加速 Apache Spark 3.x

利用 NVIDIA GPU 驅動次世代的資料分析和人工智慧

GPU-accelerated libraries, DataFrame and APIs:

  • Layered on top of NVIDIA CUDA, RAPIDS is a suite of open-source software libraries and APIs that provide GPU parallelism and high-bandwidth memory speed through DataFrame and graph operations, achieving speedup factors of 50x or more on typical end-to-end data science workflows. For Spark 3.0, new RAPIDS APIs are used by Spark SQL and DataFrames for GPU accelerated memory efficient columnar data processing and query plans.
  • With Spark 3.0 the Catalyst query optimizer has been modified to identify operators within a query plan that can be accelerated with the RAPIDS API, and to schedule those operators on GPUs within the Spark cluster, when executing the query plan.
  • A new Spark shuffle implementation, built upon GPU accelerated communication libraries including Remote direct memory access (RDMA), dramatically reduces the data transfer among Spark processes. RDMA allows GPUs to communicate directly with each other, across nodes, at up to 100Gb/s, operating as if on one massive server.

GPU-aware Scheduling in Spark

  • Spark 3.0 adds integration with the cluster managers (YARN, Kubernetes, and Standalone) to request GPUs, and plugin points to allow it to be extended to run operations on the GPU. This makes GPUs easier to request and use for Spark application developers, allows for closer integration with deep learning and AI frameworks such as Horovod and TensorFlow on Spark, and allows for better utilization of GPUs.


Apache Spark 強大的執行引擎可跨機器叢集進行大規模的平行資料處理,進而達成快速的應用程式開發和高效能。Spark 3.0 帶來大幅度的改良,讓你可以使用 GPU 的大規模平行架構進一步加速 Spark 資料處理。


  • 從 Hadoop 到 GPU 與 RAPIDS 函式庫的資料處理演變
  • Spark 的內容、用途和重要性
  • Spark 中的 GPU 加速
  • 資料架構和 Spark SQL
  • 隨機森林的 Spark 迴歸範例
  • 以 XGBoost 進行 GPU 加速的端對端機器學習工作流程範例


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