NVIDIA Accelerates Server Workloads with RAPIDS GPU Software

by Sean Michael Kerner

CPUs are no longer the only way to measure compute power.

While traditional silicon forms of compute like x86 and Power are likely to remain the cornerstone of servers for at least another generation, CPUs are increasingly being accelerated by GPUs.

GPUs, or Graphics Process Units, are somewhat of a misnomer in the modern age for many of the applications where there are deployed. While GPUs are an important component for graphics, high-end gaming and design, they are also being widely used to accelerate High Performance Computing (HPC) and Artificial Intelligence (AI) workloads.

This week, NVIDIA announced its RAPIDS open source software for GPUs, alongside multiple partners, including Oracle, HPE and IBM.

"Data analytics and machine learning are the largest segments of the high performance computing market that have not been accelerated — until now," Jensen Huang, founder and CEO of NVIDIA, said during his keynote address at the GPU Technology Conference.

"The world’s largest industries run algorithms written by machine learning on a sea of servers to sense complex patterns in their market and environment, and make fast, accurate predictions that directly impact their bottom line," said Huang.

What Exactly Is RAPIDS?

RAPIDS is a set of open source libraries for optimizing NVIDIA's GPUs for machine learning and data visualization workloads. In the past, a complex pathway of operations passing data from CPU to GPU was typically needed, but with RAPIDS the premise is that an entire data science pipeline can run directly on the GPU.

The RAPIDS libraries build on top of existing open-source projects, including Apache Arros adn scikit-learn.

"Building on CUDA and its global ecosystem, and working closely with the open-source community, we have created the RAPIDS GPU-acceleration platform," Huang said. "It integrates seamlessly into the world’s most popular data science libraries and workflows to speed up machine learning. We are turbocharging machine learning like we have done with deep learning."

Walmart, the world's largest retailer, is already using RAPIDS with apparently great results thus far.

"NVIDIA’s GPU-acceleration platform with RAPIDS software has immensely improved how we use data — enabling the most complex models to run at scale and deliver even more accurate forecasting," Jeremy King, executive vice president and chief technology officer at Walmart, wrote in a statement.

"RAPIDS has its roots in deep collaboration between NVIDIA’s and Walmart’s engineers, and we plan to build on this relationship," King continued.

Sean Michael Kerner is a senior editor at ServerWatch and InternetNews.com. Follow him on Twitter @TechJournalist.

This article was originally published on Friday Oct 12th 2018
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