GPU Accelerated Data Analytics in Python

[Pages:42]GPU Accelerated Data Analytics in Python

Mads R. B. Kristensen, NVIDIA

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Scale up and out with RAPIDS and Dask

RAPIDS and Others

Accelerated on single GPU

NumPy -> CuPy/PyTorch/.. Pandas -> cuDF Scikit-Learn -> cuML Numba -> Numba

Dask + RAPIDS

Multi-GPU On single Node (DGX) Or across a cluster

Scale Up / Accelerate

PyData

NumPy, Pandas, Scikit-Learn and many more

Single CPU core In-memory data

Dask

Multi-core and Distributed PyData

NumPy -> Dask Array Pandas -> Dask DataFrame Scikit-Learn -> Dask-ML ... -> Dask Futures

Scale out / Parallelize

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Scale up and out with RAPIDS and Dask

RAPIDS and Others

Accelerated on single GPU

NumPy -> CuPy/PyTorch/.. Pandas -> cuDF Scikit-Learn -> cuML Numba -> Numba

Scale Up / Accelerate

PyData

NumPy, Pandas, Scikit-Learn and many more

Single CPU core In-memory data

Scale out / Parallelize

4

CPU vs GPU

DOI: 10.1016/j.cam.2013.12.032.

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Data Processing Evolution

Faster data access, less data movement

Hadoop Processing, Reading from disk

HDFS Read

Query

HDFS HDFS Write Read

Spark In-Memory Processing

ETL

HDFS HDFS Write Read

HDFS Read

Query

ETL

ML Train

Traditional GPU Processing

HDFS Read

GPU Read

Query

CPU Write

GPU Read

ETL

CPU GPU ML Write Read Train

5-10x Improvement More code

Language rigid Substantially on GPU

ML Train

25-100x Improvement Less code

Language flexible Primarily In-Memory

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DDaattaa MMoovveemmeenntt aanndd TTrraannssffoorrmmaattiioonn What if we could keep data on the GPU?

APP B

Read Data

CPU

APP B Copy & Convert APP A

Copy & Convert Copy & Convert

APP B

GPU Data

GPU

GPU Data

APP A

APP A

Load Data

7

DDaattaa MMoovveemmeenntt aanndd TTrraannssffoorrmmaattiioonn What if we could keep data on the GPU?

APP B

Read Data

CPU

APP B Copy & Convert APP A

Copy & Convert Copy & Convert

APP B

GPU Data

GPU

GPU Data

APP A

APP A

Load Data

8

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