Finding Meaning Among Billions of Galaxies (Intel Labs@SC12)

As we speak, scientists are racing to apply Big Data computation techniques to help answer some of the most fundamental questions about the origin, composition, and evolution of the universe. A large part of this is the quest to understand dark matter and dark energy. These are thought to comprise as much as 96% of the universe but are undetectable through normal means – hence the term ‘dark’.

The answer may lie in Big Data — the ability to simultaneously correlate the movements of vast numbers of galaxies and find patterns that unlock these darkest of secrets in the universe. The challenge is keeping up with the data. In recent decades, the observed and simulated datasets have grown from a few dozen objects to billions. With the advent of bigger and faster telescopes there is no end in sight for the exploding data. Performing the necessary algorithm (called the Two Point Correlation Function or TPCF) today on a billion galaxies would take a single processor 50 years to analyze — and even today’s supercomputers are hard pressed to keep up. The computational requirements are expected to grow further, well into the domain of Exascale computing. This is the subject of an ACM Gordon Bell Prize – nominated SC12 paper from Intel this week.

Intel Labs, in collaboration with Lawrence Berkeley National Laboratory (LBNL) and the University of California, Berkeley (and in support of their ISAAC project) has demonstrated new techniques to significantly accelerate the computation of TPCF on these immense datasets and reduce both the cost and energy of the quest of cosmic understanding. This approach is comprised of three components: 1) the ability to effectively distribute and manage the work across tens of thousands of Intel® Xeon® compute cores, 2) more efficient use of the SIMD (single compute on multiple data) capabilities within each Intel Xeon core, and 3) more efficient communications among the compute nodes.

This technique was tested on a 1.7 billion object dataset (provided via a collaboration between LBNL and the University of Sussex) using Lawrence Livermore National Laboratory’s Zin computer, a Petascale-class machine with 1600 nodes each containing two Intel Xeon processors. The calculation was completed in just over five hours — more than 35 times faster than previous approaches (see notices below). This means scientists will be able to use this technique to complete experiments in a single day rather than weeks. In addition, the experiment demonstrated an 11x improvement in cost efficiency (measured in flops/$), making these experiments more practical and affordable.

More recently on the Texas Advanced Computing Center’s Stampede cluster, a Petascale computer using Intel® Xeon Phi™ Coprocessors (launched this week), Intel Labs achieved a further speedup in run-time of 3.2 X on each node in comparison to the results above (see notices below).

This technique provides a path to computing even larger datasets into the Exascale domain, where new answers to many cosmological questions may be found within the next decade.

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Sean Koehl

About Sean Koehl

Sean Koehl is a Vision Strategist for Intel Labs, the global research arm of Intel Corporation. He is responsible for crafting visions of how Intel R&D efforts could impact daily life in the future. He leverages insights from Intel’s technologists, social scientists, futurists, and business strategists to articulate how technology innovations and new user experiences could improve lives and society. Sean received a bachelor’s degree in Physics from Purdue University and launched his career at Intel in 1998. He has worn many hats in his career including those of an engineer, evangelist, writer, creative director, spokesperson, and strategist. He has led a variety of projects and events, authored numerous technology publications and blogs, and holds seven patents. He is based at Intel’s headquarters in Santa Clara, California.

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