Seeds of the project
The ViVA project began in the autumn of 2000, with about 30 people from IBM and NERSC, Kramer says.
The meeting was held "to address the fact that the current road maps of commodity-based computing were not going to serve the scientific community well", Kramer says. "In that discussion, we came up with the idea of adding to commodity processors some additional capability for no or very little cost that would make them more amenable to the scientific computing that goes on now and into the future."
That idea became what is now ViVA, he says. And although NERSC has discussed virtual vectors with other computer makers, IBM was by far the most interested, Kramer says.
IBM isn't the first to try the virtual vector idea. A Hitachi system installed at the Leibniz Computing Centre in Munich employed dozens of nodes, each with eight processors linked into a virtual vector processor. And the Cray X1 has four "single-stream processors", (SSPs), vector processors effectively linked into a single larger vector processor.
Indeed, the virtual-vector idea is more than a decade old, Illuminata analyst Jonathan Eunice says. Silicon Graphics' then-chief technology officer, Forrest Baskett, "looked forward and foretold all this", extrapolating from improvements to RISC (reduced instruction set computing) chips such as IBM's Power products.
"This is the proof of the Baskett theorem," Eunice says. "He put up charts in 1991, showing how much RISC had come up to speed with traditional vector processors (and predicted that) eventually, we're not going to really need specialised ones. We can pretend to have vector processors."
Vector machines allow efficient bulk operations when it comes to retrieving large quantities of data from memory quickly, processing it, then storing it, Willard says. While many scalar computers end up waiting for the appropriate information to arrive from memory, vector systems can stream that data in and out fast enough to keep a processor's calculation engines close to fully loaded.
Another vector advantage is through a technology called "gather/scatter", which lets vector processors easily read and write data to widely dispersed memory locations. Scalar processors, in comparison, deal best with data in contiguous patches of memory.
ViVA-2 will address this memory issue, Kramer says.
But Cray sees obstacles on the horizon for virtual vector machines. One problem is overhead -- the time needed for independent processors to spend on control and synchronisation tasks instead of processing, Scott says.
"Each one of the processors is operating independently, fetching its own instructions, decoding its own instructions," Scott says. "You won't get the sort of execution efficiency by having eight different processors executing independent instructions than you will by having a vector processor issuing a single instruction."
Having it both ways
But for the National Energy Research Scientific Computing Center, ViVA offers versatility, Kramer says.
"The reason we think it's important is NERSC, unlike other sites, runs a very diverse workload. We run large-scale codes for all areas of science, ranging from biology to cosmology, materials science, genomics, proteomics, climate research, astrophysics, high-energy physics, computational fluid dynamics and accelerator design," he says. "Some methods can make good use of vectorisation, and those are pretty well-known. There are a lot of methods that cannot vectorise well."
Cray is betting that vector supercomputers will remain important but also has embraced clusters of scalar machines. The company is building a system called Red Storm for Sandia National Laboratories with thousands of scalar Opteron processors from Advanced Micro Devices, and has begun selling its XD1, a kind of smaller cousin to the mammoth Red Storm cluster.
Price has been a barrier for vector systems. They bring powerful abilities, IDC's Willard says, but, he added, "those advantages at current prices aren't able to really drive a big market."
Vector systems are good for a multitude of scientific problems, says Mark Seager, assistant department head for advanced technology, integrated computing and communication at Lawrence Livermore National Laboratory. Among them: studying wave shapes for image analysis; solving notoriously difficult math problems, called large partial differential equations; and predicting fluid flow and shock wave motion.
However, vector machines are no longer the necessity they once were, even at massive research labs with challenges such as a three-dimensional simulation of a nuclear bomb explosion.
"We do it all on scalar machines. We don't have any vector machines here at Livermore," Seager says. "I think we got rid of the last Cray in 1996 to 1998."
