The document discusses the integration of GPU computing with Python through Anaconda, highlighting its potential to accelerate data science workflows. It addresses the challenges of data silos in GPU applications and presents initiatives like the GPU Open Analytics initiative aimed at standardizing data exchange. Additionally, it introduces tools like GPU DataFrames (gdf) and Dask for efficient data manipulation and distributed computing in GPU environments.

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GPU Computing with Python and
Anaconda: The Next Frontier
Accelerate. Connect. Empower.
Stan Seibert
Director of Community Innovation

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GPUs & Python: A Great Combination
• Python is becoming the glue that binds data
science
• Rapid integration empowers data scientists to
combine new technologies
• This is our goal for Anaconda:
• Free distribution of Python and R for
Win/Mac/Linux
• Includes GPU-accelerated packages:
Caffe, TensorFlow, PyTorch, Theano,
Numba, Pyculib...

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ReLU
ReLU
ReLU
ReLU
Deep Learning: An Early Success
• Powerful machine learning
technique
• Many great open source options
• Every major package has a Python
interface
• Very compute intensive
➡Perfect for GPU acceleration

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• Compile numerical
Python functions for
CPU or GPU
• Based on the LLVM
compiler library
• Great for rapid,
custom algorithm
development
Numba: JIT Python Compilation

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Problem: An Ecosystem of Silos?
GPU
ETL/Data
Prep
Database
Machine
Learning
Visualization
Data
Data Data
Data

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Problem: An Ecosystem of Silos?
GPU
ETL/Data
Prep
Database
Machine
Learning
Visualization
Data
Data Data
Data
CPU transfer
CPU transferCPU transfer

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Problem: An Ecosystem of Silos?
GPU
ETL/Data
Prep
Database
Machine
Learning
Visualization
Data
Data Data
Data
CPU transfer
CPU transferCPU transfer Why do GPU applications share
data through slow CPU memory?

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GPU Open Analytics Initiative
Goal:
Standardize data exchange between
GPU analytics applications
Current Members:
MapD, Anaconda, H2O.ai,
BlazingDB, Graphistry, Gunrock
http://gpuopenanalytics.com/

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Streamlining the Data Science Pipeline
GPU Database
Python Data
Transformation
Generalized
Linear Model
All data stays on the GPU
GDF
Packed
Array
Apache
Arrow

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• A format for tabular data in GPU memory
• Exchange GDF between different libraries
• Move between processes using CUDA IPC
• Based on Apache Arrow
• Code in separate library
• Work in progress to move functionality
into Arrow project
GPU Dataframe (GDF)

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• A Python library of manipulating GPU Dataframes:
• Create from NumPy arrays and Pandas Dataframes
• Exchange between processes
• Math operations
• Sort, Filter, Join, Group By
• Ideal for data manipulation and feature engineering stages between
data source and machine learning
• Not intended to replace dedicated database applications
• Interoperates with our Python compiler for GPU: Numba
PyGDF: Python GPU Dataframes

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PyGDF: Group By Performance
GPU speedup become
very large above 10
million elements
Aggregation functions
are extremely efficient
on the GPU

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• Scalable execution task graphs of task graphs from single
computers to 1000+ node clusters
• Scheduler is "resource aware" and can direct GPU tasks to nodes
with appropriate hardware. Great for heterogeneous clusters!
Dask: Distributed Computing

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The Future
• In flight:
• Merger of common code into Apache Arrow GPU support
• Node.js interface to GDF (Graphistry)
• Dask GDF: Distributed GPU dataframe
• Other potential future projects:
• Tensor exchange between Python GPU libraries
• GPU shared memory service (Plasma for GPU)
• Can we improve the interaction of unified memory and IPC?
• What do you want to see?

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Learn More
GPU Open Analytics Website
http://gpuopenanalytics.com
GOAI Github Organization
https://github.com/gpuopenanalytics/
GOAI Google Group
https://groups.google.com/forum/#!forum/gpuopenanalytics