Exploring the ROI Potential of GPU Supercomputing

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The growing prevalence of artificial intelligence and machine learning is putting heightened focus on the quantities of data that organizations have recently accumulated — as well as the value potential in this data.

GPU Supercomputing

Download the full report.

The recent explosion in big data, brought about in part of the arrival of the Internet of Things (IoT) — albeit potentially valuable for many enterprises — has made deriving and extracting business value from all of this data difficult.

That’s according to a new white paper from Penguin Computing. As processing such large quantities of data historically requires considerable processing power and time, companies looking to gain a competitive edge in their market are turning to tools like graphic processing units – or GPUs – to ramp up computing power.

In the past, this job was primarily given to expensive central processing unit (CPU) intensive infrastructure, but GPU supercomputing can often provide more efficient results, according to Penguin Computing.

GPUs in the past have often associated with video games, but the same technology that contributes to movie-quality gaming can be used to address today’s data processing challenges.

The new report explores in detail the ROI potential for GPU-accelerated computing, starting off with an analysis of ROI verses CPU-only computing. Although the modern CPU is well designed for general compute tasks, such as web surfing and word processing, many computing hardware vendors have been recently focused on increasing CPU speed — but this also means more power usage and more heat.

Enter GPUs.

GPU-accelerated computing occurs when a GPU is used in combination with a CPU, with the GPU handling as much of the parallel process application code as possible,” the report states.

This can accelerate some software by 100 times over a CPU alone, according to the report.

gpu supercomputing

The increased efficiencies of GPU computing will also likely lead the path for edge computing. (Photo: Shutterstock/By YIUCHEUNG)

Penguin Computing asserts that with the impending increase in the reliance on GPUs, early adopters will “enjoy greater computing power over time, but have a greater margin of difference over time than competitors who do not migrate to GPU-accelerated computing.”

The paper also outlines potential costs and cost avoidance. A GPU-based supercomputer will likely take up much less space than an equivalent CPU-based supercomputer performing the same functions. And Penguin pointed out that physical infrastructure costs, from power to staffing support for more racks, also have the potential to be cut down by adding GPUs into your supercomputing strategy.

As the coming improved networks enable a world of high-speed, low latency inference operations at the edge, the most powerful and power efficient platforms will naturally be selected for these applications.” — Penguin Computing

Ultimately, the increased efficiencies of GPU computing will also likely lead the path for edge computing, Penguin Computing noted.

Download the new Penguin Computing report, “The ROI of GPU-Accelerated Computing,” to learn more about the potential of GPU supercomputing to accelerate and enhance data processing power and efficiency.