- Google I/O 2013's Coolest Products and Services
- 10 Star Trek Technologies That are Almost Here
- 19 Generations of Computer Programmers
- 25 Must-Have Technologies for SMBs
So while a new Cray supercomputer took first place on theTop500, it was another machine, Lawrence Livermore National Laboratory's Sequoia, that proved to be the most adept at processing data intensive workloads on the Graph 500.
IN PICTURES: The 10 most powerful supercomputers on the planet
Such differences in ranking between the two scales highlight the changing ways in which the world's most powerful supercomputers are being used. An increasing number of high performance computing (HPC) machines are being put to work on data analysis, rather than the traditional duties of modeling and simulation.
"I look around the exhibit floor [of the Supercomputing 2012 conference], and I'm hard-pressed to find a booth that is not doing big data or analytics. Everyone has recognized that data is a new workload for HPC," said David Bader, a computational science professor at the Georgia Institute of Technology who helps oversee the Graph 500.
The Graph 500 was created to chart how well the world's largest computers handle such data intensive workloads. The latest edition of the list was released at the SC12 supercomputing conference, being held this week in Salt Lake City.
In a nutshell, the Graph 500 benchmark looks at "how fast [a system] can trace through random memory addresses," Bader said. With data intensive workloads, "the bottleneck in the machine is often your memory bandwidth rather than your peak floating point processing rate," he added.
The approach is markedly different than Top500. The well-known Top500 list relies on the Linpack benchmark, which was created in 1974. Linpack measures how effectively a supercomputer executes floating point operations, which are used for mathematically intensive computations such as weather modeling or other three dimensional simulations.
The Graph 500, in contrast, places greater emphasis on how well a computer can search through a large data set. "Big data has a lot of irregular and unstructured data sets, irregular accesses to memory, and much more reliance on memory bandwidth and memory transactions than on floating point performance," Bader said.
For the Graph 500 benchmark, the supercomputer is given a large set of data, called a graph. A graph is an interconnected set of data, such as a group of connected friends on a social network like Facebook. A graph consists of a set of vertices and edges, and in the social media context a vertex would be a person and the edge that person's connection to another person. Some vertices have many connections while many others have fewer. The computer is given a single vertex and is timed on how quickly it discovers all the other vertices in a graph, namely by following the edges.
Currently, IBM's BlueGene/Q systems dominate this edition of the Graph 500. Nine out of the top 10 systems on the list are BlueGene/Q models -- compared to four BlueGene/Q systems on the November 2011 compilation. For Bader, this is proof that IBM is becoming more sensitive to current data processing needs. IBM's previous BlueGene system, BlueGene/L, was geared more towards floating point operations, and does not score as highly on the list.