Few industries are as metrics driven as telco and fewer still are as data rich. Everything in the telco world creates data: voice, video, customer calls, SIM cards, cell towers, network switches, even the trucks they drive produce telematics. The challenge is that the growth of data rich services coupled with industry consolidation has created working sets that have overwhelmed the capacity of these organizations to evaluate their datasets in full - so they downsample, index and aggregate to avoid paying millions to scale out CPU solutions that were not designed for these types of tasks. Enter the GPU and GPU-powered analytics. These massively parallel solutions turn minutes into milliseconds and provide full view of the operational landscape - not slivers.

1. Jim McHugh, VP & GM of NVIDIA
Todd Mostak, CEO of MapD
Scale, Speed and Scope:
Why Telcos are Turning to
GPU-Powered Analytics
February 2017

2. 22
AGENDA
The rapidly evolving telco business model
and its implications
Why current compute models are struggling
The path forward: GPUs and GPU-powered
analytics
How to enable speed at scale
Case Studies: How GPUs go to market in
the service provider world

3. 3
The Shifting Landscape for Service Providers
GROWTH OF
BUSINESS SERVICES
NETWORK
CONVERGENCE
CONTINUED
CONSOLIDATION
DATA EXPLOSION,
NOT REVENUE

4. 4
Not Just Data Volume, Data Types
Satisfaction:
network, call,
service
Usage:
data, browser, type,
time spent, text
Network: speed,
latency, signal
strength, network type
Location:
GPS, wifi estimation,
accelerometer
Customer:
Age, family size, gender,
brand preference, behavior
Hardware:
Handset, chipset, RAM,
screen size, SIM card,
software version

5. 5
Driving Complex, Value-Laden Use Cases
Rationalize and prioritize infrastructure investment
Customer Experience Management (Customer 360)
Operational Analytics
Network Optimization
Data monetization

6. 6
Issuing iterative queries
becomes wearisome.
As little as 500ms reduces
interaction and limits the
amount of data covered.2
Analyst creativity
is impaired.
For large scale data problems,
potential avenues of
exploration are ignored
because the time cost is too
high to even consider.3
Slow Compute – The Bottleneck
Long response time
constrains questions asked.
Over time this behavior
hardens.1
1. http://engineroom.ft.com/2016/04/04/a-faster-ft-cåom/
2. http://go.mapd.com/rs/116-GLR-105/images/2014-Latency-InfoVis.pdf
3. https://www.microsoft.com/en-us/research/publication/trust-me-im-partially-right-incremental-visualization-lets-analysts-explore-large-datasets-faster/

7. 7
Pre-aggregation
struggles at scale
Scale out on CPU
infrastructure has
tremendous hidden costs
Sampling misses
the whole picture
Workarounds Create Additional Problems
$
EXPLORE THE
OUTLIERS
AND LONG-TAIL
EVENTS
RELY ON
ACCURATE DATA
SCALE WITH A ROI

8. 8
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2008 2009 2010 2011 2012 2013 2014 2016
NVIDIA GPU x86 CPU
TFLOPS
M2090
M1060
K20
K80
K40
Fast GPU
+
Strong CPU
P100
The GPU Accelerated Data Center

9. 9
GPU Accelerated Analytics
Accelerated analytics, everywhere, every platform
TESLA
Servers in every
shape and size
DGX-1
The accelerated
analytics supercomputer
for instant productivity
CLOUD
Everywhere

10. 10
NVIDIA DGX-1
Accelerated analytics supercomputer-in-a-box
170 TFLOPS | 8x Tesla P100 16GB | NVLink Hybrid Cube Mesh
2x Xeon | 8 TB RAID 0 | Quad IB 100Gbps, Dual 10GbE | 3U — 3200W

11. 11
NVIDIA and MapD for Accelerated Analytics
IMMERSIVE
VISUALIZATION
PETABYTE SCALEUNPARALLELED SPEED
Explore and discover
insights in milliseconds
with world’s fastest data
exploration platform
Dynamically interact and
visualize billions of data
points in milliseconds
Instantaneously visualize
and query multi-billion row
datasets across multiple
high density nodes

12. 12
MapD: software optimized for the fastest hardware
SPEED OF THOUGHT
VISUALIZATION
100X FASTER QUERIES
MapD Core
An in-memory, relational, column
store database powered by GPUs
MapD Immerse
A visual analytics engine that
leverages the speed + rendering
capabilities of MapD Core
+

13. 13
Proof Points
Noted DB blogger, Mark Litwintschik has
benchmarked MapD vs. major CPU systems
and found it to be between 74x to 3,500x faster
than CPU-powered databases.

14. 14
Data Lake/Data Warehouse/SOR
Performance Starts with Memory Management
SSD or NVRAM STORAGE (L3)
250GB to 20TB
1-2 GB/sec
CPU RAM (L2)
32GB to 3TB
70-120 GB/sec
GPU RAM (L1)
24GB to 384GB
3000-5000 GB/sec
Hot Data
Speedup = 1500x to 5000x
Over Cold Data
Warm Data
Speedup = 35x to 120x
Over Cold Data
Cold Data
COMPUTE
LAYER
STORAGE
LAYER
SPEEDINCREASES
SIZEINCREASES

15. 15
Purpose Built + Highly Optimized
Query Compilation Engine creates one custom function
that runs at speeds approaching hand-written functions.
LLVM enables generic targeting of different architectures
+ run simultaneously on CPU/GPU.

16. 16
Purpose Built + Highly Optimized
Backend Rendering — Data goes from compute (CUDA)
to graphics (OpenGL) pipeline without copy and comes
back as compressed PNG (~100 KB) rather than raw
data (> 1GB).

17. 17
Purpose Built + Highly Optimized
Streaming — Speed eliminates need to pre-index or
aggregate data. Compute resides on GPUs freeing CPUs
to parse + ingest. Finally, newest data can be combined
with billions of rows of “near historical” data.

18. 18
Where MapD Sits

19. 19
Verizon
IMPACTCHALLENGE
Over the air (OTA) technology
is the primary way wireless
companies manage
subscribers (via SIM cards).
OTA polling + pushes create
massive data files.
Verizon’s legacy CPU powered
database did not allow real-
time queries – so they down
sampled to reduce time…. but
they sensed they were not
getting the whole picture.
The down sampling required
by CPU-era solutions was
missing key outliers.
Finding those outliers was
worth millions.
Additionally, ease of use drove
higher utilization thus more
informed decision-making.
All at a fraction of the cost.
Using MapD’s GPU-powered
database + visual analytics
engine, Verizon was able to
execute queries against the
entire SIM card population.
Further, Verizon was able to
query + visualize streaming
data + near historical data – for
the entire country or an
individual card.
SOLUTION

20. 20
Major US Cable Operator
IMPACTCHALLENGE
The business services team was
stuck with hardware and software
that only enabled them to look at
1x1 mile blocks.
Each new block required long wait
times — taking minutes to load.
As a result, there was no adjacent
discovery and they struggled to
optimize marketing around
capabilities and infrastructure.
The solution has completely
altered the company’s approach
for business services, resulting in
operational efficiencies
(discovered some contractors
“inspecting” the same property
10+ times), targeting marketing
more effectively (based on
capacity utilization based
marketing) and campaign
analytics.
Using GPU-powered visual
analytics from MapD and
NVIDIA the operator was able
to see their entire footprint –
eliminating the need to go
section by section.
Furthermore, they retained full
grain level detail on every
customer for when they
zoomed into a building or
residence.
SOLUTION

21. 21
Major Wireless Provider
IMPACTCHALLENGE
Client subscribed to third party
performance data to prioritize
what to upgrade to create
better coverage map claims.
Their current CPU-era
infrastructure only allowed
them to see 3% of the data in
any given region.
Using less HW the telco was
able to determine,
instantaneously, what
infrastructure projects would
yield the best ROI from a
coverage map perspective –
while improving customer
experience and reducing
dropped calls.
Using NVIDIA GPUs and
MapD the telco was able to
see and interact with their
national footprint – not a
neighborhood.
Zoom, cross-filter etc. all work
in real time.
SOLUTION

22. 22
DEMONSTRATION

23. 23
For More Information
/ Twitter: @NVIDIADC, @JimMcHugh
/ DGX for Accelerated Analytics:
www.nvidia.com/analytics
/ DGX for Deep Learning:
www.nvidia.com/dgx1
/ Twitter: @MapD, @ToddMostak
/ Product Overview:
www.mapd.com/products
/ Demos: www.mapd.com/demos

24. 24
May 8 - 11, 2017 | Silicon Valley | #GTC17
www.gputechconf.com
CONNECT
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experts from NVIDIA and
other leading organizations
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hands-on training through
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DISCOVER
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innovations from startups
Don’t miss the world’s most important event for GPU developers
May 8 – 11, 2017 in Silicon Valley: MapD in booth #621
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25. Thank You