Lifecycle Strategies Can Dictate System Selection for Product Stability and Support.
Choosing a GPU is largely influenced by how an Original Equipment Manufacturer (OEM) wants to manage its graphics-heavy application. Cards that fall within the consumer or gamer category, for example the NVIDIA GeForce®, change every 12 months or less. End-of-life (EOL) notices are not standard in this type of product environment, so OEMs must remain reactive to frequent change. If this is suited to the OEM’s operations and product lifecycle, they can win with some of the highest GPU performance at the lowest cost.
However, if longevity and greater stability are required – often a factor in longer deployments of performance-critical systems – the OEM may be wiser to invest in a more costly three to five-year GPU product. Costs per GPU are greater in this scenario, but actually may be more cost effective once the GPU decision incorporates support and ongoing change needs as part of product development costs. Support needs and ongoing changes are anticipated and planned, eliminating costly surprises, and creating a clear understanding of price vs performance.
The potential of GPU computing clearly plays an important role in industrial embedded computing. Where the industry once saw a doubling of CPU performance about every 18 months, that pace has tapered off. GPU performance, however, continues to grow exponentially; NVIDIA® GPU computing has given the industry a path forward, providing a 1000x acceleration in GPU vs CPU performance anticipated by 2025. This has impact on a broad range of graphics-heavy applications, such as industrial training and initiatives like the military’s Synthetic Training Environment and its goal of providing immersive game-like instruction. Life sciences and healthcare are clamoring for augmented/virtual reality (AR/VR) capabilities at the point-of-need. And factory vision systems and video analytics are tapping powerful graphics performance that can handle tasks such as data analysis at the edge.
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