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Jim McHugh, VP & GM of NVIDIATodd Mostak, CEO of MapD
Scale, Speed and Scope: Why Telcos are Turning to GPU-Powered Analytics
February 2017
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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
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The Shifting Landscape for Service Providers
GROWTH OF BUSINESS SERVICES
NETWORK CONVERGENCE
CONTINUED CONSOLIDATION
DATA EXPLOSION,NOT REVENUE
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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
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Driving Complex, Value-Laden Use Cases
Rationalize and prioritize infrastructure investment
Customer Experience Management (Customer 360)
Operational Analytics
Network Optimization
Data monetization
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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 timeconstrains 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/
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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
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2008 2009 2010 2011 2012 2013 2014 20160.0
1.0
2.0
3.0
4.0
5.0
6.0
NVIDIA GPU x86 CPU
TFLO
PS
M2090
M1060
K20
K80
K40
Fast GPU+
Strong CPU
P100
The GPU Accelerated Data Center
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GPU Accelerated Analytics
Accelerated analytics, everywhere, every platform
TESLAServers in every shape and size
DGX-1The accelerated
analytics supercomputer for instant productivity
CLOUDEverywhere
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NVIDIA DGX-1
Accelerated analytics supercomputer-in-a-box
170 TFLOPS | 8x Tesla P100 16GB | NVLink Hybrid Cube Mesh2x Xeon | 8 TB RAID 0 | Quad IB 100Gbps, Dual 10GbE | 3U — 3200W
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NVIDIA and MapD for Accelerated Analytics
IMMERSIVE VISUALIZATIONPETABYTE 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
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MapD: software optimized for the fastest hardware
SPEED OF THOUGHT VISUALIZATION100X 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
+
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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.
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Data Lake/Data Warehouse/SOR
Performance Starts with Memory Management
SSD or NVRAM STORAGE (L3)250GB to 20TB1-2 GB/sec
CPU RAM (L2)32GB to 3TB70-120 GB/sec
GPU RAM (L1)24GB to 384GB3000-5000 GB/sec
Hot Data Speedup = 1500x to 5000xOver Cold Data
Warm DataSpeedup = 35x to 120xOver Cold Data
Cold Data
COMPUTELAYER
STORAGELAYER
SP
EE
D IN
CR
EA
SE
S SIZE
INC
RE
AS
ES
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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.
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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).
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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.
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Where MapD Sits
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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
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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
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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
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DEMONSTRATION
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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
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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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Thank You