New ASU Computer Link Facilitates Biological Research

Experts at the Arizona State University are proud to announce the dedication of a new data-processing link between two research facilities, an event that marks the beginning of a new type of scientific collaboration.

The new connection was installed between the High Performance Computing Initiative (HPCI) and the Phoenix-based Translational Genomics Research Institute (TGen), officials at the university announce.

The HPCI is part of the renowned ASU Ira A. Fulton Schools of Engineering. The data-link that the two research centers now share will make it easier to conduct biological and medical research with the aid of informatics and advanced computers.

It is the hope of ASU experts that the powerful, high-speed, data-processing link will enable more rapid progress in research pertaining to understanding and developing treatments for debilitating diseases.

These include Alzheimer's disease (a form of dementia), diabetes and related disorders, as well as a large variety of cancer types. Progress in this field is currently happening at a snail's pace.

Using advanced supercomputers, linked via high- bandwidth networking, will enable scientists to pool their resources together in cracking the intricate web of connections that underlie the development of these disorders.

ASU computer engineers are operating the Saguaro 2 supercomputer, which boast 12 terabytes of RAM, 700 nodes and about 5,000 processors. One of its uses is for processing data sent back by the Lunar Reconnaissance Orbiter (LRO).

With the aid of this machine, the 212th most powerful computer in the world, HPCI experts are sending voluminous amounts of information to TGen researchers.

Using the new link, the joint team believes that it can analyze as much as a trillion bits of DNA information in only a fraction of the time that was needed before the link was constructed.

Using information obtained from such projects, biomedical researchers may soon become able to browse through experiment data more quickly, which would enable them to find causal links faster.

Even if they don't discover anything of use in one study, at least they wouldn't have wasted years going through the information by using conventional methods.