Press Release (ePRNews.com) - Mountain View, CA - Dec 24, 2016 - Maxeler Technologies Inc., a leading provider of Dataflow computing platforms and a member of the Hitachi Technology Alliance Program, announced today its collaboration with Hitachi Data Systems to address the demands of new financial legislation and compliance regulations with a high-performance compliance capture and analytics solution.
The Maxeler-Hitachi Data Systems solution combines Maxeler Technologies Data Flow Engine (DFE) technology with high-reliability Hitachi NAS (HNAS) and Hitachi Unified Storage (HUS) for performance-matched data acquisition and storage and Pentaho Data Integration and Business Analytics software for real-time and forensic analytics and reporting dashboards. Offering end-to-end nanosecond packet capture, deep memory buffering and timestamping at line rate, the solution helps financial institutions achieve the high-frequency, transaction-related record keeping mandated in European Union MiFID II and US Dodd-Frank regulations.
With Maxeler’s customizable dataflow engine (DFE) technology, data is streamed from memory onto a chip where the data moves directly from one functional unit to another, without being written to off-chip memory until the entire process is complete. With a DFE, there is no need for instructions because the DFE itself represents the computation. As a result, there is also no need for memory decode logic, branch prediction or out of order scheduling, allowing the chip to dedicate all its resources to performing computation. As such, dataflow computing can accelerate and run different applications orders of magnitude faster than conventional CPUs. The Maxeler DFE technology captures, analyzes and stores streaming IP traffic at full 10Gb/s line speed.
Demonstrated at AWS re:Invent at the end of November, the Maxeler-Hitachi Data Systems solution leverages the new Amazon AWS F1 instance, the first cloud instance with programmable hardware for field-programmable gate array (FPGA) application acceleration, which simplifies and speeds the deployment of the application to more than a million AWS customers with just a few clicks.
“The collaboration between Maxeler Technologies and Hitachi Data Systems can solve a serious problem for financial institutions across the globe. The combination of Hitachi data acquisition, storage and analytics with high-speed dataflow computing from Maxeler Dataflow Engines and the new AWS F1 instance represents a very significant breakthrough for financial organizations who need maximum performance without unreasonable costs or timelines,” says Geoff Smailes, Chairman of the Maxeler Technologies Board of Directors, who worked previously at a Tier 1 financial institution.
“As financial institutions acknowledge the importance of data as an asset and as they continue to deploy sophisticated analytics to realize the benefit of that asset, we will begin to see some achieve competitive advantages in this fast-paced marketplace. Of course, these organizations also face continued regulatory scrutiny and disruptive changes to technology that can pose challenges,” said Michael Hay, Vice President and Chief Engineer at Hitachi Data Systems. “Together with our partner Maxeler Technologies, Hitachi Data Systems can help our customers address business demands, stay compliant and transform data into information, insight and opportunities to win.”
About Maxeler Technologies Inc.
Maxeler Technologies, Inc. pioneers Maximum Performance Computing with appliances, software and consulting services to solve our clients’ tough computing problems. At Maxeler, we are passionate about performance and providing competitive advantage. At the core, Maxeler utilizes Multiscale Dataflow technology which is available on-premise as well as on the public cloud for elasticity, reliability and business continuity. Multiscale Dataflow Computing enables organizations to speed up processing times by 20-50x, with over 90 percent reduction in energy usage and over 95 percent reduction in data center space. http://www.maxeler.com/
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