errors arising from
the class of force fields that Anton is
designed to handle efficiently may accumulate over the extremely long MD
runtimes; in a custom machine with
no operational experience, soft errors
could strike much more often and
substantially slow its performance;
quantum effects may turn out to be
necessary, beyond the classical force
field being modeled here; some clever
graduate student may come up with a
software-based approach that reduces
Anton’s two-orders-of-magnitude performance advantage to only one (which
might no longer be enough to justify
its hardware expenditure). Or Anton
might become a victim of its own success if early learnings point to much
better (and much different) MD algorithms that no longer fit well into Anton’s overall structure.
But what if things go right Benoit
Roux, an MD researcher now at the
University of Chicago, said that as soon
as Anton has delivered its first verified
scientific result he will want an engine
of his own, and so will everyone in the
entire MD field. Roux points out that
molecular biologists must normally
have “elaborate strategies to prevent
fooling themselves” in their macro-scale experiments. With Anton, “we’ll
be able to do insane things with unknown problems and two weeks later
we’ll discover how the molecules actually move. … Anton will revolutionize
molecular biology.”
It is not often that a science reaches
a clear tipping point—when it advances very quickly, virtually exploding into
a new shape and venue. Our own field
of computing has done that several
times. Many physicists expect this of
the Large Hadron Collider currently being completed in Europe. Shaw and his
coworkers are attempting nothing less
in the field of molecular dynamics. As
a computing professional, I am proud
of their efforts, I salute their attempt
to drive an extremely important basic
science forward, and I heartily recommend their paper.
Bob Colwell ( bob.colwell@comcast.net), former chief
architect of Intel’s IA- 32 microprocessors, is now an
independent consultant.