The Platform Inside and Out Release 0 - RAPIDS Docs
The Platform Inside and Out Release 0.15
Joshua Patterson ? Senior Director, RAPIDS Engineering
Why GPUs?
Numerous hardware advantages
Thousands of cores with up to ~20 TeraFlops of general purpose compute performance
Up to 1.5 TB/s of memory bandwidth Hardware interconnects for up to 600 GB/s
bidirectional GPU GPU bandwidth Can scale up to 16x GPUs in a single node
Almost never run out of compute relative to memory bandwidth!
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RAPIDS
End-to-End GPU Accelerated Data Science
Data Preparation
cuDF cuIO Analytics
Dask
cuML Machine Learning
Model Training
Visualization
cuGraph Graph Analytics
PyTorch, TensorFlow, MxNet
Deep Learning
cuxfilter, pyViz, plotly
Visualization
GPU Memory
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Data Processing Evolution
Faster Data Access, Less Data Movement
Hadoop Processing, Reading from Disk
HDFS Read
Query
HDFS Write
HDFS Read
ETL
HDFS Write
HDFS Read
Spark In-Memory Processing
HDFS Read
Query
ETL
Traditional GPU Processing
HDFS Read
GPU Read
Query
CPU Write
GPU Read
ETL
CPU Write
GPU ML Read Train
ML 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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Data Movement and Transformation
The Bane of Productivity and Performance
APP B
CPU
APP B Copy & Convert
APP A
APP A
Read Data Copy & Convert Copy & Convert
Load Data
APP B
GPU DATA
GPU
GPU DATA
APP A
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