Real-time Analytics Powered by GPU-Accelerated Databases. Chris Prendergast and Woody Christy GTC, May 8, 2017

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1 Real-time Analytics Powered by GPU-Accelerated Databases Chris Prendergast and Woody Christy GTC, May 8, 2017

2 Kinetica Background United States Army Intelligence seeks a means to assess terrorist and other national security threats. GPUdb goes live with the US Army Intelligence. Commercialization entered production with USPS. No database in the market was fast or flexible enough to met their needs Rebranded to Kinetica. Seed funding. Moved HQ to San Francisco. Expanded management team. Hired field team. Founders Amit Vij and Nima Negahban start on the pioneering use of GPUs while building a GPU-accelerated database from the ground up. Patent granted for Method and system for improving computational concurrency using a multi-threaded GPU calculation engine Wins IDC HPC Innovation Excellence Award for work with US Army. Wins IDC HPC Innovation Excellence Award for work with US Postal Service. 2

3 Evolution of Analytics GPU Acceleration Simple Reporting Standard Analytics Real-time Analytics Machine Learning Deep Learning List customer energy consumption in the past 3 years What is the average consumption by region monthly? Per household? Residential vs. Commercial? What is the current energy consumption by a region / household? How does that compare to historic averages? How does it compare to other regions? Given location, history, demographic,, usage, what is the likelihood of service issues/outage? Deduce from unspecified signals across a wide range of datasets the likelihood this customer will consume more/less energy? Have service interruption? 3

4 GPU Acceleration Overcomes Processing Bottlenecks GPUs are designed around thousands of small, efficient cores that are well suited to performing repeated similar instructions in parallel. This makes them well-suited to the compute-intensive workloads required of large data sets. 4,000+ cores per device in many cases, versus 16 to 32 cores per typical CPU-based device. Parallel processing is ideal for scanning entire dataset & brute force compute. High performance computing trend to using GPU s to solve massive processing challenges GPU acceleration brings high performance compute to commodity hardware 4

5 Kinetica: A Distributed, In-Memory Database GPU-accelerated database operations Natural language processing based full-text search Native GIS and IPaddress object support Real time data handlers to ingest structured and unstructured data No typical tuning, indexing, and tweaking Predictable scale out for data ingestion and querying Deep integration with open source and commercial frameworks and applications: Hadoop, Spark, NiFi, Accumulo, H20, Tableau, Kibana and Caravel Distributed visualization pipeline built in 5

6 Kinetica: Unique Strengths & Capabilities Fast, Distributed, OLAP Engine for Fast Moving, Large Scale Data OLAP Performance, Fast Scalability, Data Stability Converged AI and BI Native Geospatial and Visualization Pipeline Interactive Geospatial Location-Based Processing & Visualization Analytics API for GPU Powered Data Analytics & Compute Orchestration In-Database 6

7 Challenges with Lambda and Kappa Architectures What is the main problem? Database or Cache system serving up pre-computed aggregates It also takes a lot of effort to re-compute aggregates and to load the serving database or cache 7

8 CASE STUDY Performance BI LARGE TELCO Query 1 : Simple average calculation on the 1.8B row table 0.09s Leading Enterprise Database 0.65s 345s Query 2 : Sum aggregation with a subquery aggregation joining both tables 2.5s Leading Enterprise Database 0.68s 44s 8

9 Real-Time, Advanced Analytics, Speed Layer for Teradata or Oracle Parallel ingestion of events Lambda-type architecture for Teradata or Oracle Kinetica is speed layer with real-time analytic capabilities for millisecond SLAs Converge Machine Learning, Deep Learning, NLP, streaming and location analytics and fast Query, Reporting & Analytics with Kinetica & Teradata/Oracle DATA IN MOTION AND REST Amazon Kinesis Kinetica Connectors STREAM / ETL PROCESSING Fast GPU accelerated, in- Memory Database Converge ML, DL, Streaming, Location, and QR&A MOBILE USERS ANALYSTS DASHBOARDS & APPLICATIONS ALERTING SYSTEMS DATA WAREHOUSE / TRANSACTIONAL 9

10 Speed Layer for Hadoop Parallel ingestion of events Kinetica is speed layer with realtime analytic capabilities Amazon Kinesis Put, get, scan ANALYSTS HDFS for archival store Much looser coupling than traditional lambda architecture Batch mode Spark or MR jobs can push data to Kinetica as needed for fast query on data loaded from HDFS EVENTS MESSAGE BROKERS Kinetica Connectors STREAM PROCESSING Parallel Ingestion Execute complex analytics on the fly MOBILE USERS DASHBOARDS & APPLICATIONS ALERTING SYSTEMS HDFS (Hadoop Distributed File System) 10

11 SIMPLIFY YOUR ARCHITECTURE No need to regularly recompute aggregates. No need to load and manage a separate serving system or cache to make deep historical aggregates available to your stream processing code. Aggregates are always up to date, as they are computed on demand; the latest events are always included Better performance with significantly reduced operational complexity, hardware footprint and cost.

12 STREAMING ANALYTICS, SIMPLIFIED Amazon Kinesis PUT, GET, SCAN ANALYSTS MOBILE USERS ALERTING SYSTEMS EVENTS MESSAGE BROKERS Kinetica Connectors STREAM PROCESSING Execute complex analytics on the fly DASHBOARDS & APPLICATIONS

13 CASE STUDY : LOCATION BASED ANALYTICS INTELLIGENCE: US Army - INSCOM US Army s in-memory computational engine for any data with a geospatial or temporal attribute for a major joint cloud initiative within the Intelligence Community (IC ITE). U.S Army INSCOM Shift from Oracle to GPUdb Intel analysts are able to conduct near real-time analytics and fuse SIGINT, ISR, and GEOINT streaming big data feeds and visualize in a web browser. First time in history military analysts are able to query and visualize billions to trillions of near realtime objects in a production environment. Major executive military and congressional visibility. GPUdb (20ms) 42x Lower Space 28x Lower Cost 38x Lower Power Cost Oracle Spatial (92 Minutes) 1 GPUdb server vs 42 servers with Oracle 10gR2 (2011)

14 CASE STUDY : LOCATION BASED ANALYTICS LOGISTICS: Route optimization USPS is the single largest logistic entity in the country, moving more individual items in four hours than the combination of UPS, FedEx, and DHL move all year. DISTRIBUTED ANALYSIS 15,000 simultaneous sessions AT SCALE 200,000 USPS devices emitting location each minute à 250+ million events captured and analyzed daily... tracked on 10 nodes.

15 CASE STUDY : LOCATION BASED ANALYTICS PREDICTIVE INFRASTRUCTURE MANAGEMENT LARGE UTILITY COMPANY Kinetica operates as a speed-layer with ESRI to monitor, manage, and predict infrastructure health. 15

16 CASE STUDY : LOCATION BASED ANALYTICS LOGISTICS & FLEET MANAGEMENT LARGE RETAILER Kinetica enables agile tracking of shipments to assist store managers for tracking of inventory and arrival times. Visibility and tracking of deliveries & trucks for store managers ETA & Notifications Provide estimated time of delivery, notifications and custom location based alerting Route Optimization based on truck size, and if cargo is perishable or contains hazardous materials. 16

17 CASE STUDY : LOCATION BASED ANALYTICS PIPELINE & WELL ANALYTICS ENERGY RESEARCH Kinetica enables interactive query and geospatial visualization of large numbers of upstream and midstream assets. Complex joins across several tables with 300m rows of data. Approx 100GB in size. Create custom visualizations, charts. Visualization of wells by land ownership, region, etc. 17

18 CASE STUDY : ADVANCED IN-DATABASE ANALYTICS LIFE SCIENCES : GENOMICS RESEARCH GPU-acceleration on Kinetica enables processing of transcriptomics to run simulations for drug research. Seeking out signals from massive collection of drug targets combined with historical data. Accelerate simulations of chemical reactions. In-database processing to develop models, leveraging GPU acceleration for performance, and direct access to CUDA APIs via UDFs deployed within Kinetica. 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 18

19 CASE STUDY : ADVANCED IN-DATABASE ANALYTICS RISK MANAGEMENT MULTINATIONAL BANK Large financial institution moves counterparty risk analysis from overnight to real-time. Data collected by XVA library which computes risk metrics for each trade Risk computations are becoming more complex and computationally heavy. xva analysis needs to project years into the future. Kinetica enables banks to move from batch/overnight analysis to a streaming/real-time system for flexible real-time monitoring by traders, auditors and management. 19

20 CASE STUDY Faster Analytics on Inventory and Sales LARGE RETAILER Query 1 : Sum of retail sales grouped by region 0.65s Enterprise In-Mem DB 0.65s 34s Query 2 : Sum of inventory available grouped by type 0.68s Enterprise In-Mem DB 0.68s 44s 20

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