Joint Dell and NVIDIA presentation delivered to various healthcare and life sciences firms

1. Digital Transformation Through Data Analytics
AI Powered Transformation
Healthcare, Life Sciences, and Research
Dell / NVIDIA AI Roadshow
Bill Wong – Dell Technologies Artificial Intelligence and Data Analytics Practice Leader
Adam Shubinsky – NVIDIA Data Science & High Performance Compute Solutions Leader
John Armstrong – Dell Technologies Healthcare & Federal Government Director

2. Agenda
 Key Business Challenges and Trends
 Dell Technologies AI Strategy in Healthcare
 Industry Trends
 AI Partner Ecosystem
 NVIDIA Update
 AI Transformation Challenges
 NVIDIA Infrastructure Solutions
 Summary
 Dell NVIDIA Partnership

3. COVID-19 and the New Reality
Public Healthcare
 Social Distancing, Self-Isolation, Quarantine,
Lockdown
 COVID-19 Assessment Centres
 Non-COVID elective surgeries being deferred
 Increased inspections planned for Long-Term
Care institutions
Labour
 In the province of Ontario, numerous
hospitals operate at 120.0% capacity.
 Need for more front-line workers
Funding
 Short-term economic aid announced for all industry
sectors
 Long-term federal funding increases (as a % of
GNP) being assessed
 $52.4M announced for COVID research (April 2,
2020)
 Non-COVID research experiencing significant
declines in funding
Healthcare Supply Chain
 Medicine, PPE, and Medical Equipment shortages
 B.C. government is giving itself the authority to take
over supply chains for delivering essential goods
and services throughout the province (Mar 26,
2020)

4. Today’s Business Trends
Trend #1: Remote working will be a prevalent way of working
Trend #2: Organizations across industries will increasingly rely
on digital platforms/channels to increase future resilience and
growth
Trend #3: Data and analytics become essential in assisting
faster and better decision-making

5. Interest
Plateau of Productivity
Peak of Path of EnlightenmentTechnology Trigger Trough of Disillusionment
Exaggerated Expectations
Digital Health Hype Cycle
Time
www.healthcare.digital
2020
Panomics
Neural Implants
Technical Wellness
DNA Infused Jewelry
Social Isolation Technology
Health Monitoring Tattoo’s
Carbon Dioxide Bracelets
Sony mSafety Watch
Smart Fabric Health Glove
Google’s Deepmind’s Sideways
AI Powered Predictive Healthcare
Facebook’s Preventive Health Tool
Robotic Process Automation
Haptic Technology
Longevity and Age Technology
Swallowable Technology
Digital Twin
Biohacking
Blockchain in Healthcare
IBM Watson Health
Asynchronous Messaging
Personal Health Records
Remote IOT Monitoring
Conversational AI
Machine Learning
Remote Testing
Online Symptom Checkers
Image Recognition
Online Therapy

6. AI/ML/DL is the fastest growing Datacenter workload
Worldwide AI Spending
~$98 Billion by 2023
Overall CAGR = 28.5%
• H/W CAGR=24.1%
• S/W CAGR=36.7%
• Services CAGR=25.9%

7. Improve Patient
Experience and
outcome by
leveraging IoMT
devices for real-
time monitoring
Improve
Healthcare
Professional’s
Experience and
ability to diagnose
and perform
clinical operations
Optimize
healthcare costs
by reducing costs
associated with
Patient Length of
Stay
AI Healthcare and Research Drivers
Improve Patient
Outcome with
precision
medicine
technologies and
AR/VR to assist
practioners
Accelerate
Research and
Discovery by
advancing the
technology
infrastructure

8. Analytical-driven Applications for Healthcare
Working Toward the Next Normal
Internet of Medical
Things
Hospital Operations Population Health
Management
Precision Medicine
Transformation
• Flu Season Prediction
• Contact Tracing
• Public Health
Surveillance Systems
• Animal disease
surveillance
• Chatbots and
Intelligent Virtual
Assistants
• Virtual /
Augmented Reality
• Medical Imaging
and Diagnostics
• Emergency
Response
Intelligence
• Precision Oncology
• Clinical Genomics
• Drug Discovery
• Personalized Drug
Matching
• Patient Health
Monitoring and
Diagnosis
• Telemedicine and
Remote Patient
Monitoring
• Virtual Home System

9. Healthcare / Life Sciences Data Lake
Supporting Healthcare, Life Sciences and Research Analytics
Consumption
Zone /
Data Analytics
Raw /
Landing/
Secure Zone/
Data Ingestion
Research Papers,
Lab Reports
Social
Media
Internet of
Medical Things
Medical
Images
Self-Service Dashboards
Advanced Analytics
Clinicians
Consumer Dashboards
Operational Analytics
Data
Scientist
General
Public
Researchers
Data Governance | Security and Compliance
Enriched /
Discovery Zone /
Data
Transformation
Data Sources Common Services
Optimized Infrastructure for Advanced Analytics
Patient
Monitoring
Sensors
EMR
Personas
Tools /
Applications
Data Lake Capabilities
• Provide support for a variety of analytical applications, including self-service, operational, and data science analytics
• Data preparation and integration capabilities to ingest structured and unstructured data, move and transform raw data to
enriched data, and enable data access to for the target user base
• An infrastructure platform optimized for advanced analytics that can perform and scale

10. Customer and Employee Health and Safety Solutions
• Detection of persons/objects
• Display showing temperature differences accurate
to 0.1°C
• Alarm in case of exceeding or falling below defined
temperature ranges
• Event Triggers (alarm, network message, activation
of a switching output)
• Temperature range from -40 to +550 °C
•Face Redaction for privacy
Dell Workstation
with NVIDIA
Dell Technologies Surveillance Solutions
- Open Data Lake Platform
- Scalable Infrastructure
- Analytics-ready
Image, Video and Thermal-based AI Applications
Applications
- Fraud Detection
- Loss Prevention
- Workplace Accident Reduction
- Customer Insight
- Public Safety
- Counter Terrorism

11. Top 10 Types of Hardware for AI Delivery*
1. Processors (CPU, GPU, FPGA, ASIC)
2. HPC / Supercomputer Infrastructure
3. Communication Network
4. Personal Devices
5. Connected Home Devices
6. AR / VR Head-Mounted Displays (HMD)
7. Drones
8. Robotics
9. Automotive
10.Sensors and Application Components (audio, camera, LiDAR, etc.)
*The Business Impact and Use Cases for Artificial Intelligence, Gartner, 2017
Accelerate
computational
performance
AI-enabled endpoints
AI-enabled autonomous endpoints

12. AI Accelerators
Flexibility Efficiency
and many more…

13. Healthcare and Research Institutions Leveraging NVIDIA
Using a dataset of 6,000 images matched with expert
Diagnosing Retinopathy of Prematurity (ROP) diagnoses,
researchers trained the AI model to differentiate ROP
severity
Using NVIDIA to power GANs that create and validate
synthetic brain MRI images. Combining the
manufactured images with real MRI images enables
the team to train its neural network with 75% less
data.
Researchers created a GPU-powered AI hearing aid
prototype that monitors a wearer’s brainwaves, identifies
who the wearer wants to hear, then boosts the voice the
wearer wants to focus on within 2-3 seconds.
Built an AI model to read head CTs and identify acute
cases within seconds, predict future clinical events with
superhuman accuracy from ECG, assess patient care
variables and recommend optimal care teams.
“Deep Patient” developed on NVIDIA, can analyze a
patient’s medical history to predict nearly 80
diseases up to 1 year prior to onset.
Researchers have created an auto-annotation system
leveraging deep learning. The NIH research could lead to
the creation of a global library of datasets for medical
researchers.

14. Deep Learning Analytics – GPU, Graphcore
Dell Technologies – AI Compute Platforms
Performance
Inference
Data Analytics
Multi-App HPC / ML / DL
C6420pC6420p
R840
DS8440
8+
4
2 - 3
1
Solution Price $
C4140C4140
GPU DB Acceleration, AI/ML R940xa
SDS/VDI R740XDR740XD
1:1 CPU/GPU ratio
Highest density of
CPU and memory
with 2 GPUs
GRAPHCORE IPU
XILINUX FPGA INTEL FPGA
NVIDIA GPU
INTEL CPU AMD CPU
GRAPHCORE IPU
XILINUX FPGA INTEL FPGA
NVIDIA GPU
INTEL CPU AMD CPU

15. Bitfusion
Hypervisor
50%
vGPU
30%
vGPU
20%
vGPU
GPU
VSphere User Defined Virtualization VSphere Network Attached AI
Abstract Pooling, Sharing and Automating
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
NETWORK
GPUGPU GPU
GPU
GPU
GPU GPUGPU
CLOUD
GPU GPU
GPU GPU
GPU GPU
GPU
GPU GPUGPU GPU
GPU GPUGPU GPU
GPU GPU
Maximize UtilizationMaximize Efficiency
GPU Virtualization Economics

16. Combining AI with HPC to Deliver Faster Insights
AI
Predict Molecular
Structure
Produce a predicted 3D
protein structure of COVID-19
10³⁹ possible antibodies
narrowed down to 20
HPC Analytics
Protein Simulations
Simulation data of protein
interactions to reveal larger
scale interactions/patterns
Resulting in an improved
drug discovery pipeline
Lawrence Livermore National Laboratory

17. Dell EMC Data Science
Platform
Nauta ClaraAI KubeFlow
NVIDIA
EGX
Domino Cassandra HPCaaS Metropolis Spark Jupyter
Bright
Cluster
Manger
Dell-curated
Ansible/
Terraform
playbooks
CNI MetalLB CoreDNS Prometheus NFS provisioner
Helm
Kubernetes
Linux (RHEL/CentOS) + CRI (Docker/containers)
1 https://infohub.delltechnologies.com/section-assets/h18136-tco-analysis-dell-emc-hpc-ra-for-ai-da-sb
On-premises system for HPC, AI and Data Analytics
AI / Machine learning / Deep
learning
PowerSwitch S3148-ON
S5232F-ON cluster switch
PowerEdge R740
management and
compute nodes
PowerEdge C4140
acceleration nodes
DSS 8440 dense
acceleration nodes
Dell EMC Isilon
Dell EMC Ready Solution for HPC BeeGFS Storage
Dell EMC Ready Solution for HPC NFS Storage
© Copyright 2020 Dell Inc.
HPC AI Ready Architecture
One Platform for AI, Data Analytics, and Simulation Workloads
• Simplified operations and lower
cost while enabling new use
cases for users at the lowest
TCO1
• Allow HPC, DA & AI workloads
to execute on the same cluster;
reducing data movements for
faster results
• Run simulation & modeling,
analytics, visualization, and AI
workloads on a common HPC
infrastructure
Software ecosystem

18. AI Magic Quadrants
Data science and machine-learning platforms are defined as:
• A cohesive software application that offers a mixture of basic building blocks
essential both for creating many kinds of data science solution and incorporating
such solutions into business processes, surrounding infrastructure and products.
Cloud AI developer services are defined as:
• Cloud-hosted services/models that allow development teams to
leverage AI models via APIs without requiring deep data
science expertise
Data Science and Machine Learning Platforms Cloud AI Developer Services
The Marketplace
Continues To
Evolve

19. Healthcare / Data Sciences Data Lake Partner Ecosystem
Selected Platform Solutions and ISVs
Consumption
Zone /
Data Analytics
Raw /
Landing/
Secure Zone/
Data Ingestion
Research Papers,
Lab Reports
Social
Media
Internet of
Medical Things
Medical
Images
Clinicians
Data
Scientist
General
Public
Researchers
Data Governance | Security and Compliance
Enriched /
Discovery Zone /
Data
Transformation
Data Sources
Common Services
Optimized Infrastructure for Advanced Analytics
Patient
Monitoring
Sensors
EMR
Personas
Tools / Applications
*Note, some products can deliver capabilities that address multiple requirements

20. BlueDot quantifies the risk of exposure to infectious
diseases globally
Detects Outbreaks Anticipates Dispersion Anticipates Impact Empowers Response
of over 150 different
pathogens, toxins, and
syndromes in near-real
time.
Scans >100,000 official and
mass media sources in 65
languages per day
of disease, locally and
globally, using anonymous,
aggregated data on billions
of flight itineraries and
hundreds of millions of
mobile devices
of disease spread globally
and globally using diverse
datasets:
• Real-time climate conditions
• Health system capacity
• Animal, insect populations etc.
to mobilize timely, effective,
efficient, coordinated, and
measured responses to
epidemic threats
BlueDot published the
first scientific paper on
COVID-19, accurately
predicting its global
spread

21. How H2O.ai is Contributing to COVID-19
Expertise
H2O.ai’s data science experts
are contributing their
knowledge to solve pressing
problems with the pandemic
AI Platforms
H2O.ai is contributing its
Driverless AI and Q platform
to model, predict, and
visualize data sets
Sri Ambati
CEO and Founder, H2O.ai
1. Hospital staffing predictions
2. ICU transfers and triage
3. Population risk segmentation
4. Predicting the spread of COVID-19.
5. Predicting operational efficiency and
resilience during a pandemic
6. Hospital supply chain predictions
7. Predicting responses by city,
hospitals
8. Sepsis predictions
Problems we are solving
Data Sets
H2O.ai is evaluating global
and open health data sets to
determine patterns
“Data Science can save
lives today. AI is an
incredible force to do
good for humanity.”
AI Solutions
H2O.ai is creating pandemic
and health specific solutions
for general use

22. NVIDIA CLARA FRAMEWORKS AND LIBRARIES
Accelerating key healthcare domains
GENOMICSMEDICAL IMAGING SMART HOSPITALS
CLARA IMAGING
Clara Imaging is a
comprehensive application
framework that provides
developers with a complete
set of tools to build, manage,
and deploy intelligent
imaging workflows and
enable software-defined
instruments.
PARABRICKS
Clara Parabricks provides
enterprise- grade, turnkey,
GPU-accelerated sequencing
software, and a technology
stack for developers to build
HPC, deep learning, and data
analytics genomic
applications.
CLARA GUARDIAN
Clara Guardian is an application
framework that brings intelligent
video analytics and automatic
speech recognition to healthcare
delivery — simplifying the
development and deployment of
smart sensors for public safety,
improved patient care, and
enhanced operational efficiencies.

23. NVIDIA CLARA FRAMEWORKS AND LIBRARIES
Accelerating key healthcare domains
PRE-TRAINEDMODELS AIASSISTED
ANNOTATION
TRAINING
FRAMEWORK
DEPLOYMENT
FRAMEWORK

24. NVIDIA CLARA PARABRICKS
Fast, Accurate, and Scalable Genomics

25. NVIDIA CLARA GUARDIAN
An Application Framework for Smart Sensor Fusion

26. Decision Criteria for AI Infrastructure/Solutions
Data Scientist Perspective
IDC 2018

27. The Digital Future Demands a New Perspective
Cloud First Data First
Infrastructure-centric Business-centric
Takes into consideration:
• Data gravity
• Data velocity
• Data control
• Data privacy and compliance
Driven by:
• Lower infrastructure CapEx
• Offload infrastructure maintenance
• Improve time to market (deployment
time for infrastructure)
Evolve to a Data-Driven Business

28. • Design and build systems for HPC and
Deep Learning workloads
• Systems include compute, storage,
network, software, services, support
• Integration with factory, software, services
• Power and performance analysis, tuning,
best practices, trade-offs
• Focus on application performance
• Vertical solutions
• Research and proof of concept studies
• Publish white papers, blogs, conference
papers
• Access to the systems in the lab delltechnologies.com/innovationlab
Dell Technologies HPC and AI Innovation Lab

29. Dell Technologies for AI
AI Leadership
• Worldwide Market leader for AI infrastructure
• Platform of Choice for AI Applications and Research
• HPC AI Innovation Lab
• Local Expertise
Healthcare Leadership
• Healthcare Technology Solution Provider
• Price/Performance Leader
• Improve Time to Value
• Drive Innovation

30. NVIDIA DGX
ONE SYSTEM — MANY WORKLOADS

31. THE MARKET FORCES SHAPING COMPUTING
Breakdown
of Dennard
Scaling
Amdahl’s
Law
End of the
Line
1,000X
by 2025
1,000X
by 2025
1980 1990 2000 2010 2020
102102
103103
104104
105105
106106
107107
Single-threaded perfSingle-threaded perf
1.1X per year1.1X per year
GPU-Computing perf
1.5X per year
1.5X per year1.5X per year
ARTIFICIAL INTELLIGENCE
SCIENTIFIC COMPUTING
DATA ANALYTICS
Sources: A New Golden Age for Computer Architecture, by John L. Hennessy, David A. Patterson
Communications of the ACM, February 2019, Vol. 62 No. 2, Pages 48-60. 10.1145/3282307
https://cacm.acm.org/magazines/2019/2/234352-a-new-golden-age-for-computer-architecture/fulltext
Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K.
Olukotun, L. Hammond, and C. Batten New plot and data collected for 2010-2015 by K. Rupp

32. OVERCOMING DATA PROCESSING CHALLENGES
Meeting Data-Processing requirements, Today and Tomorrow
2030202020102000
Data Processing
Requirements
CPU
Data to
Analyze
GPU
3.03.0
Hadoop Era Spark Era Spark + GPU Era
Learn More - nvidia.com/spark-book
“These contributions lead to faster data
pipelines, model training and scoring for more
breakthroughs and insights with Apache Spark
3.0 and Databricks.”
— Matei Zaharia, original creator of Apache
Spark and chief technologist at Databricks
Spark 3.0 scale-out clusters are accelerated using
RAPIDS software and NVIDIA GPUs
GPU year-over-year performance increases to
meet and exceed data processing requirements

33. AI WORKLOADS: FROM TRAINING TO INFERENCE
Untrained
Neural Network
Model
Deep Learning
Framework
TRAINING
Learning a new capability
from existing data
Trained Model
New Capability
App or Service
Featuring
Capability
INFERENCE
Applying this capability
to new data
Trained
Model
Optimized for
Performance

34. THE KEY CHALLENGE: ACCELERATING BIG & SMALL
AI Advances Demand
Exponentially Higher Compute
AI Applications Demand
Distributed Pervasive Acceleration
3000X Higher Compute Required to Train
Largest Models Since Volta
Every AI Powered Interaction Needs
Varying Amount of Compute
AlexNet
ResNet
BERT
GPT-2
Megatron-GPT2
Turing NLG
Megatron-BERT
1E-03
1E-02
1E-01
1E+00
1E+01
1E+02
1E+03
2012 2013 2014 2015 20162017 2018 2019 20202021
Petaflop/s-Days
3000X AI Interactions Per Day
Source: OpenAI, NVIDIA

35. TODAY’s HYPERCONVERGED DATA CENTER
Impossible to Optimally Design Server Mix for Unpredictable Demand

36. SOLVING THE INFLEXIBILITY OF AI INFRASTRUCUTURE
Not Optimized, Complex to Manage, Difficult to Scale Predictably
Inflexible infrastructure that was never meant
for the pace of AI
Constrained workload placement by system-level
characteristics
Non-uniform performance across the data center
Unable to adapt to dynamic workload demands
Constrained capacity planning
TRAINING CLUSTER
ANALYTICS CLUSTER INFERENCE CLUSTER

37. CONSOLIDATING DIFFERENT WORKLOADS ON DGX a100
One Platform for Training, Inference and Data Analytics
TRT TRT TRT TRT TRT TRT TRT
TRT TRT TRT TRT TRTT TRT TRT
Instance 1 Instance 7
Instance 14Instance 8
2x A100s for inference in MIG mode
Data Analytics
Training
4x A100s
2x A100s

38. 38
ELASTIC AI INFRASTRUCTURE WITH DGX A100
DGX A100 with MIG Delivers New Agility for Today’s Enterprise Data Center
DGX A100 Infrastructure is Agile
DGX A100 infrastructure uses MIG to allocate GPU resources to workloads
TRAINING CLUSTER ANALYTICS CLUSTER
INFERENCE
CLUSTER
OVEROPTIMAL UNDER
Infrastructure silos starve AI workloads or waste capacity
ANALYTICS
INFERENCE
TRAINING
Toda
y
Tomorro
w
Next
Week
Traditional Infrastructure is Constrained

39. 39
DGX A100 LOWERS TCO WITH MAXIMIZED UTILIZATION
Legacy infrastructure is inflexible
Sits idle when demand drops, unable to scale
when demand increases
Nearly impossible to optimize utilization
Adapt to Changing Business Needs Without Reinvesting
BEFORE AFTER
DGX A100 is agile, outperforming legacy for every AI workload:
analytics, training, and inference
Adapts to business demand providing a single elastic
infrastructure that’s more efficient
Better utilization = lower TCO and faster ROI on AI
0
10
20
30
40
50
60
70
80
90
100
Training Cluster
Time
0
10
20
30
40
50
60
70
80
90
100
Combined Workloads on…
Time
Target utilizationTarget utilization

40. 40
TODAY’S AI
DATA CENTER
50 DGX-1 systems for AI
training
600 CPU systems for AI
inference
$11M
25 racks
630 kW

41. 41
5 DGX A100 systems
for AI training and
inference
$1M
1 rack
28 kW
1/10th
COST
1/20th
POWER
$1M 28 kW
DGX A100
DATA CENTER

42. THE NVIDIA POWERED INFRASTRUCTURE
Reducing costs, power-consumption, and server footprint
1/5th
THE COST
1/5th
THE COST
1/3rd
THE POWER
1/3rd
THE POWER
1/5th
THE COST
1/3rd
THE POWER
$10M 140 kW$10M$10M 140 kW140 kW
163 GB/s Throughput on RAPIDS Implementation of TPCx-BB
@ SF 10K
$2M | 16 DGX-1 | 2 Racks | 56 kW
Learn More - nvidia.com/spark-book
Equivalent 163 GB/s Throughput on TPCx-BB @ SF 10K
$10M | 167 2U CPU Systems | 11 Racks | 140 kW

43. 25 Years of Accelerating Computing
The NVIDIA philosophy: One systems architecture — many uses
X-FACTOR SPEED UP FULL STACK DATA-CENTER SCALE
GPU
CPU
DPU
ONE ARCHITECTURE

44. The Value of Dell for AI Infrastructure
- Comprehensive and Scalable AI/Analytics Platform Portfolio
- Workstations, Servers, Clusters, Storage, Networking
- Infrastructure and Data Science and Analytics Expertise
- HPC and AI Innovation Lab
- IoT / Intelligent Video Analytics Lab
- Solution-based Offerings
- Pre-configured AI Ready Offerings
- IoT / Safety and Security and
Thermal Vision Solutions
- GPU Virtualization
- ML Platforms
Infrastructure
Scalability
Reduce
Complexity
Address
Demand
Partner
Ecosystem
Cost
Effective

45. - Appendix -
Dell Technologies
AI and Data Analytics Solutions

46. Dell Technologies AI and Data Analytics Solutions
AI / Machine Learning / Deep Learning
• Domino Data Science Platform Design Document
• HPC for AI and Data Analytics Ready Architecture
• Retail Loss Prevention Ready Solutions
• DataRobot Reference Architecture
• H2O AI Reference Architecture
• Kubeflow Reference Architecture
• OneConvergence Dkube Reference Architecture
• Iguazio Reference Architecture
• Deep Learning with NVIDIA Ready Solutions
• Isilon with NVIDIA DGX-1 Reference Architecture
• Isilon with NVIDIA DGX-2 Reference Architecture
• Isilon with Dell Precision 7920 Data Science Workstation Reference Architecture
• Isilon with Dell EMC DSS8440 Reference Architecture
• Noodle.ai (OEM) Solution Bundle
IoT / Streaming / Machine Data Analytics
• IntelliSite (OEM) Thermal Detection Solution
• Retail Loss Prevention Ready Solutions
• Dell IoT Safety and Security Portfolio
• Real-Time Data Streaming Ready Architecture
• Splunk Enterprise on Dell EMC Infrastructure
• Streaming Data Platform
• ElasticSearch (OEM) Solution Bundle
© Copyright 2020 Dell Inc.
Augmented Analytics and Data Warehouse
• Spark on Kubernetes
• Kinetica (OEM) Solution Bundle
• ThoughtSpot (OEM) Solution Bundle
• Pivotal Greenplum
• Dell Boomi
Data Lake / Unstructured Data Infrastructure
• Microsoft SQL Server 2019: Big Data Cluster Ready Solution
• Cloudera Hadoop Ready Architecture
• Hortonworks Hadoop Ready Architecture
• Kubernetes Containers with Diamanti (OEM) Solution Bundle
• Grid Dynamics Reference Architecture
• Red Hat OpenShift Reference Architecture
HPC Ready Solutions
• HPC Digital Manufacturing
• HPC Life Sciences
• HPC Research
• HPC BeeGFS Storage
• HPC Lustre Storage
• HPC NFS Storage
• HPC PixStor Storage
*Note, some products can deliver capabilities that address multiple use cases
Product Offerings and Technical Collateral for Analytical Use Cases