From inception to insight: Accelerating AI productivity ...
From inception to insight: Accelerating AI productivity with GPUs
John Zedlewski, Director, RAPIDS Machine Learning @ NVIDIA Ramesh Radhakrishnan, Technologist @ Server OCTO, Dell EMC
Data sizes continue to grow
Prototyping and production diverge
Large-scale cluster
Spark, Hadoop High throughput
Full data
Workstation
Python Fast iteration Small data subset
Challenges!
"Tools gap" - rewriting Python or R code to Spark/Hadoop jobs to scale to cluster
High latency on cluster leads to slower iteration
Small data subsets on workstation make it hard to build realistic models
RAPIDS on GPU
RAPIDS + Dask
Consistent tools: Workstation or Cluster
High throughput / low latency
Full data or large subsets
2
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
QueryWCrPiUte
GPU Read
ETL
CPU Write
GPU ML Read Train
5-10x Improvement More code
Language rigid Substantially on GPU
ML Train
25-100x Improvement Less code
Language flexible Primarily In-Memory
3
DDaattaa MMoovveemmeenntt aanndd TTrraannssffoorrmmaattiioonn The bane of productivity and performance
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
4
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
5
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