Supercomputing performance is getting a new
measurement with the Graph500 executive committee’s announcement of
specifications for a more representative way to rate the large-scale data
analytics at the heart of high-performance computing.
An international team that includes Sandia
National Laboratories announced the single-source shortest-path specification
to assess computing performance at the International Supercomputing Conference
in Hamburg, Germany.
The latest benchmark “highlights the importance
of new systems that can find the proverbial needle in the haystack of data,”
said Graph500 executive committee member David A. Bader, a professor in the School of Computational Science and Engineering
and executive director of High-Performance Computing at the Georgia Institute
The new specification will measure the
closest distance between two things, said Sandia National Laboratories
researcher Richard Murphy, who heads the executive committee. For example, it
would seek the smallest number of people between two people chosen randomly in
the professional network LinkedIn, finding the fewest friend of a friend of a
friend links between them, he said.
Graph500 already gauges two computational
techniques, called kernels: a large graph that links huge numbers of
participants and a parallel search of that graph. The first two kernels were
relatively easy problems; this third one is harder, Murphy said. Once it’s been
tested, the next kernel will be harder still, he said.
The rankings are oriented toward enormous
graph-based data problems, a core part of most analytics workloads. Graph500
rates machines on their ability to solve complex problems that have seemingly
infinite numbers of components, rather than ranking machines on how fast they
solve those problems.
Big data problems represent a $270 billion
market and are increasingly important for businesses such as Google, Facebook,
and LexisNexis, Murphy said.
Large data problems are especially
important in cybersecurity, medical informatics, data enrichment, social
networks, and symbolic networks. Last year, the Obama administration announced
a push to develop better big data systems.
Problems that require enormously complex
graphs include correlating medical records of millions of patients, analyzing
ever-growing numbers of electronically related participants in social media and
dealing with symbolic networks, such as tracking tens of thousands of shipping
containers of goods roaming the world’s oceans.
Medical-related data alone could potentially
overwhelm all of today’s high-performance computing, Murphy said.
Graph500’s steering committee is made up of
more than 30 international experts in high-performance computing who work on
what benchmarks supercomputers should meet in the future. The executive
committee, which implements changes in the benchmark, includes Sandia, Argonne
National Laboratory, Georgia Institute of Technology, and Indiana University.
Bader said emerging applications in
healthcare informatics, social network analysis, web science and detecting
anomalies in financial transactions “require a new breed of data-intensive
supercomputers that can make sense of massive amounts of information.”
But performance can’t be improved without a
meaningful benchmark, Murphy said.
“The whole goal is to spur industry to do
something harder” as they jockey for top rankings, he said.
“If there’s a change in the list over time—and
there should be—it’s a big deal,” he added.
Murphy sees Graph500 as a complementary
performance yardstick to the well-known Top 500 rankings of supercomputer
performance, based on speed processing the Linpack code. Nine computers made
the first Graph500 list in November 2010; by last November, the number had
grown to 50. Its fourth list, released at the conference in Germany, ranked
88. Rankings are released twice a year at the Supercomputing Conference in
November and the International Supercomputing Conference in June.
“A machine on the top of this list may
analyze huge quantities of data to provide better and more personalized health
care decisions, improve weather and climate prediction, improve our
cybersecurity, and better integrate our online social networks with our
personal lives,” Bader said.
Source: Sandia National Laboratories