BSC LMS DDL

© 2018 IBM Corporation
IBM Cognitive Systems
IBM OpenPOWER foundation
Barcelona Supercomputing Center
Power9 AI differentiators
Ander Ochoa – ander.ochoa.gilo@ibm.com
Cognitive Systems Technical Architect for SPGI
https://es.linkedin.com/in/anderotxoa @AnderOtxoa IBM 2018

2
Agenda
• Large Memory Support (LMS)
• Distributed Deep Learning (DDL)
• PowerAI Vision

3
Large Memory Support (LMS)
Objective: Overcome GPU Memory Limitations in DL Training. Increase the Batch Size
and/or increase the resolution of the features.
LMS enables processing of high definition images, large models, and higher batch
sizes that doesn’t fit in GPU memory today (Maximum GPU memory available in
Nvidia P100 and V100 GPUs is 16/32GB).
Available for
- Caffe
- TensorFlow
- Chainer
https://www.sysml.cc/doc/127.pdf
GPU RAM
System RAM
NVLinkv2.0
2 TB
16/32 GB
Accelerated
by
NVLink
Dataset

LMS advantages running in Power architecture
• LMS DL workload in Power vs LMS DL workload in x86
• NVLink provides up to 380% (depends on every workload/dataset)
• NO LMS DL workload in Power vs NO LMS DL workload in x86
• NVLink provides 30% advantage (depends on every workload/dataset)
NVMe Storage
GPU RAM
System RAM
NVMeover
PCIeV4
@32-140GB/sN TB
2 TB
16/32 GB
SAS Storage
GPU RAM
System RAM
SASover
PCIeV3
@12Gb/s
PCIeV3
@16GB/s
N TB
1 TB
16/32 GB
GPU
GPU
NVLinkv2.0
@150GB/s
HBM2
HBM2
Power9x86
DDR4
DDR4

5
AC922 System buses and components diagram
32 -140+GB/s
64GB/s

https://www.linkedin.com/pulse/deep-learning-high-resolution-images-large-models-sumit-gupta/
This comparison is between a AC922 server with four NVIDIA Tesla
V100 GPUs versus a server with two Intel Xeon 2640v4 CPUs with
four NVIDIA Tesla V100 GPUs

7
LMS in Caffe
$caffe train -solver solver.prototxt -bvlc -gpu 0,1,2,3 -lms 10000 -lms_frac 0.5
• -lms 10000. Any memory chunk allocation larger than 10000KB will be done in
CPU memory, and fetched to GPU memory only when needed for
computation.
• -lms_frac 0.5. LMS doesn’t kick in until more than at least 50% of GPU
memory is expected to be utilized.
Note that configuring the “lms” and “lms_frac” values depends on the below
factors:
• Batch size used
• Model used
• Number of GPUs used
• System memory available
Arriving at an optimal configuration requires understanding of the above and
experimentation based on that. A general guideline is that the optimal
configuration should utilize GPU memory close to fullest.

8
Demo
https://developer.ibm.com/linuxonpower/2017/09/22/realizing-value-large-model-support-lms-powerai-ibm-caffe/
ssh bsc18651@plogin2.bsc.es
srun -N 1 --exclusive --gres="gpu:4" --exclusive --pty $SHELL
#Set the cpu to performance mode
lscpu; ppc64_cpu –smt; ppc64_cpu --smt=2
cpupower -c all frequency-set -g performance
#check gpu status
nvidia-smi ; nvidia-smi -i 0 –q; nvidia-smi -ac 877,1530
#Activate caffe
cd /gpfs/scratch/bsc18/bsc18040/lms
source /opt/DL/caffe/bin/caffe-activate
#show solver
cat solver.prototxt
#check in model batchsize = 1
vi models/googlenet_big.prototxt
caffe train -solver solver.prototxt -bvlc -gpu 0,1,2,3
#change model batchsize = 5
#will give error (out of memory)
caffe train -solver solver.prototxt -bvlc -gpu 0,1,2,3
#check in model batchsize = 5
caffe train -solver solver.prototxt -bvlc -gpu 0,1,2,3 -lms 10000 -lms_frac 0.5

9
Agenda
• PowerAI Vision

10

11
Distributed Deep Learning
Objective: Overcome the server boundaries of some DL frameworks.
How: Scaling. Using “ddlrun” applied to Topology aware distributed frameworks.
Our software does deep learning training fully synchronously with very low
communication overhead.
The overall goal of ddlrun is to improve the user experience DDL users.
To this end the primary features of ddlrun are:
• Error Checking/Configuration Verification
• Automatic Rankfile generation
• Automatic mpirun option handling
Available for:
• Tensorflow
• IBM Caffe
• Torch
How to code Topology Aware Distributed Models:
• https://netweblog.wordpress.com/2018/04/10/distributed-tensorflow-sample-code-and-how-it-works/
• https://arxiv.org/pdf/1704.04560.pdf
https://www.sysml.cc/doc/127.pdf
Good for:
• Speed
• Accuracy

Distributed Deep Learning (DDL) for Training phase
Using the Power of 100s of Servers
August 8, 2017
16 Days Down to 7 Hours: Near Ideal Scaling to 256 GPUs and Beyond
1 System 64 Systems
16 Days
7 Hours
ResNet-101, ImageNet-22K, Caffe with PowerAI DDL, Running on Minsky (S822Lc) Power System
58x Faster
https://www.ibm.com/blogs/research/2017/08/distributed-deep-learning/

Demo
13
ssh bsc18651@plogin2.bsc.es #Slurm login node
# You should have a ~/data dir with the dataset downloaded or internet conection to download it
#Edit and include the following line in ~/.bashrc
export TMPDIR=/tmp/
# To pass all the variables, like activate ...., you may need to write a simple submission script: Run as “sbatch script.sh”
[bsc18651@p9login2 ~]$ cat script.sh
#!/bin/bash
#SBATCH -J test
#SBATCH -D .
#SBATCH -o test_%j.out
#SBATCH -e test_%j.err
#SBATCH -N 2
#SBATCH --ntasks-per-node=4
#SBATCH --gres="gpu:4"
#SBATCH --time=01:00:00
module purge
module load anaconda2 powerAI
source /opt/DL/ddl-tensorflow/bin/ddl-tensorflow-activate
export TMPDIR="/tmp/"
export DDL_OPTIONS="-mode b:4x2"
NODE_LIST=$(scontrol show hostname $SLURM_JOB_NODELIST | tr 'n' ',')
NODE_LIST=${NODE_LIST%?}
cd examples/mnist
ddlrun -n 8 -H $NODE_LIST python mnist-init.py --ddl_options="-mode b:4x2" --data_dir /home/bsc18/bsc18651/examples/mnist/data
[bsc18651@p9login2 ~]$ sbatch script.sh
https://developer.ibm.com/linuxonpower/2018/05/01/improved-ease-use-ddl-powerai/

14
Agenda
• PowerAI Vision

PowerAI Vision v1.1
15
https://www-01.ibm.com/common/ssi/cgi-
bin/ssialias?infotype=an&subtype=ca&appname=gpateam&supplier=877&letternum=ENUSZP18-0143
PowerAI Vision V1.1 can help provide robust end-to-end
workflow support for deep learning models related to
computer vision. This enterprise-grade software provides a
complete ecosystem to label raw data sets for training,
creating, and deploying deep learning-based models.
PowerAI Vision is designed to empower subject matter
experts with no skills in deep learning technologies to
train models for AI applications.
It can help train highly accurate models to classify images
and detect objects in images and videos.
PowerAI Vision is built on open source frameworks
• User interface-driven interaction to
configure and manage lifecycles of data
sets and models
• A differentiated capability where trained
deep learning models automatically
detect objects from videos
• Preconfigured deep learning models
specialized to classify and detect objects
• Preconfigured hyper-parameters
optimized to classify and detect objects
• Training visualization and runtime
monitoring of accuracy
• Integrated inference service to deploy
models in production
• Scalable architecture designed to run
deep learning, high-performance
analytics, and other long-running
services and frameworks on shared
resources

PowerAI Vision demo
16
http://9.172.154.29:9080/powerai-vision/index.html

Artificial Intelligence Power8 System Available NOW!
ü To Facilitate and Expedite PoCs, Demos, Workshops…
ü To offer to clients/BPs/partners so they can test it by themselves
ü To show it to the world J
Located in the IBM Client Center
(Madrid)
IBM “9”
network
Accesible from: (on demand)
Internet
Tech specs:
- S822LC Power8 server (20 P8 cores)
- 2x Nvidia P100 GPUs (7168 cuda cores)
- 2x 500GB SDD + 1.6 TB NVMe Storage
- 256 GB RAM
- Ubuntu 16.04
- PowerAI v1.4 + VisionAI TP4

IBM ICP + IBM DSX + IBM AI. Available NOW!
ü To Facilitate and Expedite PoCs, Demos, Workshops…
ü To offer to clients/BPs/partners so they can test it by themselves
ü To show it to the world J
Located in the IBM TEC
(Madrid)
IBM “9”
network
Accesible from: (for the time being)
Tech specs:
- S822L Power8 server
(10 P8 cores)
- 2 LPARs
- 2x 500GB HDD
- 512GB RAM
- Ubuntu 16.04
- IBM Cloud Private
- IBM Data Science
Experience
- S822L Power8 server (10
P8 cores)
- 2x Nvidia P100 GPUs
(7168 cuda cores)
- 2x 500GB SDD + 1.6 TB
NVMe Storage
- 256 GB RAM
- Ubuntu 16.04
- PowerAI tools
ICP https://9.172.229.247:8443/console/
DSX https://9.172.229.247:31843

THANK YOU!

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