Offers ability to process analytics on large graphs at a scale and performance level that previously only one-of-a-kind supercomputers could perform
Lucata, provider of a next generation computing architectural extension to Intel technology for high performance, massively scalable graph analytics processing, announced it launched the new Lucata Pathfinder server for large scale production applications. Leveraging the performance and scalability breakthroughs of Pathfinder, banks can identify fraudulent credit card applications and account use in real-time, dramatically reducing the more than $149B in annual debit and credit card losses they now absorb or pass on to consumers.
Cloud ready and x86 compatible, the new Pathfinder is the only commercially available solution that affordably fills the gap between the performance and scalability of conventional servers and the capabilities of supercomputers for Big Data graph analytics. A single rack of Pathfinder chassis provides the same graph analytics processing power as 16 racks of Intel® Xeon® servers while consuming 1/10th the power. Massively scalable, up to 8,000 Pathfinder chassis can be combined without clustering software.
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Pathfinder leverages patented Migrating Thread technology that moves compute threads to the data rather than data to the processors. This results in orders-of-magnitude less network traffic and up to 60x higher processor and memory utilization rates over conventional computing.
The price-performance of Pathfinder enables organizations to implement a broad range of high-value use cases at a speed and scale that was previously too expensive, too slow, or simply impossible to consider with current server technology. Financial fraud detection, cybersecurity threat detection, logistics and supply chain management, computational pharmaceutical modeling, retail recommendation engines, and more can all experience step function improvements in graph analytics performance and scale, which can reshape the economics of multiple industries.
“The Intel Xeon-based frontend makes it easy to integrate Pathfinder into any datacenter. The massive scalability and low operating costs make Pathfinder a major step forward in the technology available for running analytics on large graph databases,” said Marty Deneroff, Lucata COO. “The performance, scalability and operating cost advantages of Pathfinder demonstrate the orders-of-magnitude increase in performance and scalability made possible by our patented Migrating Thread technology.”
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“We envision a future when we offer the next generation of Pathfinder, which will provide a further 10x improvement in performance and a 16x improvement in memory scalability while reducing production costs. It will increase the performance of graph analytics by more than double what the largest present-day supercomputers can deliver at a fraction of their cost,” said Michael Maulick, Lucata CEO. “We are also developing, under a National Science Foundation SBIR grant, support for simultaneous graph database query and update capabilities.”
Pathfinder enables organizations to leverage massive pools of physical memory to accelerate and scale graph analytics and AI and ML model training by orders of magnitude beyond the capabilities of conventional computing approaches. The solution enables high-performance exascale graph analytics, including exhaustive breadth-first search (BFS), on unpruned, unsharded massive graph databases. Lucata can be used with open source or commercial graph software or with custom-written graph solutions that leverage LAGraph, GraphBLAS, or the Lucata library of algorithms, enabling organizations to use their existing software to uncover much deeper connections within much larger graphs than possible today. These unique capabilities allow organizations to reimagine the potential of graph analytics, AI and ML and address intractable challenges in fraud detection, cybersecurity, blockchain, risk assessment, healthcare and many other fields. Lucata delivers extreme high performance for a broad range of common Big Data computing use cases which cannot be cost-effectively addressed with conventional computer architectures.
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