SGI's Altix technology is an example of what is called global shared memory, or GSM, computing. James Lowey, director of high performance biocomputing at the US Translational Genomics Research Institute (TGen) in Arizona, says GSM machines allow organisations using massive datasets to run multiple threads simultaneously.

TGen has just purchased a new Altix 4700 64-bit system, which boasts over half a terabyte of shared memory. It will allow the institute's researchers to search across multiple datasets without having to break up problems into smaller parts, as is often the case for older technology like Beowulf clusters.

"From a computer geek side of things, having the SGI system allows us to greatly increase what is called IPC, or interprocess communications, which is one of the shortfalls of the traditional Beowulf architecture," Lowey says.

"There's a considerable amount of latency in passing information between nodes. So when you break a problem into 64 different chunks it might need chunk one to talk to chunk 64, and the amount of time it takes to do that is prohibitive. With this SGI machine, instead of having to worry about those processes being taken far apart, we have them actually processing simultaneously on the same memory space."

TGen's senior scientific programmer, Dr Waibhav Tembe, says there are two prime examples of how high performance computing is allowing the life sciences to venture into brave new territory.

"Right now there is tremendous interest in studying the genomic variations known as SNPs (single nucleotide polymorphisms) - not independently but as a combination," Tembe says. "How do different SNPs in combination act in disease or non-disease conditions? This requires heavy computation, and there are programs specially written for such combinatorial genomic analysis. The demand for data structures and all of the input/output elements, can't be met with the conventional 32-bit machines.

"This is a classic case where 64-bit machines, such as the one we have from SGI, has helped us immensely in that we can actually analyse all 23 chromosomes simultaneously. The work that would take a couple of months can be finished in just three or four days.

"The other example is on comparing genomic sequences. There are a number of large databases now that store DNA and protein sequences. Many projects at TGen require homology searches, comparing say a few thousand proteins' sequences. Before we had to split the jobs up, but we can now load them up just directly into memory and perform all the computation. It now takes very little time."

While the new machine may have over half a terabyte of RAM, that might yet prove too little, Lowey says. "[Tembe] here has already managed to run it out of memory. You can build it big but it only took him three months to find a problem that was bigger."