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UPDATED 17-11-27 PyData NY Lightning Talk: GPU Acceleration with GOAI in Python
The document discusses the evolution of data processing with a focus on GPU acceleration using GoAI in Python, highlighting improvements in data access speed and reduced data movement. It emphasizes the transition to GPU-accelerated architectures with shared data access, using technologies like Apache Arrow and GPU Data Frames. The author encourages collaboration in the GPU analytics community, offering resources for integration and support.
UPDATED 17-11-27 PyData NY Lightning Talk: GPU Acceleration with GOAI in Python
- 1. Mike Wendt @mike_wendt GPU ACCELERATION WITHGOAI IN PYTHON
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- 3. 3 DATA FORMATS Avro XML JSON GML ProtoBuf HDFS Pickle CSV Parquet Panda Plain Textvs Binary Compressed vs Uncompressed CSR COO CSC * Not a complete list Numpy
- 4. 4 DATA PROCESSING EVOLUTION FasterData Access Less Data Movement HDFS Read HDFS Write HDFS Read HDFS Write HDFS Read Query ETL ML Train Hadoop Processing, Reading from disk
- 5. 5 DATA PROCESSING EVOLUTION FasterData Access Less Data Movement HDFS Read HDFS Write HDFS Read HDFS Write HDFS Read Query ETL ML Train HDFS Read Query ETL ML Train Hadoop Processing, Reading from disk 25-100x Improvement Less code Language flexible Primarily In-Memory Spark In-Memory Processing
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- 7. 7 25-100x Improvement Less code Languageflexible Primarily In-Memory DATA PROCESSING EVOLUTION Faster Data Access Less Data Movement HDFS Read HDFS Write HDFS Read HDFS Write HDFS Read Query ETL ML Train HDFS Read Query ETL ML Train HDFS Read GPU Read Query CPU Write GPU Read ETL CPU Write GPU Read ML Train 5-10x Improvement More code Language rigid Substantially on GPU GPU/Spark In-Memory Processing Hadoop Processing, Reading from disk Spark In-Memory Processing
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- 10. 10 APP A GPU-ACCELERATED ARCHITECTURETHEN Too much data movement and too many different data formats CPU GPU APP B Read DataH2O.ai Anaconda Gunrock Graphistry BlazingDB MapDKinetica Copy & Convert Copy & Convert Copy & Convert Load Data APP A GPU Data APP B GPU Data
- 11. 11 APP A GPU-ACCELERATED ARCHITECTURETHEN Too much data movement and too many different data formats CPU GPU APP B Read DataH2O.ai Anaconda Gunrock Graphistry BlazingDB MapDKinetica Copy & Convert Copy & Convert Copy & Convert Load Data APP A GPU Data APP B GPU Data
- 12. 12 GPU-ACCELERATED ARCHITECTURE NOW Singledata format and shared access to data on GPU CPU GPU GPU MEM Read DataH2O.ai Anaconda Gunrock Graphistry BlazingDB MapDKinetica Load Data Apache Arrow GPU Data Frame Powered by:
- 13. 13 GPU OPEN ANALYTICSINITIATIVE github.com/gpuopenanalytics GPU Data Frame (GDF) Ingest/ Parse Exploratory Analysis Feature Engineering ML/DL Algorithms Grid Search Scoring Model Export @gpuoai Apache Arrow
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- 16. 16 USE GPUS INPYTHON @gpuoai
- 17. 17 GROWING COMMUNITY SUPPORT ApacheArrow Apache Parquet
- 18. 18 APACHE ARROW COMMONDATA LAYER From Apache Arrow Home Page - https://arrow.apache.org/
- 19. 19 GPU ACCELERATION ACROSSTHE ECOSYSTEM
- 20. 20 25-100x Improvement Less code Languageflexible Primarily In-Memory DATA PROCESSING EVOLUTION Faster Data Access Less Data Movement HDFS Read HDFS Write HDFS Read HDFS Write HDFS Read Query ETL ML Train HDFS Read Query ETL ML Train HDFS Read GPU Read Query CPU Write GPU Read ETL CPU Write GPU Read ML Train Arrow Read Query ETL ML Train 5-10x Improvement More code Language rigid Substantially on GPU 25-100x Improvement Same code Language flexible Primarily on GPU End to End GPU Processing (GOAI) GPU/Spark In-Memory Processing Hadoop Processing, Reading from disk Spark In-Memory Processing
- 21. 21 LET’S COLLABORATE AND SHAREDATA ON GPUS! @gpuoai github.com/gpuopenanalytics Docker Demos with Jupyter Notebooks pyGDF Library gpuopenanalytics.com Google Groups Public Slack Wiki
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