NVIDIA introduced this for Large-Scale Data Analytics and Machine Learning
NVIDIA announced a GPU-acceleration platform for data science and machine learning, with broad adoption from industry leaders that enables even the largest companies to analyze massive amounts of data and make accurate business predictions at unprecedented speed.
GPU-accelerated data science platform
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. To bring additional machine learning libraries and capabilities to RAPIDS, NVIDIA is collaborating with such open-source ecosystem contributors as Anaconda, BlazingDB, Databricks, Quansight and scikit-learn, as well as Wes McKinney, head of Ursa Labs and creator of Apache Arrow and pandas, the fastest-growing Python data science library.
“RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow,” McKinney said. “NVIDIA’s collaboration with Ursa Labs will accelerate the pace of innovation in the core Arrow libraries and help bring about major performance boosts in analytics and feature engineering workloads.”
To facilitate broad adoption, NVIDIA is integrating RAPIDS into Apache Spark, the leading open-source framework for analytics and data science.
“Data analytics and machine learning are the largest segments of the high performance computing market that have not been accelerated ‑‑ until now,” said Jensen Huang, founder and CEO of NVIDIA, who revealed RAPIDS in 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. Building on CUDA and its global ecosystem, and working closely with the open-source community, we have created the RAPIDS GPU-acceleration platform. 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.”