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Scale, Speed and Scope: Why Telcos Are Turning to GPU-Powered Analytics

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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.

Published in: Data & Analytics
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Scale, Speed and Scope: Why Telcos Are Turning to GPU-Powered Analytics

  1. 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. 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. 3 The Shifting Landscape for Service Providers GROWTH OF BUSINESS SERVICES NETWORK CONVERGENCE CONTINUED CONSOLIDATION DATA EXPLOSION, NOT REVENUE
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18 Where MapD Sits
  19. 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. 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. 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. 22 DEMONSTRATION
  23. 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. 24 May 8 - 11, 2017 | Silicon Valley | #GTC17 www.gputechconf.com CONNECT Connect with technology experts from NVIDIA and other leading organizations LEARN Gain insight and valuable hands-on training through hundreds of sessions and research posters DISCOVER See how GPUs are creating amazing breakthroughs in important fields such as deep learning and AI INNOVATE Hear about disruptive 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 SAVE ADDITIONAL 20% OFF REGULAR RATES AT WWW.GPUTECHCONF.COM
  25. 25. Thank You

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