a test case for big-data flood
Posted May 1, 2013
Big data extends far beyond its literal interpretation. It’s more than large volumes of information. It’s also complexity, including new classes of data that stretch the capabilities of existing computers.
In fact, analytical algorithms and computing infrastructures must rapidly evolve to keep pace with big data. “On the science side,” says David Brown, director of the Computational Research Division at Lawrence Berkeley National Laboratory, “it caught us somewhat by surprise.”
Detectors, for example, would improve and provide ever more data, Brown says, “but we didn’t realize that we might have to develop new computational infrastructure to get science out of that data.” (See sidebar, “Big data’s breadth.”)
Unfortunately, that reflects a common theme, Brown says. “People often neglect the computational infrastructure when designing things.” Consequently, some big data challenges in basic energy research – such as analyzing information from Berkeley Lab X-ray scattering experiments – must start virtually from zero.
Sources for scattering
A material’s crystalline structure scatters a beam of X-rays in a particular way, depending on the angle of the incident beam and the crystal structure’s orientation. This scattering of X-rays requires two components: the X-ray beam and detectors to create images of the scattered rays. The Advanced Light Source (ALS) at Berkeley Lab provides a wide selection of beams, ranging from infrared light to hard X-rays.
Researchers from all over the world use the facility’s beams, says Alexander Hexemer, ALS experimental systems staff scientist. Most of the work around the ring-shaped accelerator involves energy science – battery materials, fuel cells, organic photovoltaics and more. Scientists also come to study biological materials, such as bones to better understand osteoporosis.