Nvidia GPU-based Rapids system promises '50x faster' data analytics

Nvidia RAPIDS accelerates analytics and machine learning

Nvidia RAPIDS accelerates analytics and machine learning

MapRs work with NVIDIA in the RAPIDS ecosystem is helping make broad adoption in the enterprise easy for the largest breadth of workloads, said Clment Farabet, vice president of AI infrastructure at NVIDIA. These encompass such mission-critical applications as credit card fraud detection, retail inventory forecasts, and customer purchasing prediction, each one of these represents billions of dollars to the economy. "We are excited to partner with NVIDIA on RAPIDS to accelerate the application of data science and machine learning to help our customers drive faster and more insightful outcomes".

NVIDIA announced on Wednesday a GPU-acceleration platform for data science and machine learning, which enables even the largest companies to analyze massive amounts of data and make accurate business predictions at improved speed.

Oracle Cloud Infrastructure is also working with NVIDIA to support RAPIDS across its platform, including the Oracle Data Science Cloud, to further accelerate customers' end to-end data science workflows.

GPU leader Nvidia, generally associated with deep learning, autonomous vehicles and other higher-end AI-related workloads (and gaming, of course), is mounting an open source end-to-end GPU acceleration platform and ecosystem directed at machine learning and data analytics, domains heretofore within the CPU realm. It relies on NVIDIA CUDA primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

According to Jensen Huang, CEO of Nvidia, who on Tuesday informed several technology journalists by phone, Nvidia has experienced 50 times shorter training time using RAPIDS instead of a pure CPU implementation.

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RAPIDS builds on popular open-source projects - including Apache Arrow, pandas and scikit-learn - by adding GPU acceleration to the most popular Python data science toolchain. MapRs ability to span on-prem and cloud, from IoT edge to core with a scalable, high-performance common platform means that more data can be fed to GPUs and more innovative applications can be created by data scientists faster. Besides the aforementioned server-makers, RAPIDS also garnered backing from more than a dozen other organizations with a stake in data science, including big-name vendors such as NetApp, SAP, and MapD, as well as public organizations like NERSC, Georgia Tech, and UC Davis.

Analyst Patrick Moorhead of Moor Insights & Strategy said RAPIDS was all about Nvidia trying to make its GPUs more accessible to enterprises interested in running AI workloads.

"We look to continue to aggressively push the performance boundaries of AI for our clients as we bring RAPIDS into the IBM portfolio".

Hewlett Packard Enterprise Co., Cisco Systems Inc., Dell Technologies Inc. and Lenovo Group Ltd. will also support RAPIDS on their own systems, Nvidia said. It integrates seamlessly into the world's most popular data science libraries and workflows to speed up machine learning. Apache Arrow is a development platform for in-memory data processing that is now the industry standard for columnar in-memory data analytics. The companies are also announcing the general availability of support for GPU-accelerated deep learning and high performance computing (HPC) containers from the (NGC) container registry on Oracle Cloud Infrastructure.

Access to the RAPIDS open-source suite of libraries is immediately available online, where the code is being released under the Apache license.

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