For machine learning applications at scale, DDN delivers up to 40x faster performance than competitive Enterprise scale out NAS and up to 6x faster performance than Enterprise SAN solutions, while providing faster results against more types of data using a wide variety of techniques. It also allows machine learning and deep learning programmes to start small for proof of concept and scale to production-level performance and petabytes per rack with no additional architecting required.
"The high performance and flexible sizing of DDN systems make them ideal for large-scale machine learning architectures", stated Joel Zysman, director of advanced computing at the Center for Computational Science at the University of Miami. "With DDN, we can manage all our different applications from one centrally located storage array, which gives us both the speed we need and the ability to share information effectively. Plus, for our industrial partnership projects that each rely on massive amounts of instrument data in areas like smart cities and autonomous vehicles, DDN enables us to do mass transactions on a scale never before deemed possible. These levels of speed and capacity are capabilities that other providers simply can't match."
With storage appliances that can start at a few hundred terabytes and grow to ~10 PB in a single rack, DDN's machine learning customers can scale from test bed to production ramp and beyond in a single platform. DDN solutions are enabling customers to leverage machine learning applications to speed results and improve competitiveness, profitability, customer service, business intelligence and research effectiveness, including:
DDN storage allows machine learning algorithms to run faster and to include more data than any other system in the market, which enables researchers to accelerate algorithm testing, decrease development/refinement times and ultimately decrease time to market for the "learned" results - a significant advantage in today's competitive markets.
"The uniqueness of DDN's architecture enables the University of Miami to save data being generated constantly from literally millions of sensors to address the entire storage needs for a smart city with up to 15,000 residents", Joel Zysman added. "Equally impressive, we can do all that without impacting our other research, computations and simulations that are going on at the same time."
As huge amounts of processing power and large data repositories have become more affordable, a rich environment for the advancement of machine learning and deep learning has emerged. Machine learning applications are being created and implemented across a wide range of processes, replacing or improving human input, and addressing problems that previously were not undertaken because of the sheer volume of the data.
"To be successful, machine learning programmes need to think big from the start", stated Laura Shepard, senior director of product marketing at DDN. "Prototypes of programs that start by using mid-range enterprise storage or by adding drives to servers often find that these approaches are not sustainable when they need to ramp to production. With DDN, customers can transition easily with a single high-performance platform that scales massively. Because of this, DDN is experiencing tremendous demand from both research and enterprise organisations looking for high-performance storage solutions to support machine learning applications."