Chapter 8. Getting Started
GPU acceleration delivers both performance and price advantages over configurations containing only CPUs in most database and data analytics applications.
From a performance perspective, GPU acceleration makes it possible to ingest, analyze, and visualize large, complex, and streaming data in real time. In both benchmark tests and real-world applications, GPU-accelerated solutions have proven their ability to ingest billions of streaming records per minute and perform complex calculations and visualizations in mere milliseconds. Such an unprecedented level of performance will help make even the most sophisticated applications, including cognitive computing, a practical reality. And the ability to scale up or out enables performance to be increased incrementally and predictably—and affordably—as needed.
From a purely financial perspective, GPU acceleration is equally impressive. The GPU’s massively parallel processing can deliver performance equivalent to a CPU-only configuration at one-tenth the hardware cost, and one-twentieth the power and cooling costs. The US Army’s Intelligence & Security Command (INSCOM) unit, for example, was able to replace a cluster of 42 servers with a single GPU-accelerated server in an application with more than 200 sources of streaming data that produce more than 100 billion records per day.
But of equal importance is that the GPU’s performance and price/performance advantages are now within reach of any organization. Open designs make it easy to incorporate GPU-based solutions into virtually any existing data architecture, where they can integrate with both open source and commercial data analytics frameworks.
With purpose-built GPU solutions, the potential gain can quite literally be without the pain normally associated with the techniques traditionally used to achieve satisfactory performance. This means no more need for indexing or redefining schemas or tuning/tweaking algorithms, and no more need to ever again predetermine queries in order to be able to ingest and analyze data in real time, regardless of how the organization’s data analytics requirements might change over time.
As with anything new, of course, it is best to research your options and choose a solution that can meet all of your analytical needs, scale as you require, and, most important, be purpose-built to take full advantage of the GPU. So start with a pilot project to gain familiarity with the technology, because you will not be able to fully appreciate the raw power and potential of a GPU-accelerated database until you experience it for yourself.