The RAPIDS Accelerator for Apache Spark leverages GPUs to accelerate processing via the RAPIDS libraries.
As data scientists shift from using traditional analytics to leveraging AI(DL/ML) applications that better model complex market demands, traditional CPU-based processing can no longer keep up without compromising either speed or cost. The growing adoption of AI in analytics has created the need for a new framework to process data quickly and cost-efficiently with GPUs.
The RAPIDS Accelerator for Apache Spark combines the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework. The RAPIDS Accelerator library also has a built-in accelerated shuffle based on UCX that can be configured to leverage GPU-to-GPU communication and RDMA capabilities.
If you are a customer looking for information on how to adopt RAPIDS Accelerator for Apache Spark for your Spark workloads, please go to our User Guide for more information: link.