Supercomputer architectures battle for hearts and minds of users

Computerworld - To IT managers, high-stakes supercomputing may seem like the land-speed record: a freak show, amusing but hardly relevant. Oh, a car broke Mach 1? And a defense lab has a 280 TFLOPS computer? Cool. Now let's get back to work.

However, supercomputing specialists are wrestling with problems that will affect everyday IT within the next two to five years. Essentially, improvements in processors have outstripped those in data movement. For some time now, the limiting factor in high-performance computing has been the speed with which data can be moved to and from the processors. Indeed, the cylindrical shape of the iconic Cray supercomputers is an effort to limit the distances data must flow.

Because supercomputing is the sharp end of the technology spear, these data-flow problems -- still manageable in most corporate data centers -- are quickly reaching critical mass in the world's top research facilities. Breakthroughs are needed, and experts acknowledge that answers are elusive.

Backdrop: Multicore, Clusters

There are two key factors in today's supercomputing tumult: multicore chips and the rise of "cluster" supercomputers composed of hundreds or thousands of humble Intel-style CPUs.

Multicore chips place more than one processor on a single integrated circuit. Dual-core PCs are already common, and experts believe this Moore's Law-driven progression will continue so that by 2010, your garden-variety chip will house 64 processors. With each of these processors running four software threads at once, 256 threads could be simultaneously executed on a single chip.

The world’s fastest supercomputer, the IBM Blue Gene/L at Lawrence Livermore National Laboratory, applies its 131,072 processors to problems in molecular dynamics.

Jack Dongarra, a computer science professor at the University of Tennessee and the keeper of the list of the world's top 500 supercomputers, says these elite machines typically cluster 500 to 1,000 processors. The current top gun, the IBM Blue Gene at Lawrence Livermore National Laboratory, can process 280 trillion floating-point operations per second (TFLOPS) and clusters a staggering 131,072 processors. Those clusters must rapidly move huge quantities of information from memory to all those processors and back again. The bandwidth needed for this data flow, along with the latency caused by the sheer distances involved, effectively caps the amount of work the computer can do.

Dongarra says that more than 60% of the top 500 computers are clustered rather than relying on the traditional exotic architectures most commonly associated with Seattle-based Cray Inc. "Clusters have completely changed the scientific computing landscape," he says, because they offer a price/performance ratio that exotic machines can't touch.