The document provides an overview of PowerAI, IBM's set of libraries for developing machine learning and deep learning applications. It discusses what PowerAI is, its hardware requirements, the differences between CPUs and GPUs for machine learning, how to use PowerAI components like TensorFlow and Theano, and tuning recommendations for PowerAI performance.

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Paulo Sergio Lemes Queiroz
Systems Consultant
IBM
PowerAI Deep Dive

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• What is PowerAI
• PowerAI Components
 Hardware requirements
 CPU VS GPU
 Volta / Tensors
• Using PowerAI Components
• Extending PowerAI
Session objectives

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What is PowerAI
• Set of support Libraries to
develop machine learning and
Deep learning applications

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Hardware Requirements
Minimal requirements for accelerated Machine Learning is:
- Any Power Server with a Nvidia GPU
IBM Power System S824L (with NVIDIA technology)
IBM Power System S822LC for High Performance Computing
IBM Power System S822LC for Big Data
IBM Power System S821LC
Key start point is have the GPU for acceleration
Nvlink enable even further acceleration
Nvlink usage is transparent

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CPU vs GPU
CPU is a generic core, wich has access to the system main memmory in order to do all kind of tasks
GPU is composed by thousands of specialized cores that handles mathematical operations
GPUs are specialized on Tensor / Matrix / Vector / Float point parallel operations
Sequential operations are slower on GPUS

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GPU limitations
GPU doesn't has access to the system main memmory, all data must be copied to the GPU and copied
back when processing is done ( batch like operations )
Non SIMD ( Same Instruction Multiple Datapoints ) operations are not THAT fast

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Volta / Tensors
Volta GPU will have dedicated Tensor function units, which will allow processing of Neural Networks to
run even faster
However this will require even bigger bandwidth ( and lower lattency ) between the main memory and
the GPU

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Using PowerAI Components
PowerAI, by default, require Ubunto 16.04 and it's install files can be located at:
https://public.dhe.ibm.com/software/server/POWER/Linux/mldl/ubuntu/README.html
The following components come along at the free package:
caffe-bvlc - Berkeley Vision and Learning Center (BVLC) upstream Caffe, v1.0.0
caffe-ibm - IBM Optimized version of BVLC Caffe, v1.0.0
caffe-nv - NVIDIA fork of Caffe, v0.15.14
chainer - Chainer, v1.23.0
digits - DIGITS, v5.0.0
tensorflow - Google TensorFlow, v1.1.0
ddl-tensorflow - Distributed Deep Learning custom operator for TensorFlow
theano - Theano, v0.9.0
torch - Torch, v7
It's important to highlight that PowerAI isn't limited by these pre-compiled libraries or versions
As on any Ubuntu machine, the user can compile any software

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Using PowerAI Components
All software components of PowerAI are installed at /opt/DL
root@pq-s824l-kvm:/opt/DL# ls -l
total 0
drwxr-xr-x 4 root root 29 Jun 23 16:08 bazel
lrwxrwxrwx 1 root root 23 Jun 23 16:39 caffe -> /etc/alternatives/caffe
drwxr-xr-x 13 root root 151 Jun 23 16:38 caffe-bvlc
drwxr-xr-x 13 root root 151 Jun 23 16:38 caffe-ibm
drwxr-xr-x 13 root root 151 Jun 23 16:38 caffe-nv
drwxr-xr-x 7 root root 68 Jun 23 16:38 chainer
drwxr-xr-x 4 root root 28 Aug 8 14:21 ddl
drwxr-xr-x 6 root root 55 Aug 8 14:21 ddl-tensorflow
drwxr-xr-x 8 root root 210 Aug 6 16:47 digits
drwxr-xr-x 7 root root 67 Jun 23 16:38 nccl
drwxr-xr-x 6 root root 54 Jun 23 16:38 openblas
drwxr-xr-x 3 root root 44 Aug 8 14:19 repo
drwxr-xr-x 5 root root 54 Jun 23 16:10 tensorflow
drwxr-xr-x 6 root root 54 Jun 23 16:38 theano
drwxr-xr-x 9 root root 94 Jun 23 16:38 torch
root@pq-s824l-kvm:/opt/DL# pwd
/opt/DL
root@pq-s824l-kvm:/opt/DL#

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Using PowerAI Components
By default these packages are not in the system PATH, however to enable those to exist at the PATH a
few helper scripts can be used:
. /opt/DL/tensorflow/bin/tensorflow-activate
. /opt/DL/theano/bin/theano-activate
export PATH="${PATH}:/opt/DL/bazel/bin"

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Tuning recommendation
Change the CPU govenor to performance, in order to avoid performance fluctuations
for i in ./devices/system/cpu/cpufreq/policy* ; do echo performance > $i/scaling_governor ; done
cpupower -c all frequency-set -g performance
Enable Persistent memory mode for the GPU:
nvidia-smi -pm ENABLED

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Submit a survey at:
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