Nvidia announces supercomputer deployment

 
  • First deployment of DGX-1 in Asia Pacific is in Australia
  • System equivalent to 250 regular CPU-based servers
Nvidia announces supercomputer deployment
 
NVIDIA has announced Asia Pacific’s first deployment of two DGX-1 deep learning supercomputers at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), the federal government agency for scientific research in Australia. It is installed in CSIRO’s Canberra data centre. The announcement was made at the company's GTCx Australia conference this week.
 
According to Nvidia, the DGX-1 is the world’s first deep learning supercomputer to meet the computing demands of artificial intelligence. It enables researchers and data scientists to easily harness the power of GPU-accelerated computing to create a new class of computers that learn, see and perceive the world as humans do.
 
Providing the throughput equivalent to 250 conventional servers in a single box, the supercomputer delivers the highest levels of computing power to drive next-generation AI applications, allowing researchers to dramatically reduce the time to train larger, more sophisticated deep neural networks.
 
Nvidia announces supercomputer deployment
 
Built on the company's Tesla P100 GPUs that use the latest Pascal GPU architecture, the DGX-1 supercomputers enable CSIRO scientists to expand research in areas such as medical image analysis, nano-material modeling, genome analysis, radioastronomy, earth observation and analysis.

 

“There is a growing interest from research groups to adopt machine learning techniques to support their projects,” said Angus Macoustra, executive manager for Scientific Computing at CSIRO. “CSIRO research projects are already using the DGX-1 systems, and in time, it is expected that machine learning will have applicability across all our areas of research and be used by hundreds of researchers.”
 
CSIRO research is built upon making sense of data, to identify patterns or things of interest among the “noise” of the growing deluge of data. These computers have the capacity to see, learn and react to help make sense of the data (images, sound and text) used across many areas of CSIRO research.
 
A project in CSIRO’s Health and Biosecurity business will use the supercomputers to process massive amounts of complex, multi-layered and dynamic data to help understand the effect of environment on disease. It will explore the various influences on disease proteins – genetic, epi-genetic and environmental, to better understand disease across multiple generations, helping to identify protein markers and predict, prevent and treat disease.
 
Deep learning techniques allow machines to sufficiently understand the complexity of the underlying biological relationship and hence learn and predict which environmental effects can contribute towards disease.
 
The latest generation GPU technology in CSIRO’s deep learning supercomputer enables the use of massively parallel multithreading, which will greatly speed up the most time consuming part of the model, enabling a more exhaustive search and development of an optimal model – which should lead to more effective prevention and treatment for disease.
 
“Supercomputers are an integral part of the advanced information and communications technologies that enable CSIRO science to solve real issues,” said Macoustra. “It’s exciting to now have these latest generation machines available and the uplift to research productivity they’ll deliver.”

“Offering unprecedented performance in a compact package, the DGX-1 gives CSIRO researchers the power and capabilities needed to solve complex problems – and we can’t wait to see the scientific discoveries that result,” said Raymond Teh, vice president of sales and marketing for Asia Pacific at Nvidia.
 
Nvidia worked with high performance computing provider Xenon Systems to install the DGX-1 supercomputers at CSIRO.
 
The DGX-1 supercomputer was shown off at the GTCx Australia conference in Melbourne this week. Read our full report on the event here.
 
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