Run Spark-RAPIDS ML workloads with GPUs on Amazon EMR on EKS

[Amazon Web Services]

Apache Spark revolutionized big data processing with its distributed computing capabilities, which enabled efficient data processing at scale. It offers the flexibility to run on traditional Central Processing Unit (CPUs) as well as specialized Graphic Processing Units (GPUs), which provides distinct advantages for various workloads. As the demand for faster and more efficient machine learning (ML) workloads grows, specialized hardware acceleration becomes crucial. This is where NVIDIA GPUs and Compute Unified Device Architecture (CUDA) come into the picture.

To further enhance the capabilities of NVIDIA GPUs within the Spark ecosystem, NVIDIA developed Spark-RAPIDS. Spark-RAPIDS is an extension library that uses RAPIDS libraries built on CUDA, to enable high-performance data processing and ML training on GPUs. By combining the distributed computing framework of Spark with the parallel processing power of GPUs, Spark-RAPIDS significantly improves the speed and efficiency of analytics and ML workloads...

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