Artificial Intelligence (AI) and Machine Learning (ML) are becoming mainstream initiatives at many organizations. Data is at the heart of AI and ML. Immediate access to large data sets is pivotal to successful ML outcomes. Without data, there is no learning. The goal of AI and ML is to try to simulate human thinking and understanding. AI and ML initiatives cannot however be realized unless the data processing layer has immediate access to, and a constant supply of, data. The problem is that NAS solutions, often those designed for HPC environments, is what most organizations try to leverage as the AI/ML storage architectures. Legacy storage systems, like NAS, cannot support AI and ML workloads, because they were architected when spinning disk and slower networking technologies were the industry standard. Join Storage Switzerland and WekaIO for our on demand webinar to learn the three reasons why NAS is no good for AI and ML: * NAS wasn’t architected to leverage today’s flash technology and can’t keep pace with the I/O demands, leaving GPUs starved for data * NAS has no or very rudimentary Cloud Integration. Tiering to the cloud can play an integral role in AI and ML workloads * NAS data protection schemes are expensive given the amount of data required to feed an AI/ML environment

1. Three Reasons Why NAS is No Good
for AI and Machine Learning
● NAS can’t leverage today’s flash technology
● NAS has no or very rudimentary cloud integration
● NAS data protection schemes are expensive
Watch and learn why:
For audio playback and Q&A go to: bit.ly/NASAIML

2. OurSpeakers
Sean Kerr, Product
Manager - HPC/AI Storage
Solutions, HPE
George Crump,
Founder and Lead Analyst of
Storage Switzerland
Barbara Murphy,
VP of Marketing,
WekaIO

3. What is
Artificial Intelligence (AI)
and Machine Learning (ML)?

4. Top Mainstream Use
Cases for AI and ML
● Autonomous Vehicles
● Fraud and Theft Detection
● Customer Service (ChatBots)
● Logistic Management
● Cyber-Security
● Smart Cities

5. The Data
Requirements for
AI and ML
● Extreme High-Performance IO
● Low Latency
● Adept at High File Count
● Massive Scalability
(Capacity and Performance)
● Multi-Location Processing
(Multiple Data Centers and Cloud)

6. AI and ML
Tends to
“Sneak Up”
on Core IT
● Starts as a pilot or skunk-works project
● Uses existing storage resources or cloud
storage
● Moves into production, breaking
traditional storage
● IT tries to upgrade/expand traditional
NAS
○ The project gets costly and can’t meet
requirements

7. 3 Reasons Why NAS
Won’t Work for AI/ML

8. NAS is a 90’S
Technology
● Not architected to
leverage flash
● Compute architecture has
changed (GPUs + CPUs)
● NAS has to deliver high
performance

9. NAS Doesn’t Know Cloud
● Today’s NAS (file systems) have rudimentary cloud
support
○ Typically replication for DR
● AI/ML Requires
○ Successful AI outcomes require keeping the
compute layer saturated with data
● Seamless movement of data to the cloud for
processing
○ Recall data (the results) from the cloud for
processing
○ Archive data to the cloud for long-term storage

10. NAS Data
Protection is
Expensive
● Impacts performance
○ Especially during failed-state
● Costly
○ Requires too much resource overhead
(compute and capacity)
● Slow
○ Return to good-state requires either time
or a lot of CPU

11. ● Software not Hardware
● High Performance
○ Leverage Flash
○ Leverage NVMe
IT Needs a Modern Approach
● More of a Fabric than a File
System
○ Data moves seamlessly
between locations and cloud
○ Exploits cloud tiers and
compute
● Data Protection Designed for
AI/ML

12. 11© 2018 All rights reserved.
How WekaIO Solves the AI
Challenge
Barbara Murphy
VP of Marketing
July 25, 2018 | 9:00 a.m. PDT

13. 12© 2018 All rights reserved.
NFS = Not For Speed
GPU Bandwidth
11GBytes/sec/IB
connection
NFS Bandwidth
1 - 1.5GBytes/sec
o A protocol invented in 1984 trying to solve a 2018 problem
o pNFS tried to fix NFS but failed when metadata workloads exploded
o Legacy parallel file systems like Lustre and GPFS cannot handle billions of
small files
– And they are complex to operate

14. 13© 2018 All rights reserved.
The Power of Parallelism
o Modern networks on 100Gbit Ethernet are 100x faster than SSD
o Everything can be distributed
o With NVMe-oF, shared storage is faster than local storage

15. 14© 2018 All rights reserved.
WekaIO Solves the Data Accessibility Problem
o Shared, POSIX compliant Parallel file system written for NVMe
o Scales to trillions of files, billions in a single directory
o Simple to install and use
GPU Bandwidth
4-11GBytes/sec
Per GPU Server
11GBytes/sec

16. 15© 2018 All rights reserved.
WekaIO Matrix: Full-featured and Flexible
WekaIO Matrix Shared File System
Fully Coherent POSIX File System That is Faster than Local File System
Distributed Coding, More Resilient at Scale, Fast Rebuilds, End-to-End DP
Instantaneous Snapshots, Clones, Tiering to S3, Partial File Rehydration
InfiniBand or Ethernet, Converged or Dedicated Storage Server
HPE AWS Cloud

17. 16© 2018 All rights reserved.
WekaIO is Faster than Local NVMe Drives
Testing conducted on WekaIO/HPE reference platform
• Inference benchmarks are I/O Bound
• Single Client Performance on HPE Apollo 6500
• 8 NVIDIA V100 GPUs
• WekaIO Matrix on 8x HPE DL360
• Local NVMe FS is I/O Bound
http://dlpg.labs.hpe.com report 10 and 11

18. 17© 2018 All rights reserved.
Results From Deep Learning Use Case
Actual measured data to the AI/ML GPU Cluster
WekaIO is over 10x Faster than NFS All Flash
NAS
Initial tests
Production

19. HPE & WekaIO
an Engineered Solution
Sean Kerr

20. WekaIO Matrix – Solution Building Block
19a00045311enw
Apollo 2000 Gen10
ProLiant DL360
Gen10
Supported Storage Systems for
WekaIO Matrix
Apollo 6500 Gen10
Mellanox Switch
WekaIO with
Apollo 2000

21. Thank you!
Storage Switzerland
http://www.storageswiss.com
georgeacrump@storageswiss.com
StorageSwiss on Twitter:
http://twitter.com/storageswiss
StorageSwiss on YouTube:
http://www.youtube.com/user/storageswiss
WekaIO
https://www.weka.io
info@weka.io
WekaIO on Twitter: @Wekaio
Barbara Murphy on Twitter: @scaleoutlady
WekaIO on LinkedIN:
https://www.linkedin.com/company/weka-io
HPE
https://www.hpe.com
HPE on Twitter:
https://twitter.com/hpe_hpc
https://twitter.com/HPE_Servers
HPE on Facebook:
https://www.facebook.com/HPEServers/

22. Three Reasons Why NAS is No Good for AI and Machine Learning
For complete audio and Q&A please register for the On Demand Version:
bit.ly/NASAIML