This document proposes a collaboration between IBM and the university to establish an AI, HPC, and cloud center of excellence. It outlines IBM's leadership in AI and the need for academic-industry partnerships and cross-disciplinary research. The proposed setup would include an IBM AC922 server with CPUs, GPUs, and storage to provide computing resources. It describes various use cases for how students, faculty, researchers could access and use the resources for classes, projects, and adding their own applications. It also outlines an initial 2-year timeline and discusses potential software offerings, courses, and opportunities for skills development, publications, and intellectual property.

1. Proposed
Collaboration with
your University
IBM® AI , HPC and Cloud
Center of Excellence

2. IBM is leading the way
IBM is teaming with universities, startups,
ISV’s and industries to help develop further
the impact of artificial intelligence for
solutions for real-world opportunities

3. 3
Background and Motivation
The IBM AI Lab will play a major role in the research and
development commercial and industrial development of
emerging AI technologies
There is a strong need for research and development activity
in these domains:
– Encouraging academic-industry partnerships
– Cross-disciplinary and collaborative research
– Making AI accessible to non-technical business students
– Enabling faculty-technologist interaction and learning
– Enabling startups , ISVs and industries to use the platform
to innovate in ways that improve the World condition
.

4. Technologies
and Partners
The AI Lab will include IBM
and other corporate
sponsors, coupled with open
source technologies to
accelerate results
4

5. 5
CoE Charter and Objectives
1. Conduct research on rapidly advancing AI technologies
2. Enable and facilitate industry-academia partnerships in research and
development, and foster relationships through collaborative projects
3. Encourage cross-disciplinary research in applied computing, in critical
scientific and industrial domains, via research proposal submissions to
funding agencies
4. Provide a state-of-the-art R&D facility for students, faculty and
collaborators
5. Offer a comprehensive and meaningful computing environment for
education by:
1. complementing the theoretical coursework in CC with appropriate laboratory
coursework for students, and
2. encouraging team participation and cross-disciplinary problem solving

6. IBM’s AI Lab
OpenPOWER System for
Data Analytics with
Accelerators (GPU)
Collaborative technical projects
Access to IBM Academic Initiative
Toolkit
Graduate, Ph.D. and Post-Doctoral
research
Webinars and Technical Workshops
Projects related to make smart cities
and smart villages

7. 7
Proposed AI cloud setup and specifications - Hardware
College Ethernet Network
4
4
College Ethernet Network
1 IBM AC922 System
128 GB Memory
2 TB Hard drive
40 Cores Power 9 Processor
NVLINK-2 nVidia GPUs – 4
2 Raptor POWER 9 based Talos Servers
1 x86 with 2 K80 Server for CFD
applications
1 EDR Infiniband swtich
16 TB Storage Sub System
IB ConnectX cards
Edge Compute devices
1 RACK ( Which can fit in )

8. The AC922 has 2 POWER9 sockets, each providing extreme levels of IO
and memory bandwidth. As an example, in the configuration proposed, each
socket will communicate with two Nvidia V100 GPUs directly utilizing the
300GB per second NVLink2 bus connections on each POWER9 socket. In
addition, sockets have high memory bandwidth, PCIe Gen4 bandwidth,
and a high bandwidth SMP interconnect.
8

9. 9
AI Lab users
AI Lab Software Components

10. University Use Cases and Scenarios of
Proposed AI Lab
AI Cloud at Universities

11. 11
Use Case 1 : Students (daily use) requests for compute resource
Basic ML/DL exercises
Login to web portal
with “Student”
profile; browse
service catalog.
Select and request
for desired image,
and usage period
eg. MS Office with
Windows for 2
hours.
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal AI Cloud
Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into
Docker Container
Application image
deployed into Docker
Container
Login info sent to
user via email
Docker Container
with PowerAI image f
Downloads
completed work
into laptop and logs
off.
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
Application and OS
images have to be
preconfigured by the
cloud admin before
use.

12. 12
Use Case 2 : Final Year Students requests for compute resource for
AI Projects
Login to webportal
with “FY” profile;
browse service
catalog.
Select and request
for desired image,
and usage period e
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal Cloud Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
Downloads
completed work
into laptop and logs
off.
Resources made available to
final year students will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
FY Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
IP address for VM
deployed within same
subnet. Students
access from laptop.
VM and Storage size :
2-4 cores, 4GB RAM, 10GB
storage
RHEL
Jetson Nano
VM for the FY student will be
operational until the expiration
date stated in his request.

13. 13
Use Case 3 : Final Year Students creates own application image,
and shares image with other FY students.
Student to seek
approval from
Cloud Admin to
create new app
image in cloud infra
New image is
displayed in the
service catalog
Other FY students
proceed to request,
access and use
new application (as
per Use Case 2)
Ai Cloud Admin AI Cloud
Infrastructure
To ensure proper cloud
operations, only the cloud
administrator is allowed to
manage image offereings in
the cloud.
FY Students
In order to allow other
FY students to have
access to the new
application image for
their own project, the
originator of the
application has to work
with the cloud admin to
package the app as an
image offering in the
cloud.
Cloud Portal
Provisioning
manager packages
app image with OS
Image is registered
with service
automation
manager and portal
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud

14. 14
During 2 hr class,
provides VM login
information to 40
students in class /
exam
Use Case 4 : Lecturers prebooking seats for AI/ML/DL class or exam
AI Cloud Portal AI Cloud Infrastructure
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
lecturer via email
VMs deprovisioned
back into the cloud
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Lecturers
VM and Storage size :
40 VMs
2 core, 4GB RAM, 5GB
storage
RHEL
PowerAI Vision
Watson Machine Learning
Accelerator
Lecturer proceed to
request for VMs
with “Lecturer”
profile.
Select and request
for desired image,
and future usage
period eg. 40 VMs
of SPSS with LInux
for 2 hours.
Students access
VMs from laptop /
PC / workstations
Students download
work at end of class
Application and OS
images have to be
preconfigured by the
cloud admin before
use.
IP address for VM
deployed within same
subnet.

15. 15
Use Case 5 : Researchers adding compute capacity with own
applications through the AI cloud
AI Cloud Portal Ai Cloud InfrastructureResearchers
Researchers proceed
to request, access VM
and install own
application (as per
Use Case 2)
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
VM and Storage size :
8 cores, 16GB RAM, 250GB
storage
RHEL

16. 2 Year Developmental Timeline
a) IBM POWER Academic Initiative
partnership
b) OpenPOWER system and
Accelerator for Deep Learning
and Machine Learning
c) Technical Projects deployment
d) Review of progress in technical
projects, lab coursework
e) Big data and AI curriculums

17. IBM Software
Offerings along
with the Servers
Software Overview
 IBM’s hardware offerings for HPC are enhanced when
combined with enterprise class software solutions. These
include Red
 Hat Enterprise Linux (RHEL), IBM Watson Machine Learning,
and IBM Spectrum Computing.
Red Hat
 The proposed solution includes Red Hat Enterprise Linux 7
(RHEL) with 5-year support on all compute and storage
nodes, RHEL and CentOS are highly compatible Linux
operating systems. Although support is available for both
operating systems on the IBM Power Systems AC922 server,
running RHEL on IBM Power provides clients with enterprise
grade Linux support.
 Red Hat is a leading provider of open-source solutions, and
IBM is one of the largest Linux contributors. RHEL 8 for
Power exploits the latest IBM POWER and virtualization
technologies to help maximize system resources and provide
high qualities of service to your end users. RHEL 7 enables
the following functions on POWER:
 Simultaneous multithreading
 Static micro threading
 Transactional memory
17

18. IBM Software
Offerings along
with the Servers
 IBM Watson Machine Learning CE) are available at
no charge.
 IBM Watson Machine Learning (formerly IBM
PowerAI)
 IBM Watson Machine Learning makes deep learning
and machine learning more accessible to your staff,
and the benefits of
 AI more obtainable to the University. It combines
popular open source deep learning frameworks,
efficient Artificial Intelligence development tools, and
accelerated IBM. Power Systems™ servers. With
IBM Watson Machine Learning, the University can
deploy a fully optimized and supported AI platform
that delivers blazing performance, along with proven
dependability and resilience.
18

19. HPC/AI Scheduler
19

20. IBM Solution for HPC and AI
20

21. AI Cloud (On
Premise)
PowerAI makes deep
learning, machine learning
and AI more accessible and
more performant
By combining this software
platform for deep learning with
IBM Power Systems,
enterprises and Institutions can
rapidly deploy a fully optimized
and supported platform for
machine learning frameworks
and their dependencies. And it
is built for easy and rapid
deployment
PowerAI runs on the IBM Power
System AC922 for High
Performance Computer server
infrastructure

22. Advantages for Your Faculty and
Students
 Talent and Skills: (Remote Interns; Skills and Training)
Students and Research scholars will start working on the
advanced technologies will enable them to work on
many applications
Publications and Mindshare: (Press releases, Articles,
and Publications; Conferences and Events)
1. Conference Paper on software-based application
research /development in 6 months
 Intellectual Capital: (Patents, Open source; Prototypes,
Demos; Curriculum; Student projects, Theses)
1. Prototype building of many research problems using
software-centric approach (hardware-centric baseline
implementation almost getting completed)
2. Potential to file disclosures
 Opportunities: (Seed revenue; Leverage other funding;
Build ecosystems; Build government/client relationships)
1. Once software-centric solution available with
comparable performance using latest technologies ,
your team would create prototypes which can be
demonstrated to several colleges

23. Special Courses
 Big Data with docker and Kubernetes
 Machine Learning with Python
 Data Science Course
 Exascale Computing Infra
 Quantum Computing Workshop
 Faculty Development programs 2
More than 100 hours of technology workshops

24. Processor Core Enablement and
Partnership
1. Introduction of open-ended experiments on A2I
Core in the FPGA Lab curriculum
2. Allotment of Mini Projects to students on
HDL/Verilog/ A2I Core
3. Global Remote Mentoring for students with our
mentors, who have desired FPGA coding skills
4. FDP for faculty on porting & integration of modules
for application design using A2I core
5. Discussion on the creation of data-path for the
development of softcore processor architecture
6. Joint research activities
7. Development of specific solutions for IBM as
sponsored projects / consultancy
8. Sharing of learning materials for A2I core and
relevant tool chain
24

25. Onstitute Platform and Wisconsin
Collaboration-Platteville
 By registering to Onstitute, the students can get the
following benefits:
 Learn a broad range of data science topics (e.g., big
data analytics, cloud computing, machine learning, deep
learning, etc.) and analytic software tools.
 Get access to cutting edge hardware infrastructure
(including supercomputing-level systems with multicore
CPU, multiple GPU, etc.) while learning.
 Exposure multiple job opportunities in data-science and
related field.
 Involve in real-world big data and AI projects together
with academia and industry leaders.
 Opportunities to participate in world-class
workshops/webinars and rewarding hackathons.
25

26. University of Oregon , E4S and TAU
Collaborations
E4S or the Extreme-scale Scientific Software Stack [https://e4s.io] is a community effort to provide open
source software packages for developing, deploying and running scientific applications on high-
performance computing (HPC) platforms. E4S provides from-source builds and containers of a broad
collection of HPC software packages. E4S exists to accelerate the development, deployment and use of
HPC software, lowering the barriers for HPC users.
 "TAU Performance System® [http://tau.uoregon.edu] available on
OpenPOWER:
– Profiling and tracing support with 3D profile browsers
– Support for IBM XL, GNU, and LLVM Clang compilers
– Support for PowerAI, Spectrum MPI, and MVAPICH2 GDR, CUDA,
OpenACC
– Multi-platform support in TAU
• IBM Power, Cray XC, ARM64, x86_64, NVIDIA CUPTI and AMD
GPUs (ROCm)
26

27. Ohio State University and xScale
solutions collaborations
27

28. 28
Ganesan Narayanasamy
ganesana@in.ibm.com
OpenPOWER leader in
Education and Research WW
IBM Systems
Thank
you!