Accelerating Computing - Enhance Big Data & in Memory Analytics. Michael Viray Product Manager, Power Systems

Transcription

1 Accelerating Computing - Enhance Big Data & in Memory Analytics Michael Viray Product Manager, Power Systems viraymv@ph.ibm.com ASEAN

2 Real-time Cognitive Analytics -- a Game Changer Deliver analytics in real-time at the point of the transaction Uncover new insights and understanding Predict what is going to happen

3 There s big time pressure to handle Big Data 10 billion Mobile connected devices in use quintillion Bytes of data created every day. 3 80% of data is unstructured and it is growing at 50-60% per year. 5 47% Increase in M-commerce sales 2014 Q2 year-over-year billion Daily internet transactions by % Growth in cloud deployment since

4 The Opportunities from Big Data are Infinite 1000X faster insights 68% less attrition among high-value customers 50% increase sales order capacity 3.5X less infrastructure 75% productivity improvement 10X storage space savings 140X faster queries Source(s): Fiserv (Case Study), NC State Univ (Video), Coca Cola (ebook, Video, Case Study), STO (Case Study), Dillards (Video), BCBS of Tenn (ebook), BCBS of Tenn (ebook), Fossil (based on customer internal benchmarks) 4

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7 Representative Watson Explorer Use Cases Explore Analyze Interpret Aerospace Manufacturer Enterprise-Wide Exploration Improves Operational Efficiencies Provides unified exploration and navigation for fast access to information across the enterprise: Placed 50 new aircraft into service during the first year without a staffing increase Saved USD 36m/yr in supporting the 24/7 aircraft-onground program Provided supply chain visibility to reduce cycle time, saving millions of dollars on critical parts deliveries Communications Service Provider Customer Analytics Reduce Customer Churn Analyze call center notes, surveys, and customer s to automatically detect likely candidates for customer churn: Improved accuracy to detect churn by 50% Improved rates for model and service upgrades to loyal customers Improved self-service FAQ system Healthcare Insurance Call Center 360 View of Customer Reduces Call Handle Time Agents provided faster access to siloed customer and product data saving millions of dollars annually: Improved productivity for 14,000 agents, saving 3 seconds of average call handling time Increased net promoter score as a result of better customer engagement 7

8 How are cognitive systems different? Can see, use and operationalize virtually all data Are not programmed but pose hypotheses based on data patterns and probability Can understand, reason, learn and interact with humans naturally

9 Cognitive businesses can access and use all types of data. Data you possess + Data outside your firewall + Customer records Transactional systems Predictive models Institutional expertise Operational systems News Events Social media Weather Geospatial information Data that s coming Internet of Things (IoT) Sensory data Images Video Structured and active Unstructured and dark

10 Price/Performance Fundamental forces are accelerating change in our industry x86 processor technology alone can no longer provide the price performance gains necessary to sustain Moore s Law Moore s Law IT consumption models are expanding to: Processor Technology Firmware / OS Accelerators Software Storage Network Scale-out & Hyperscale Data Centers Hybrid Cloud A better processor and full system stack open innovation required Open Solutions

11 An IT infrastructure that is built with open innovation to accelerate big data insights Relational Databases IBM BLU Acceleration Solution 82X faster analytics queries C1 C2 C3 C4 C5 C6 C7 C8 IBM POWER8 server Running AIX or Linux PowerVM Enterprise Edition DB2 Advanced Workgroup or Advanced Enterprise Edition Optionally InfoSphere DataStage NoSQL Databases IBM Data Engine for NoSQL 24X less server infrastructure IBM POWER8 S822L CAPI-Attached Nallatech FPGA Accelerator IBM FlashSystem 840 Ubuntu Linux Redis Software MapReduce IBM Data Engine for Analytics 3X less storage infrastructure POWER8 S822L servers Red Hat Linux BigInsights (Hadoop) & Streams Platform Computing Elastic Storage Server (GPFS) Mellanox Ethernet and InfiniBand Interconnects

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13 Power Systems with POWER8, built with open innovation to accelerate big data insights and hybrid cloud deployment 4X threads per core vs. x86 (up to 1536 threads per system) Processors flexible, fast execution of analytics algorithms 4X memory bandwidth vs. x86 1 (up to 16TB of memory) Memory large, fast workspace to maximize business insight 4X more cache vs. x86 2 (up to 800MB cache per socket) Cache ensure continuous data load for fast responses Continuous data load Massive IO bandwidth Parallel processing Flash for extreme performance Large-scale memory processing 1. Up to 4X depending on specific x86 and POWER8 servers being compared 2. Up to 4.9X more cache depending on specific x86 and POWER8 servers being compared.

14 Solusi247 Hadoop Benchmark Comparison Best Daily Query on Power System : 6 hour 33 min 16 sec Running on 7 nodes 12 core 256 GB Memory Best Daily Query on Intel System : ~7 hour Running on 14 nodes 12 core 256 GB Memory Same duration for query with half amount of node on Power Systems

15 IBM Power Systems with POWER8, designed to take on the most complex data challenges Power Scale-out servers are affordable, easy-to-deploy and energy efficient with superior cloud economics and security. Power Enterprise servers are designed for data and offer businesses the ultimate in resiliency, availability, security, and performance. Scale-out Systems 1 or 2 sockets Up to 24 cores Up to 192 threads Up to 2 TB memory Running AIX, IBM i, Linux Enterprise Systems* Up to 16 sockets Up to 192 cores Up to 1536 threads Up to 16 TB memory Running AIX, IBM i, Linux

16 IBM Power Systems S812LC optimized for entry and small Hadoop workloads Improve the agility and reduce the cost of running Spark and Hadoop workloads 16X the memory capacity of Xeon E3 servers, 2X 1P Xeon E5 servers Complete the same Spark workloads for <½ the cost of Intel Xeon E v3 systems 2.3X BETTER performance per dollar spent 94% more Spark workloads in the same rack space as Intel Xeon E v3 systems 1.94X BETTER performance per system (10 core S812LC vs 24 core DL380) 1-socket, 2U Up to 10 cores ( Ghz) 1 TB Memory (32 DIMMs) 115GB/sec memory bandwidth 14 LFF (HDD/SSD) 84TB storage 4 PCIe slots, 2 CAPI enabled Default 3 year 9x5 warranty, 100% CRU

17 Elastic Storage Server - A Data Lake for many applications Technical Computing Big Data & Analytics Cloud File GPFS NFS Hadoop BigSQL Traditional Analytics Block Object Cinder Swift POSIX 77.5 Percent of organizations are already investing in a Data Lake* *Evans Data: Big Data and Advanced Analytics Survey 2014 Enterprise storage on standard hardware Data Lake Structured Data Unstructured Data Elastic Storage Server Single Name Space Linear capacity & performance scale out 17 17

18 Compute Intensive Add More Servers Data Engine for Analytics offers Independent Scaling of Servers & Storage Add servers or storage or both as needed Adjust compute to storage ratio as workload needs change Standard Hadoop configs with local storage and triple replica can result in overprovisioned compute to meet the storage demands Data Engine for Analytics allows right sizing of compute and storage independently to create an optimized configuration Add More Storage Storage Intensive 18

19 The OpenPOWER Foundation Market Shifts New Open Innovation Moore s law no longer satisfies performance gain Growing workload demands Numerous IT consumption models Mature Open software ecosystem The OpenPOWER Foundation is an open ecosystem, using the POWER Architecture to serve the evolving needs of customers. Open Development open software, open hardware Collaboration of thought leaders simultaneous innovation, multiple disciplines Performance of POWER architecture amplified capability Members And growing. SYS ODM I/O I/O OEM Networking Chip Storage SoC Dev IP Dev Technology Acceleration FAB Rich software ecosystem Spectrum of power servers Multiple hardware options Derivative POWER chips FW Open Source WEB 2.0 Data Center SW MSP Linux Cloud ISV ISV Open Source 19

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