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Fuzzy Logix and Kinetica to Provide Enhanced GPU-Accelerated In-Database Analytics

May 8, 2017 No Comments

SOURCE: Fuzzy Logic

San Jose, CA – May 8, 2017NVIDIA GPU Technology Conference – Fuzzy Logix, Inc., provider of high performance, in-database analytics, and Kinetica, provider of the fastest GPU-accelerated database, today announced a partnership to offer a joint solution that will allow customers of both companies to leverage high performing advanced analytics with acceleration of 100-500x on 1/10th the hardware over CPU-only based solutions. The joint solution will initially be targeted at the most time-sensitive and compute-heavy applications in financial services, retail, and healthcare, where speed and scale are critical for real-time data insights and competitive advantage.

By combining technologies, Kinetica’s in-database analytics capabilities will be extended by hundreds of additional GPU-accelerated and highly-parallelized, machine learning and predictive analytics algorithms from Fuzzy Logix. At the same time, those analytic functions will now also be able to take full advantage of Kinetica’s distributed GPU pipeline via its User Defined Functions (UDFs).

“Leveraging GPUs for analytical workloads is on the rise, particularly among financial services, life sciences and retail organizations that often deal in extremely large data volumes with high scaling and real-time processing requirements,” said Jim Curtis, Senior Analyst, Data Platforms & Analytics at 451 Research. “As such, the partnership between Kinetica and Fuzzy Logix should provide these additional analytical capabilities.”

Analytics and data science teams using the joint solution will have access to a rich library of algorithms on a SQL-compliant, in-memory database that leverages the GPU’s massive parallelization and brute force compute for real-time analytics. Use cases include computing portfolio risk management, options and equity pricing, product-based inventory optimization, next-likely purchase, prescribing habits of physicians and care gap analysis.

Under the terms of the agreement, the joint solution will be available as a premium offering from Kinetica. The combined offering will make it easy for customers to acquire and get support from a single provider, whether deployed in on-premises, cloud, or a hybrid architecture.  The first set of algorithms will be available by the third quarter of 2017.

“Faced with a data deluge, it’s essential for companies to look beyond CPU technology to discover what GPUs can deliver for extremely compute-intensive problems,” said Jim McHugh, Vice President and General Manager of NVIDIA. “Fuzzy Logix and Kinetica are building on each other’s strengths in accelerating time-to-market for a much-needed solution.”

“I’m pleased to announce the launch of this joint solution, which promises to be the first-of-its-kind in meeting an unmet market need,” said Partha Sen, CEO of Fuzzy Logix. “Fuzzy Logix has already managed to achieve 100-500x performance improvement using NVIDIA GPUs, and with this Kinetica partnership, we can now bring the merits of these GPU gains and our in-database technology to the most challenging problems with which the industry continues to struggle.”

“Since 2012, Fuzzy Logix has had GPU-accelerated analytics, but no GPU-accelerated database to take full advantage of their advancements at scale.  We are very excited that Fuzzy Logix has chosen the Kinetica GPU database as the home for their extensive library of GPU-accelerated analytics,” said Amit Vij, CEO and Co-founder of Kinetica.  “We are pleased to extend our offering of in-database analytics with the leading pioneer in this space, Fuzzy Logix, and bring this joint solution to the customer base of both companies.”

Fuzzy Logix and Kinetica are presenting and exhibiting at NVIDIA’s GPU Technology Conference in San Jose, CA May 8-11. Executives will also be available to explain the architecture and how it will help accelerate use cases at Kinetica booth 431 and Fuzzy Logix booth 200. Fuzzy Logix’s CEO, Partha Sen will present: Faster than the Blink of an Eye: High Performance Analytics using NVIDIA GPUs, on Monday, May 8th at 2:30pm.Kinetica’sChris Prendergast and Woody Christy will presentReal-Time Analytics Powered by GPU-Accelerated Databases, on Monday, May 8th at 1:30pm, and Mark Brooks will present, How to Achieve Real-Time Analytics on a Data Lake Using GPUs, on Tuesday, May 9th at 9am.

About Fuzzy Logix

Fuzzy Logix accelerates analytics. The company is a growing team of data scientists, programmers and industry experts that have deep expertise in advanced analytics, and are joined by a common purpose: To find tough problems that current technology cannot solve well, and accelerate those solutions by factors of 10 to a 100. Over the years, Fuzzy Logix has created a large library of over 700 machine learning and predictive analytics functions that we can apply to discover interesting patterns in data and make this science available to anyone within the organization. Fuzzy Logix is about making advanced analytics simple to apply and learn, pervasive within the entire organization, and an order of magnitude faster than the state of the art. Find out more at www.fuzzylogix.com.

About Kinetica

Kinetica is a GPU-accelerated, in-memory analytics database that delivers truly real-time response to queries on large, complex and streaming data sets: 100x faster performance at 1/10 of the hardware of traditional databases. Kinetica’s fully distributed architecture and simplified data structures lead to more predictable scale out on industry-standard hardware. In-database analytics via user-defined functions (UDFs) open the way for converged AI and BI workloads accelerated by GPUs. Kinetica comes with native geospatial and visualization pipeline for interactive location-based analytics. Organizations use Kinetica to simultaneously ingest, explore, analyze and visualize data within milliseconds to make critical decisions and find efficiencies, lower cost, generate new revenue, and improve customer experience. Learn more at kinetica.com.

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