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Explaining GPUs to Your
CEO
The Power of Productization
Dan Woods, CTO and Editor, CITO Research | Amit Vij, CEO and Co-Fo...
Dan Woods
CTO and Editor, CITO Research
Dan Woods is CTO and Editor of CITO Research, a firm that focuses on the membrane ...
Why is hardware hot again?
3
1995
PC Internet
2005
Mobile Cloud
2015
AI and IoT
Pervasive use of
GPUS
Massively parallel c...
Act 1
The End of Moore’s Law
Amit Vij | CEO | Kinetica
Life After Moore’s Law
40 Years of Microprocessor Trend Data
1980 1990 2000 2010 2020
102
103
104
105
106
107
Single-threa...
Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, a...
GPU Acceleration Overcomes Processing Bottleneck
5,000+ cores per device
versus ~16 cores per
typical CPU
High performance...
GPU-Accelerated, Distributed, Scale-out Architecture
8
+
+
+
On Demand Scale-out
• CPUs geared more for sequential process...
Act 2
What the heck are GPUs anyways?
9
Act 2: What the Heck are GPUs Anyway?
10
Each pixel of a
display was assigned
a simple processor,
not as complex an
instru...
Act 3
Vectors Heart GPUs
11
Act 3: Vectors Heart GPUs
12
It turns out that
AI and Machine
Learning algorithms
love GPUs.
Many deep learning
and ML alg...
Act 4
The Power of Integrated GPUs
13
Act 4: The Power of Integrated GPUs
The power of GPUs has been well
understood in high tech circles for
a while.
Even befo...
GPU-Based Systems
15
Make processing and analysis of
big data an interactive process
that leads to a much faster cycle
to ...
The Big Reveal: Implications for CEOs
16
CEOs should understand
the value of breaking through
those bottlenecks.
Understan...
Act 5
Use Cases and Case Studies
17
Accelerated Business Intelligence
18
Tableau + Kinetica
Kinetica combines GPU’s brute-force compute with the
simplicity of...
Distributed Location-Based Analytics
19
NATIVE VISUALIZATION IS DESIGNED FOR FAST MOVING, LOCATION-BASED DATA
Native Geosp...
In-Database Machine Learning
ETL / STREAM
PROCESSING
ON DEMAND SCALE OUT +
1TB MEM / 2 GPU CARDS
SQL
Native
APIs
PARALLELI...
Customer Case Studies
ENTERTAINMENT | Customer 360
22
CASE STUDY : BI ACCELERATION
BUSINESS OBJECTIVE
• Accelerate Tableau dashboards for faster...
One of the things I like about
Kinetica is it gives us more of a
general-purpose use of the
technology. There has been a l...
LOGISTICS | Workforce optimization
BUSINESS OBJECTIVE
• Deliver better business services, optimize operations, and save
co...
Download the O’Reilly Book
Kinetica.com/ebook
info@kinetica.com
Contact: dwoods@EvolvedMedia.com
Follow Dan: @danwoodsearl...
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Webinar Slides: Explaining GPUs to Your CEO

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Discussions about the future of data and artificial intelligence, from the Internet of Things (IoT) to predictive analytics, generally focus on potential to reshape how we live and how products perform for the better for accurately predicting cataclysmic failures or business problems far faster than ever imagined. But all this potential will remain theoretical if enterprises do not have the processing power to analyze all the data at their disposal. When trying to integrate real-time or streaming data into their BI and AI platforms, many organizations are experiencing crashes due to the limits of their current processing capabilities. In other words, the expansion in data must be accompanied by an expansion of the capacity to process it.
To extract timely meaning form data in the future, many companies will have to change, adapt, or move on from their current technologies. GPU databases are one of the best ways for enterprises to get full utility from streaming data in real time and to converge big data analytics with machine learning AI workloads in a single platform.

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Webinar Slides: Explaining GPUs to Your CEO

  1. 1. Explaining GPUs to Your CEO The Power of Productization Dan Woods, CTO and Editor, CITO Research | Amit Vij, CEO and Co-Founder, Kinetica November 9, 2017
  2. 2. Dan Woods CTO and Editor, CITO Research Dan Woods is CTO and Editor of CITO Research, a firm that focuses on the membrane between the world of IT and the hotbeds of advanced technology around the world. Dan’s goal is to help CIOs and CTOs who lead the application of technology in organizations of all sizes become better leaders. Dan helps vendors of technology understand how to adapt their technology to the world of IT. Dan has written more than 25 books, most recently APIs: A Strategy Guide, published by O’Reilly Media, and is a regular contributor to Forbes. Amit Vij CEO and Co-Founder, Kinetica Amit is responsible for the vision, administrative, and executive decisions for the company. With a background in computer engineering, he has over a decade of software development experience in the commercial and federal space, with an emphasis in analyzing and visualizing big data, and helped architect Kinetica. Amit served as the chief GEOINT technical architect as a contractor for a major classified cloud initiative between the US Army, NSA, and the DIA. Prior to Kinetica, Amit had been chief architect and a subject matter expert on geospatial intelligence for several Department of Defense and Department of Homeland Security contracts as. Amit received a B.S. in Computer Engineering from the University of Maryland with concentrations in Computer Science, Electrical Engineering, and Mathematics. Presenter Bios 2
  3. 3. Why is hardware hot again? 3 1995 PC Internet 2005 Mobile Cloud 2015 AI and IoT Pervasive use of GPUS Massively parallel computing needed to tackle data tsunami and advanced analytics needs on a single database platform
  4. 4. Act 1 The End of Moore’s Law Amit Vij | CEO | Kinetica
  5. 5. Life After Moore’s Law 40 Years of Microprocessor Trend Data 1980 1990 2000 2010 2020 102 103 104 105 106 107 Single-threaded perf 1.5X per year 1.1X per year Transistors (thousands) 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 5
  6. 6. 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 1980 1990 2000 2010 2020 102 103 104 105 106 107 Single-threaded perf 1.5X per year 1.1X per year GPU-Computing perf 1.5X per year 1000X By 2025 Rise of GPU Computing 6
  7. 7. GPU Acceleration Overcomes Processing Bottleneck 5,000+ cores per device versus ~16 cores per typical CPU High performance computing trend to using GPU’s to solve massive processing challenges GPU acceleration brings high performance compute to commodity hardware Parallel processing is ideal for scanning entire dataset & brute force compute 77
  8. 8. GPU-Accelerated, Distributed, Scale-out Architecture 8 + + + On Demand Scale-out • CPUs geared more for sequential processing • GPUs geared more for parallel processing • CPUs and GPUs are paired together for the best overall optimization
  9. 9. Act 2 What the heck are GPUs anyways? 9
  10. 10. Act 2: What the Heck are GPUs Anyway? 10 Each pixel of a display was assigned a simple processor, not as complex an instruction set as a CPU, but enough to control all the pixels in a massively parallel form. We have this architecture to thank for sharp and responsive displays. NVIDIA created an API to allow GPUs to be used for other types of computing. The CUDA API This opened up a powerful new source of computing for particular types of applications. GPUs were developed for making displays more powerful.
  11. 11. Act 3 Vectors Heart GPUs 11
  12. 12. Act 3: Vectors Heart GPUs 12 It turns out that AI and Machine Learning algorithms love GPUs. Many deep learning and ML algorithms involve processing vectors that are matrices of numbers. 3 5 10 4 GPUs add and multiply vectors faster than any other method−a lot faster. Many of the victories in AI such as Alpha GO and Image net were achieved through algorithms powered by GPUs. The victories were also powered by open source, the availability of massive pools of on-demand computing, the availability of data.
  13. 13. Act 4 The Power of Integrated GPUs 13
  14. 14. Act 4: The Power of Integrated GPUs The power of GPUs has been well understood in high tech circles for a while. Even before the recent wave of AI victories, companies have been figuring out how to harness GPUs for a broader enterprise workloads, not just for AI. There is a fascinating evolution going on right now as different ways of packaging up the power of GPUs are being developed. The opportunity for the enterprise is to harness the power of GPUs to accelerate analysis. The challenge vendors face is how to use GPUs to make traditional workloads faster and power AI workloads. In most companies using big data, the focus has been on batch use because processing has been bound by CPUs. 14
  15. 15. GPU-Based Systems 15 Make processing and analysis of big data an interactive process that leads to a much faster cycle to create insights. Package and make AI models widely available. Allow big data analytics and AI insights to be available in time to matter for real-time business processes. Absorb and make use of streaming data faster than other methods.
  16. 16. The Big Reveal: Implications for CEOs 16 CEOs should understand the value of breaking through those bottlenecks. Understand the Value CEOs can easily determine if GPUs matter by attempting experiments and POCs. Attempt Experiments CEOs should understand when their use of data and analytics is running into bottlenecks. Find the Bottleneck
  17. 17. Act 5 Use Cases and Case Studies 17
  18. 18. Accelerated Business Intelligence 18 Tableau + Kinetica Kinetica combines GPU’s brute-force compute with the simplicity of a relational database for millisecond query response on massive data sets without extensive tuning. • Incredibly fast query performance. • Distributed design - ideal for large and streaming datasets. • SQL-92 compliant relational database – without limits. • More power means less need for tuning, indexing, and administration of the database. • No need to do pre-aggregation or build out cubes. • Reduce reliance on specialized skills to prep and set-up data.
  19. 19. Distributed Location-Based Analytics 19 NATIVE VISUALIZATION IS DESIGNED FOR FAST MOVING, LOCATION-BASED DATA Native Geospatial Object Types • Points, Shapes, Tracks, Labels Native Geospatial Functions • Filters (by area, by series, by geometry, etc.) • Aggregation (histograms) • Geofencing - triggers • Video generation (based on dates/times) Generate Map Overlay Imagery (via WMS) • Rasterize points • Style based on attributes (class-break) • Heat maps
  20. 20. In-Database Machine Learning ETL / STREAM PROCESSING ON DEMAND SCALE OUT + 1TB MEM / 2 GPU CARDS SQL Native APIs PARALLELINGEST Geospatial WMS Custom Connectors In-Database Processing CUSTOM LOGIC BIDMach MLLibs BI DASHBOARDS BI / GIS / APPS CUSTOM APPS & GEOSPATIAL KINETICA ‘REVEAL’ STREAMINGDATAERP/CRM/ TRANSACTIONALDATA UDFs 20
  21. 21. Customer Case Studies
  22. 22. ENTERTAINMENT | Customer 360 22 CASE STUDY : BI ACCELERATION BUSINESS OBJECTIVE • Accelerate Tableau dashboards for faster customer 360 analytics NEW CAPABILITIES DELIVERED • 24X faster dashboard loads • 3.5X faster slice and dice, drilldowns, filters SOLUTION OVERVIEW • Tableau Server and Kinetica running on Google Cloud Platform • Kinetica accelerates EDW workload • Simply point to Kinetica using Tableau’s replace data source feature 5s Load Dashboard 4s Kinetica Update Customer Filters 120s 15s Teradata 4s 5s
  23. 23. One of the things I like about Kinetica is it gives us more of a general-purpose use of the technology. There has been a lot of software created to answer certain questions [but] highly specialized tools have limited functionality and are tuned to do a certain workload. " Mark Ramsey, Chief Data Officer at GSK BUSINESS OBJECTIVE • Faster processing of transcriptomics to run simulations of chemical reactions for drug discovery, research, and development NEW CAPABILITIES DELIVERED • In-database processing to develop models, leveraging GPU acceleration for performance, and direct access to CUDA APIs via UDFs deployed within Kinetica • Seek out signals from massive collection of drug targets combined from external data, historical data from experiments, ad clinical trials SOLUTION OVERVIEW • Kinetica running on-premises on a cluster of 7 HPE DL 380 servers • Familiar relational database with GPU acceleration LIFE SCIENCES : GENOMICS RESEARCH CASE STUDY : ADVANCED IN-DATABASE ANALYTICS 23
  24. 24. LOGISTICS | Workforce optimization BUSINESS OBJECTIVE • Deliver better business services, optimize operations, and save costs across 600,000 employees, 215,000 delivery vehicles, and deliver 500 million pieces of mail daily NEW CAPABILITIES DELIVERED • Real-time delivery and pickup notifications, shipment routing, just-in-time supplies • Real-time route optimization - route planning, rerouting • Geospatial analytics to uncover overlapping coverage areas, uncovered areas, and distribution bottlenecks • Advanced workload optimization for last minute route changes SOLUTION OVERVIEW • Kinetica collects, processes, and analyzes 200,000 messages per minute for real-time streaming analytics. 15,000 daily sessions with 5 9’s uptime 24
  25. 25. Download the O’Reilly Book Kinetica.com/ebook info@kinetica.com Contact: dwoods@EvolvedMedia.com Follow Dan: @danwoodsearly Work with Dan: www.CITOResearch.com: Finding Technology for Early Adopters (Research and IT Consulting) www.EvolvedMedia.com, Helping B2B Tech vendors find customers through content marketing.

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