Apache Spark 3.0 Is GPU-Accelerated with RAPIDS Apache Spark 3.0 is the first release of Spark to offer fully integrated and seamless GPU acceleration for analytics and AI workloads. Tap into the power of Spark 3.0 with GPUs either on-premises or in the cloud, without changing your code. The breakthrough performance of GPUs empowers enterprises and researchers to train bigger models more frequently ultimately unlocking the value of big data with the power of AI. Learn More
GPU-Accelerated Business in Action Maximize performance, productivity and ROI for machine learning workflows. Interactive Infographic Solution Brief
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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. - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas
I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!? - Streaming Media Company
My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome. - A mid-market specialty retailer with 6000 stores
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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. - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas
I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!? - Streaming Media Company
My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome. - A mid-market specialty retailer with 6000 stores
RAPIDS, a GPU-accelerated data science platform, is a next-generation computational ecosystem powered by Apache Arrow. The NVIDIA 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. - Wes McKinney, Head of Ursa Labs and Creator of Apache Arrow and Pandas
I got 24x speedup using RAPIDS XGBOOST and can now replace hundreds of CPU nodes, running my biggest ML workload on a single node with 8 GPUs. You made XGBOOST too fast!? - Streaming Media Company
My previous bottleneck was I/O. …10 minutes to pull in data for 10 stores (about 1 million rows). With RAPIDS, we can pull in data for about 6000 stores (millions of rows) in less than 3 minutes. That scale could have easily taken us 4 days on legacy infrastructure … just plain awesome. - A mid-market specialty retailer with 6000 stores