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October 31, 2012
SANTA CLARA, CA, Oct. 31 – NVIDIA Tesla GPU accelerators are enabling Life Technologies Corporation's new Ion Proton System to accelerate primary genome-sequence analysis – the computation that generates DNA base pairs – by over 16 times. This will dramatically reduce the cost to sequence an entire human genome from about $1 billion a decade ago to $1,000 in the near future.
"GPU acceleration and other advanced Ion Proton features enable every laboratory in the world to take advantage of human genome sequencing quickly and easily, without costly IT investments," said Alan Williams, vice president of software and engineering in the Ion Torrent unit at Life Technologies Corporation. "By democratizing genome sequencing, we expect to see an unprecedented wave of innovation in life sciences and the advancement of clinical research."
The Ion Proton System's technology builds on the rapid advances in increasing throughput, accuracy and read-length achieved with the Life Technologies Ion Personal Genome Machine (PGM) Sequencer, which also uses GPUs to accelerate primary analysis. The Ion PGM sequencer was the first to decode the deadly 2011 E. coli bacteria outbreak in Germany because of its exceptional speed.
Setting new standards for performance, ease of use and affordability, the Ion Proton System enables researchers to rapidly go from multiplex sample sequencing to genome-scale sequencing on a single platform. At one-fifth the cost of light-based genome-scale sequencing systems, it can save researchers hundreds of thousands of dollars.
"GPU acceleration has become pervasive in all aspects of computing for life science applications and will enable research to push the envelope of scientific discovery," said Sumit Gupta, general manager of the Tesla accelerated computing business unit at NVIDIA. "The pace of research has fundamentally been accelerated by the use of GPUs for everything from gene sequencers and sequence analysis to molecular modeling and diagnostic imaging."
About NVIDIA Tesla GPUs
NVIDIA Tesla GPUs are massively parallel accelerators based on the NVIDIA CUDA parallel computing platform and programming model. Tesla GPUs are designed from the ground up for power-efficient, high performance computing, computational science, and supercomputing, delivering dramatically higher application acceleration for a range of scientific and commercial applications than a CPU-only approach.
NVIDIA awakened the world to computer graphics when it invented the GPU in 1999. Today, its processors power a broad range of products from smartphones to supercomputers. NVIDIA's mobile processors are used in cell phones, tablets and auto infotainment systems. PC gamers rely on GPUs to enjoy spectacularly immersive worlds. Professionals use them to create 3D graphics and visual effects in movies and to design everything from golf clubs to jumbo jets. And researchers utilize GPUs to advance the frontiers of science with high performance computing. The company has more than 5,000 patents issued, allowed or filed, including ones covering ideas essential to modern computing.
In quieter times, sounding the bell of funding big science with big systems tends to resonate further than when ears are already burning with sour economic and national security news. For exascale's future, however, the time could be ripe to instill some sense of urgency....
In a recent solicitation, the NSF laid out needs for furthering its scientific and engineering infrastructure with new tools to go beyond top performance, Having already delivered systems like Stampede and Blue Waters, they're turning an eye to solving data-intensive challenges. We spoke with the agency's Irene Qualters and Barry Schneider about..
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 22, 2013 |
At some point in the not-too-distant future, building powerful, miniature computing systems will be considered a hobby for high schoolers, just as robotics or even Lego-building are today. That could be made possible through recent advancements made with the Raspberry Pi computers.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 15, 2013 |
Supercomputers at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC) have worked on important computational problems such as collapse of the atomic state, the optimization of chemical catalysts, and now modeling popping bubbles.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/15/2013 | Bull | “50% of HPC users say their largest jobs scale to 120 cores or less.” How about yours? Are your codes ready to take advantage of today’s and tomorrow’s ultra-parallel HPC systems? Download this White Paper by Analysts Intersect360 Research to see what Bull and Intel’s Center for Excellence in Parallel Programming can do for your codes.
In this demonstration of SGI DMF ZeroWatt disk solution, Dr. Eng Lim Goh, SGI CTO, discusses a function of SGI DMF software to reduce costs and power consumption in an exascale (Big Data) storage datacenter.
The Cray CS300-AC cluster supercomputer offers energy efficient, air-cooled design based on modular, industry-standard platforms featuring the latest processor and network technologies and a wide range of datacenter cooling requirements.