2018 bsc power9 and power ai

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

2

IBM and AI
• IBM Cognitive Systems SPGI team
• The History of IBM and A.I.
• AI, Machine Learning and Deep Learning
• IBM current proposal for Deep Learning
• HW, SW & Storage for AI workloads
• What brings the near future

IBM Cognitive Systems SPGI team
4
Ander Ochoa
Architect
Ander.ochoa.gilo2@ibm.com
Carmen Recio
Data scientist
Carmen.recio.valcarce@ibm.com
Cristina Gil
Storage
Cristina.gil.ortiz@ibm.com
Any sufficiently advanced technology is indistinguishable from magic.
“Hazards of prophecy: the failure of imagination” (Arthur C. Clarke)
Ivan Peacock
Technical Leader
ivan_peacock@uk.ibm.com
Hardware
• POWER
• Mainframe
• …
Software
• PowerAI
• DSX
• …
Services
• PoCs
• Projects
• …
Jordi Caubet
HPC
jordi.caubet@es.ibm.com

 Deep Blue
 Blue Geen
 Playstation 3
 Watson
The History of IBM and A.I.
Cluster of PS3s, U.S.A.F. 2,000 Console Supercomputer
Image courtesy: U.S. Air Force
IBM Deep Blue
1997 - chess
playing computer
defeated Kasparov
A Blue Gene/P supercomputer at Argonne National
Laboratory
Watson, the computer program that crushed the human competition on the
“Jeopardy!” quiz .

HPC, Artificial Intelligence, Machine Learning, Deep Learning
Artificial
Intelligence
Deep Blue
Machine
Learning
Video/Audio
Regressions
Support Vector
Machines
Clustering
H.P.C. (High Performance Computing)
Optimization
Deep
Learning
(Neural Networks)

Very Computing intensive
Algorithm
Raw
data
Info
• New paradigm of how computers interact with us.
• So far traditional computing has been deterministic:
Pre training Training Inference
Process
Pretrained
Data Set
A.I.
framework
A.I.
Algorithm
Infered
data Responses
Raw
data
Accuracy!
Machine learning process
Raw
data
Raw
data
Big Data
Uses 80% time
Big Data

Algorithm
Millions of photos
Inferenced data
In your Data Center
12
In the field
Process
Pretrained
data
A.I.
framework
A.I.
Algorithm
Infered
data
Responses
Raw
data
Video streams
Deep learning process example
How
Autonomous
Cars are created

Tesla Autopilot 2.0 hardware for inference – Nvidia Drive PX 2

Pre training Training Inference
Process
Pretrained
data
A.I.
framework
A.I.
Algorithm
Infered
data
Responses
Raw
data
Raw
data
Uses 80% time
Hardware
 Power Systems
 Mainframe
 Storage
Software
 Platfom computing
 DataBases
 ETLs
 Data Mining Tools
 DSX
 PowerAI Vision
Services
 Data Miners
 Data Scientists
Hardware
 Special Power Systems
(AC922)
 NVMe Storage
Software
 PowerAI
 PowerAI Vision
 Platfom computing
Services
 Data Scientists
 3rd Party Special A.I. Teams
Hardware
 Special Power Systems
(Newell)
 NorthGate
 3rd Party products (Nvidia
Jetson TXn)
 NVMe Storage
Software
 PowerAI
Services
 Data Scientists/Customer
SPECIAL HW needed
IBM Offering play ground

Open Source Workloads
Now hyper-focused on expanding Cognitive/AI industry
applications
Enterprise Support/Subscription model
OpenPOWER
300 OpenPOWER members contribute to 87 OpenPOWER
ready products and 17 servers, delivering choice to industry
Close partnership with major AI/accelerator industry leader
Nvidia
OpenCAPI
High bandwidth open interconnect to attach to
accelerators and SCM
Open Frameworks
Highly optimized & accelerated Cognitive/AI frameworks
Cognitive/AI SDK for deployment and deployment tools
IBM Offering context

What is our current base offering?
https://goo.gl/ZtS81g
https://public.dhe.ibm.com/software/server/POWER/Linux/mldl/ubuntu/

19
Transform &
Prep
Data (ETL)
AI Infrastructure Stack
Applications
AI APIs
(Eg: Watson)
In-House APIs
Machine & Deep Learning
Libraries & Frameworks
Distributed Computing
Data Lake & Data Stores
Segment Specific:
Finance, Retail, Healthcare
Speech, Vision,
NLP, Sentiment
TensorFlow, Caffe,
SparkML
Spark, MPI
Hadoop HDFS,
NoSQL DBs
Accelerated
Infrastructure
Accelerated Servers Storage
PowerAI

Simplifies adoption of DL
Fast time to deploy a DL
environment
Simplified Installation
Precompiled DL libraries & frameworks
Optimized performance:
IBM Sytems unique capabilities
Distributed DL
Large Model Support
Designed for Enterprise
deployments
Enterprise-ready software distribution for DL and AI

Servers Roadmap

Operator Interface
• 1 USB 3.0
• Power Button
• Service LED’s
4X - Cooling Fans
• Counter- Rotating
• Hot swap
• 80mm
Memory DIMM’s (16x)
• 8 DDR4 IS DIMMs per socket
Power 9 Processor (2x)
• 190W & 250W
BMC (Service Processor Card)
• IPMI
• 2x 1 Gb Ethernet
• 1 VGA
• 1 USB 3.0
PCIe slot (4x)
• Gen4 PCIe
• 2, x16 HHHL Adapter
• 1, x8,x8 Shared HHHL Adapter
• 1 x4 HHHL Adapter
NVidia Volta GPU
• 2 per socket
• SXM2 form factor
• 300W
• NVLink 2.0
• Air Cooled
Power Supplies (2x)
• 2200W
• Configuration limits for redundancy
• Hot Swap
• 200VAC, 277VAC, 400VDC input
Storage
• Optional 2x SFF SATA Disk
• Optional 2x SFF SATA SSD
• Disk are tray based for hot swap
Note: Front Bezel removed
22
High level System Overview
 2-Socket, 2U Packaging
 40 P9 Processor cores
 2/4/6 NVIDIA Volta 2.0 GPUs
 2 TB Memory (16x - 64GB DIMMs)
 4 PCIe Gen4 Slots
 2x SFF (HDD/SSD), SATA, Up to 7.7 TB storage
 Supports 1.6TB and 3.2TB NVMe Adapters
 Redundant Hot Swap Power Supplies and Fans
 Default 3 year 9x5 warranty, 100% CRU
AC922 server
Newell - POWER9 with increased GPU
and IO bandwidth for differentiation

https://www.nvidia.com/en-us/data-center/tesla-v100/
Volta architecture fits in the same power
envelope and form factor as the previous Pascal
generation GPU with 1.5x the memory
performance, 2x the NVLink performance,
and 7.8 teraflops of FP64 processing (15.7
teraflops at FP32) on the GPU (CUDA) cores
and a total of 125 teraflops of processing
performance with the tensor cores.
The GPU

25
32-140+GB/s
64GB/s

NVMe Storage
Network technologies as
transport
OP OP
SCSI / SAS /
SATA
IB ETH
OP
SCSI / SAS /
SATA
FC
SCSI / SAS /
SATA
TCP
IP
HPC LPC
L1
L3
L2
Storage
1 Instruction queues
32/256 command
depth

Evolution
NVMe Storage
Network technologies as
transport
OP OP
SCSI / SAS /
SATA
IB ETH
OP
SCSI / SAS /
SATA
FC
SCSI / SAS /
SATA
TCP
IP
HPC LPC
L1
L3
L2
Storage
NVMe NVMeNVMe
64K Instruction
Queues
64K Instructions
supported, each

NVMe benefits
• NVMe provides much greater bandwidth than SAS and SATA
SATA SAS NVMe
Throughput 6Gbps 12Gbps >120Gbps
(in PCIe v3)
Command
Queue dephts
32 256 64.000
Queues 1 1 64000
• Lower latency
• Lower power consumption

TPU2 = Tensor
Processing Unit
Accelerates neural network
computations
IBM TrueNorth
Neuromorphic CMOS integrated circuit

TrueNorth neuropsynaptic chip
http://www.research.ibm.com/articles/brain-chip.shtml
TrueNorth, once loaded with a neural network model, can be used in real-time as a sensory
streaming inference engine, performing rapid and accurate classifications while using minimal
energy. TrueNorth’s 1 million neurons consume only 70 mW, which is like having a neurosynaptic
supercomputer the size of a postage stamp that can run on a smartphone battery for a week.

Where do we go from here: Summit & Sierra
The United States Department of Energy together with Oak Ridge National
Laboratory and Lawrence Livermore National Laboratory have contracted IBM and
Nvidia to build two supercomputers, the Summit and the Sierra, that will be based
on POWER9 processors coupled with Nvidia's Volta GPUs. These systems are
slated to go online in 2018
http://www.teratec.eu/actu/calcul/Nvidia_Coral_White_Paper_Final_3_1.pdf
IBM Summit #1 !!
IBM Sierra #3 !!

33

Part of Mare Nostrum 4
• 3 Racks
• 54 Power9 Systems
• 54 AC922 servers
• 4x Nvidia V100
• 512 GB RAM
• 6.4 TB NVMe storage
• 1.48 Pflops !
BSC Mare Nostrum 4

THANK YOU!

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2018 bsc power9 and power ai

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2018 bsc power9 and power ai