Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
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This document discusses accelerating Apache Spark workloads using RAPIDS Accelerator for Spark and Alluxio. It provides an introduction to RAPIDS Accelerator for Spark, shows significant performance gains over CPU-only Spark, and discusses combining GPU acceleration with Alluxio for optimized performance and cost on cloud datasets. Configuration options for RAPIDS and Alluxio are also covered.
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Summary
RAPIDS Accelerator for Spark unlocks GPU
acceleration for Spark dataframes, Spark SQL, &
ML/DL frameworks such as XGBoost(with more
coming)
Alluxio is a high performance data orchestration
system for GPU compute.
Spark & GPUs on Alluxio optimizes for
performance and cost on cloud scale datasets.
Spark 3 & GPUs on Databricks, EMR and Dataproc
available today
Try it yourself
https://nvidia.github.io/spark-rapids/Getting-
Started/
Developer Blog:
Accelerating Analytics and AI with Alluxio
and NVIDIA GPUs
GTC Talk:
Enabling Data Orchestration with RAPIDS
Accelerator [S32746]
Accelerating Apache Spark Shuffle with UCX
[S31822]
Tuning GPU Network and Memory Usage in
Apache Spark [S31566]
Running Large-Scale ETL Benchmarks with
GPU-Accelerated Apache Spark [S31846]
……. and more](https://image.slidesharecdn.com/alluxiorapidsspark1-210427172005/85/Advancing-GPU-Analytics-with-RAPIDS-Accelerator-for-Spark-and-Alluxio-19-320.jpg)
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Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
- 1. April 27 2021 Advancing GPU Analytics with RAPIDS Accelerator for Spark and Alluxio
- 2. 2 AGENDA 1. Introduction for RAPIDS Accelerator for Spark 2. RAPIDS Accelerator for Spark Performance 3. GPU Acceleration combined with Alluxio
- 3. 3 GROWTH IN REQUIREMENT FOR DATA PROCESSING 2030 2020 2010 2000 Hadoop Era Spark Era Spark GPU Era Spark 2.0 on CPUs GPU Accelerated Spark 3.0 “These contributions lead to faster data pipelines, model training and scoring for more breakthroughs and insights with Apache Spark 3.0 and Databricks.” Matei Zaharia, creator of Apache Spark and chief technologist at Databricks
- 4. 4 Accelerate data preparation Quickly move to next stages of the pipeline Focus on most-critical activities Orchestrate end-to-end pipelines From ETL to model training to visualization Same infrastructure for Spark and ML/DL frameworks Complete jobs faster with less hardware Save on-prem and in the cloud Do more with less SPARK 3.0 ON NVIDIA GPUs Accelerate data science pipelines without code changes Faster Execution Time Streamline Analytics to AI Reduced Infrastructure Costs
- 5. NVIDIA BRINGS GPU ACCELERATION TO APACHE SPARK Features • Use existing (unmodified) customer code • Spark features that are not GPU enabled run transparently on the CPU Initial Release - GPU Acceleration of: • Spark Data Frames • Spark SQL • ML/DL training frameworks Seamless integration with Spark 3.0
- 6. RAPIDS ACCELERATOR FOR APACHE SPARK UCX Libraries RAPIDS libcudf (C++ Libraries) CUDA JNI bindings Mapping From Java/Scala to C++ RAPIDS Accelerator for Spark DISTRIBUTED SCALE-OUT SPARK APPLICATIONS Spark SQL API Spark Shuffle DataFrame API if gpu_enabled(operation, data_type) call-out to RAPIDS else execute standard Spark operation JNI bindings Mapping From Java/Scala to C++ ● Custom Implementation of Spark Shuffle ● Optimized to use RDMA and GPU-to-GPU direct communication APACHE SPARK CORE
- 7. GAME-CHANGING PERFORMANCE GAINS 7x Performance Boost 90% Cost Savings on Databricks Opens new possibilities for AI-driven services in Adobe Experience Cloud “We’re seeing significantly faster performance with NVIDIA-accelerated Spark 3.0 compared to running Spark on CPUs. With these game-changing GPU performance gains, new possibilities open up for enhancing AI-driven features in our full suite of Adobe Experience Cloud apps.” — William Yan, Senior Director of Machine Learning at Adobe
- 8. RAPIDS ACCELERATOR ECOSYSTEM MOMENTUM Databricks Machine Learning Runtime Google Cloud Dataproc Apache Spark 3.0 Community Release Amazon EMR Available Now Available Now Available Now Available Now Cloudera CDP Available in Jun’21
- 9. 9 NVIDIA CONFIDENTIAL - DO NOT COPY OR DISTRIBUTE Nodes 8 CPU 2 x AMD EPYC 7452 (64 cores/128 threads) GPU 2 x NVIDIA Ampere A100, PCIe, 250W, 40GB RAM 0.5 TB Storage 4 x 7.68 TB Gen4 U.2 NVMe Networking 1 x Mellanox CX-6 Single Port HDR100 QSFP56 Cost w/o GPU ~$42,000 per w/ bulk discount Cost w/ GPU ~$71,000 per w/ bulk discount Software HDFS (Hadoop 3.2.1) Spark 3.0.2 (stand alone) EGX / NVIDIA Certified OEM servers Benchmark Environment – EGX
- 10. SPARK SQL QUERIES – EGX CLUSTER Based on 97 NVIDIA Decision Support (NDS) benchmark (3TB Dataset without decimals)* GPU is 3.21x faster with a cost ratio of 0.52 (GPU cost was 52% that of the CPU) Queries 14b and 72 were removed because of failures *NVIDIA Decision Support (NDS) benchmark is derived from the TPC-DS benchmark and is used for internal performance testing. Results from NDS are not comparable to TPC-DS
- 11. UCX ON VS OFF GPU + UCX shuffle is 1.23x faster than the GPU alone. Queries 67 and 72 removed because of failures. GPU + UCX Shuffle is 4.15x faster than the CPU and a cost ratio of 0.41 Queries 14a, 67, and 72 were removed because of failures. SPARK SQL QUERIES – EGX CLUSTER
- 12. 12 12 Nodes 1 driver (CPU only), 4 workers CPU n1-standard-4 (driver) 4 x n1-standard-32 (workers) GPU 4 x 16GB T4 per executor RAM 120 GB Storage Google Cloud Storage/Alluxio with SSD Networking 32 Gbps Cost w/o GPU $7.82/hour incl GCE + Dataproc Cost w/ GPU $13.41/hour incl GCE + Dataproc Software Dataproc Spark 3.0.1 + YARN Benchmark Environment – GCP DATAPROC
- 13. 13 *NVIDIA Decision Support (NDS) benchmark is derived from the TPC-DS benchmark and is used for internal performance testing. Results from NDS are not comparable to TPC-DS
- 14. WHY SOME GPU QUERIES FAILED GPU Memory Limitations/Spilling Operation Problem Solution Sort In cases of data skew, the amount of data being sorted can exceed limits of the hardware/software. A modified external batch sort is implemented in the working branch for the 0.5 release. Window* In cases of data skew the amount of data in the window operation can exceed the limits of the hardware/software. Implement a chunked rank optimization. Github issue #1859 Join Worst case join output row count is left.rows * right.rows Materialize the output of a join in chunks. Github issue #1629 Have conditional filters run as a part of the join. Github issue #288 * Actually, this is for rank which we don’t support yet but plan to in the 0.5 release
- 15. 15 WHY IS THE GPU SLOWER FOR SOME QUERIES? • Failed Queries • Small Data Sizes (spark.sql.adaptive.advisoryPartitionSizeInBytes=1G) • Q28, Q44, and Q67 • Less computation overlap (spark.rapids.sql.concurrentGpuTasks=1) • Host/Device Memory Transfers • All of them • Cache Consistency on Reductions/Very Small Aggregate Results • Q88 • Lack of GPU support and CPU parallelism is much less • Q44, Q49, and Q67
- 16. 16 ALLUXIO CONFIGURATION - Co-locate the Alluxio worker nodes with Spark worker nodes to ensure short-circuit reads and writes. - Size cache according to the working set. - Choose the right cache medium choice(SSD or System Memory)
- 17. 17 spark.rapids.sql.enabled is the master enable spark.rapids.sql.explain enables logging of operations not accelerated - Set to NOT_ON_GPU to print only incompatible ops spark.rapids.sql.concurrentGpuTasks controls concurrent task count per GPU - Set to a value between 2 and 4, with 2 typically providing the most benefit spark.rapids.memory.pinnedPool.size significantly improves performance of data transfers between the GPU and host memory RAPIDS ACCELERATOR CONFIGURATION
- 18. 18 WILL MY SPARK WORKLOAD ACCELERATE WITHOUT CHANGES? If I know my Spark workload characteristics... Accelerates Well on GPUs Not for GPUs Data Pipeline Use Cases ● Data Mining, Analytics and BI ● Batch processing and writing large datasets to a Data Warehouse ● Data extraction, aggregation and feature preparation for ML Training & Inference ● Real-time Streaming Analytics/AI pipeline ● Online Transaction Processing (OLTP) ● Data Pipeline with custom code Technical Characteristics ● Batch processing of GB+ data sets ● Parquet, ORC, CSV data formats ● HDFS, S3-compatible, or V2 data sources ● DataFrame/SQL (join, agg, sort, window), Selected Hive & Scala UDFs ● Stream processing ● Spark RDD, MLLib, Dataset, GraphX, Streaming libraries If I am unsure... Use the Log-Analysis Tool ● Review Spark history logs from existing CPU jobs ● Understand how much of the workloads could execute on GPUs ● Get tips on optional code optimizations for GPUs Apache Spark Apache Spark - Core Catalyst Query Optimizer Spark Streaming Spark SQL Spark Dataframes Spark Datasets RDD Spark Shuffle Spark MLLib GraphX CPU Only GPU Aware GPU Accelerated Partially GPU Accelerated
- 19. 19 Summary RAPIDS Accelerator for Spark unlocks GPU acceleration for Spark dataframes, Spark SQL, & ML/DL frameworks such as XGBoost(with more coming) Alluxio is a high performance data orchestration system for GPU compute. Spark & GPUs on Alluxio optimizes for performance and cost on cloud scale datasets. Spark 3 & GPUs on Databricks, EMR and Dataproc available today Try it yourself https://nvidia.github.io/spark-rapids/Getting- Started/ Developer Blog: Accelerating Analytics and AI with Alluxio and NVIDIA GPUs GTC Talk: Enabling Data Orchestration with RAPIDS Accelerator [S32746] Accelerating Apache Spark Shuffle with UCX [S31822] Tuning GPU Network and Memory Usage in Apache Spark [S31566] Running Large-Scale ETL Benchmarks with GPU-Accelerated Apache Spark [S31846] ……. and more