Nvidia rides the RAPIDS to accelerate graphics chips for AI training

Nvidia rides the RAPIDS to accelerate graphics chips for AI training

Examples include predicting credit card fraud, forecasting retail inventory and understanding consumer buying behavior, Nvidia officials said. It encompasses GPU support for not just conventional analytics, but also machine learning (including deep learning), graph analytics, stream processing, and eventually visualization.

"Data has become a very important part of every company, and it's become a critical aspect of how companies do business", said Jeff Tseng, head of product at Nvidia's newly-formed AI Infrastructure team.

"Data analytics and machine learning are the largest segments of the high-performance computing market that have not been accelerated - until now", says NVIDIA founder and CEO Jensen Huang.

NVIDIA has announced a set of open-source libraries meant to bring GPU acceleration to a wide swathe of data analytics applications. The companies are also announcing the general availability of support for GPU-accelerated deep learning and HPC containers from the NVIDIA GPU Cloud (NGC) container registry on Oracle Cloud Infrastructure. This optimizes machine learning training with more iterations for better model accuracy.

A Reference Architecture (URL) providing detailed technical information on how to configure NVIDIA DGX systems with MapR Data Platform for optimal use is also available here. Initial RAPIDS benchmarking, using the XGBoost machine learning algorithm for training on an NVIDIA DGX-2 system, shows 50 times speedups compared with CPU-only systems. "We are turbocharging machine learning like we have done with deep learning", he said.

Reflecting the growing consensus about the GPU's importance in data analytics, an array of companies are supporting RAPIDS - from pioneers in the open-source community, such as Databricks and Anaconda, to tech leaders such as Hewlett Packard Enterprise, IBM and Oracle. They include database giant Oracle Corp., which is supporting RAPIDS on its Oracle Cloud Infrastructure via Nvidia's cloud.

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Oracle Cloud Infrastructure is the enterprise Infrastructure as a Service (IaaS) platform.

Oracle and NVIDIA today announced that Oracle is the first public cloud provider to support the NVIDIA HGX-2 platform on Oracle Cloud Infrastructure, to meet the needs of the next generation of analytics, machine learning and AI. MapR complements RAPIDS with a data management and logistics fabric to accelerate the high-scale processing and access of disparate data across geographies.

The RAPIDS suite of open-source libraries has been under development for the past two years by Nvidia engineers working with open-source contributors, including Apache Arrow (a data layer for in-memory analytics), Pandas and scikitlearn, and it's created to give scientists the tools to run the entire data science pipeline on GPUs.

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

IBM, HPE, Dell EMC, Oracle, Cisco, and Lenovo also stand to benefit, although perhaps not to the same extent as NVIDIA, inasmuch as they can also sell CPU-only systems for their big data customers. "These technologies are driving RAPIDS' ability to integrate into today's most popular data science workloads and accelerate them...." The new software suite has attracted support from IBM, HPE, Oracle and a number of other key providers.

If it can do this, Nvidia has a great shot at dominating the market for AI workloads for years to come, said Holger Mueller, principal analyst and vice president at Constellation Research Inc.

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