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No matter the industry, data science has become a universal toolkit for businesses. Data analytics and machine learning give organizations insights and answers that shape their day-to-day actions and future plans. Being data-driven has become essential to lead any industry. While the world's data doubles each year, CPU computing has hit a brick wall with the end of Moore's law. For this reason, scientific computing and deep learning have turned to NVIDIA GPU acceleration.


Nvidia RAPIDS accelerates analytics and machine learning

ZDNet

At a keynote at the GPU Technology Conference in Munich today, Nvidia, the video/graphics company turned Artificial Intelligence (AI) juggernaut, is today going another step forward in the AI direction. This time though, Nvidia isn't announcing a new Graphics Processing Unit (GPU) platform, or a new proprietary SDK for deep learning, but is instead announcing new a set of new open source libraries for GPU-accelerated analytics and machine learning (ML). Rapid AI movement Dubbed RAPIDS, the new library set will offer Python interfaces similar to those provided by Scikit Learn and Pandas, but which will leverage the company's CUDA platform for acceleration across one or multiple GPUs. According to Nvidia CEO Jensen Huang, who briefed a number of technology journalists by phone on Tuesday, Nvidia has seen 50x speed up in training times when using RAPIDS versus a CPU-only implementation. Integrations and partners RAPIDS apparently incorporates in-memory columnar data technology Apache Arrow, and is designed to run on Apache Spark.


Nvidia and Databricks announce GPU acceleration for Spark 3.0

ZDNet

At its GPU Technology Conference (GTC) event today, consumer graphics and AI silicon powerhouse Nvidia is announcing its next-generation Graphical Processing Unit (GPU) architecture, dubbed Ampere, and its first Ampere-based GPU, the A100. For more details, please see ZDNet's Natalie Gagliordi's coverage of all the Nvidia Ampere-related news today. Specifically, Nvidia is announcing new GPU-acceleration capabilities coming to Apache Spark 3.0, the release of which is anticipated in late spring. The GPU acceleration functionality is based on the open source RAPIDS suite of software libraries, themselves built on CUDA-X AI. The acceleration technology, named (logically enough) the RAPIDS Accelerator for Apache Spark, was collaboratively developed by Nvidia and Databricks (the company founded by Spark's creators).


Nvidia GPUs for data science, analytics, and distributed machine learning using Python with Dask

ZDNet

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.


Nvidia GPUs for data science, analytics, and distributed machine learning using Python with Dask ZDNet

#artificialintelligence

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.