Earlier, we discussed the challenges that plague big data initiatives, and why there needs to be a dramatic shift in the industry and technology to meet the real-time needs of today’s businesses. X86 architectures, can no longer keep up, and FPGA systems are moving forward as the solution to data analytics challenges.

FPGA-accelerated systems have several benefits, but three main benefits that data scientists, business analysts, and other data systems specialists care about are performance, ease-of-use, and lower total cost of ownership.

  • Performance is the main benefit most organizations care about. For targeted analytics applications, FPGA-based systems can easily achieve performance numbers that are two orders of magnitude or more when compared to servers that are typically found in today’s datacenters.
  • Ease-of-use: Once not associated with FPGAs, ease-of-use is becoming one of the most important drivers of FPGA use. When properly architected and used with open APIs (like the Ryft ONE and the Ryft Open API Library, respectively), what was once a complex hardware programming exercise is now abstracted away with a simple and intuitive interface. Users don’t even need to know that there are FPGAs working behind the scenes. In fact, they don’t even have to know how to spell FPGA.
  • Total cost of ownership (TCO) is another major consideration—especially when budgets are still being scrutinized and the return on investment for big data implementations must be proved quickly. Fortunately, the mathematics involved with TCO become very simple for most FPGA-enabled systems; because a single FPGA-based system can handle the workload of 100 or more traditional clustered nodes for specific applications, the TCO benefits of FPGAs quickly emerge. If an organization has to configure and maintain a single 1U box vs. 100 traditional servers, then the real-estate (rack-space) costs, power costs, cabling costs, networking costs, IT personnel costs, ongoing software maintenance costs, and so on add up very quickly. The dramatic TCO savings that result from a 1U box solution are dramatic, and clear.

There is no stemming the flow of data; in fact, it continues to grow exponentially. Social media and the Internet of Things (IoT) are just two of a myriad of examples of ever-growing data streams, and these streams contain important and valuable business information for those organizations that are capable of processing them in real time. Organizations want answers and they want them immediately, not next week or next month. However, current solutions haven’t allowed for fast, accurate data analysis, using all of the data available. The result has been that decision makers have been forced to act on outdated, inaccurate, and/or incomplete data, which means that the decisions are compromised, and this can harm business.

There’s an old adage “garbage in, garbage out.” If we expect our business decision makers to make informed decisions, then we sure ought to provide them with the best possible tools to do so. X86-based systems don’t meet performance needs, are too cumbersome to deploy, program, and maintain, and at scale they are too costly. That means that, as they stand today, they aren’t meeting business needs.

For these reasons, adoption of FPGA for big data analytics is moving fast, and FPGAs are quite the popular topic lately. I recently contributed to Dataconomy talking about the benefits and future of FPGA-based systems. To learn more, visit “Can FPGA-accelerated Systems Eliminate x86 Bottlenecks and Speed Value From Data?

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