A new supercomputer, the Chinese Tianhe-1A, was unveiled at HPC 2010 in China and has set a new performance benchmark at 2.507 petaflops. It has become the fastest in the world and has taken the title from the Cray XT5, made in US, which had a performance of 1.756 petaflops. The new supercomputer uses 7,168 NVIDIA Tesla M2050 GPUs and 14,336 CPUs combined, which allow much more operations to be performed in parallel.

NVIDIA said that its GPUs power now two of the first three fastest supercomputers in the world. NVIDIA CUDA-based Tesla GPU is made specifically for applications where deep and massively parallel computations are required. They help in tsunami and hurricane modelling, cancer research, drug discovery, car design and many other very complex calculations.


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A new supercomputer built in China has claimed the world's top spot from the U.S. The Tianhe-1A beat out the previous record holder by 40 percent.

China's Tianhe-1A supercomputer has been confirmed as the most powerful in the world.

Located at China's National Supercomputer Centre in Tianjin much of the machine's processing power comes from chips more typically found in graphics cards. It is expected to be doing simulations to help Chinese weather forecasts and to help with work to locate undersea oil fields.

Of the top four machines on the list, three are now largely built around graphics processors. By contrast the US Jaguar supercomputer that Tianhe-1A has pushed into second place is built around more traditional CPUs typically used in desktop computers.



China's Tianhe-1A crowned supercomputer king

He [Jack Dongarra, director of the Innovative Computing Laboratory at the University of Tennessee at Knoxville] recently helped create a new speed test, one that considers many variables, called the HPC challenge benchmark. It measures things like how fast data can be stored, retrieved, and moved within the computer. But the test is not as popular as the method used by the Top 500 list, although the NSF and other U.S. agencies have used it, he says. It doesn't produce a ranking, since there is no way to fairly reduce the variables to one number for comparative purposes—in the same way Consumer Reports doesn't produce a single ranking for all cars, he says. "I wouldn't want to make policy based on one number."

In November, still another supercomputer ranking was unveiled, at a conference in New Orleans. This one, called Graph500, does produce a ranking, but it is based on how fast supercomputers solve complex problems related to randomly generated graphs, rather than on the simpler computation of the Top 500. Some computers that had ranked well on the Top 500 ran the Graph500, but their operators refused to announce the scores, most likely because they fared less well.

Does that mean China may not actually be ahead in the supercomputing race? Hard to tell, unless its computers participate in alternative challenges to Top 500. One scientist here speculated that the Chinese computer may have been designed simply to do well on that one test.

Mr. Dongarra says he saw the Chinese machine when he visited China's National Supercomputing Center, about two hours' drive from Beijing. "It's state-of-the-art in many ways," he says, noting that he was impressed by the unique interconnections among processors that researchers there had developed.

Mr. Reed, of Microsoft Research, says he, too, has been impressed by China's efforts. "I used to say that in the high-performance computing race, the U.S. was laps ahead," he says. "Now it's steps ahead."


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BGI Speeds Genome Analysis with GPUs


The data deluge in the life sciences is no where more acute than at Chinese genomics powerhouse BGI, which probably sequences more DNA than any other organization in the world. To turn that data into something meaningful for genomic researchers, the institute has begun to employ GPU-accelerated HPC to greatly reduce processing times. In doing so, BGI was able to increase computational throughput by an order of magnitude or more.

The data deluge problem stems from an imbalance between the DNA sequencing technology and computer technology. According to Dr. Wang, using second-generation sequencing machines, genomes can now be mapped 50,000 times faster than just a decade ago. The technology on track to increase approximately 10-fold every 18 months. That is 5 times the rate of Moore's Law, and therein lies the problem.

To estimate the MAF results for even a modest size population is very computationally expensive. For example, using just 1,024 human genomes, it would take 10 years on a single CPU and 0.5 years on a single GPU to generate the MAF results. To make such an application run feasible, one would need thousands or CPUs or hundreds of GPUs.

To achieve an MAF population estimation, BGI teamed up with the Tianjin Supercomputing Center to use their GPU-equipped Tianhe-1A, the top supercomputer in China, and the second most powerful system in the world. Using 256 of the machine's 7,168 GPUs, and employing MPI to communicate between the nodes, the MAF run for that 1,024 population took just 13 hours.


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