AWS for Semiconductor and Electronics Design | Hsinchu, April 10

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Join us in Hsinchu on Thursday, April 10, 2014 as we bring Taiwanese semiconductor and electronic systems companies and engineering users together with Electronic Design Automation (EDA) and Computer …

Join us in Hsinchu on Thursday, April 10, 2014 as we bring Taiwanese semiconductor and electronic systems companies and engineering users together with Electronic Design Automation (EDA) and Computer Aided Engineering (CAE) ISVs to learn about cloud deployments. Hear about successes in industries directly related to EDA/CAE, and consider your own POC and production projects with AWS best-practices.
“We are attending because we think the cloud is a compute model change that is of interest to our customers, which makes it of interest to Cadence. We’re looking forward to conversations with customers to understand their specific interests and timeframes. We appreciate the invitation from Amazon to attend. Amazon is clearly a leader in this space and we anticipate working together on many engagements.”
Larry Drenan, Services Director, Global Design Environment, Cadence
This free, half-day event will focus on commercial HPC use-cases and best practices for design, engineering, and manufacturing of semiconductors and electronic systems using AWS to achieve increased flexibility, reduced development costs, and faster time-to-market.

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  • 1. AWS for Semiconductor and Electronics Using Cloud for Design, Engineering, Manufacturing April 10, 2014 David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 2. Agenda 13:30 AWS Cloud forIT Enterprise – OpeningRemarks and Case Studies James Tien,Sales & Business Development Manager,Amazon Web Services 14:00 15:00 AWS Cloud for Design and Simulation – Case Studies in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 15:15 16:00 Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 16:00 16:45 MentorGraphics Design Collaboration Julian Sun,Business Development Director,Mentor Graphics David Pellerin,HPC Business Development Principal,Amazon Web Services 16:45 AWS Kinesis – Big Data Management and Analytics in Manufacturing Ken Chan,Solutions Architect,Amazon Web Services David Pellerin,HPC Business Development Principal,Amazon Web Services 17:15 ClosingRemarks and Q&A James Tien,Sales & Business Development Manager,Amazon Web Services
  • 3. AWS Semiconductor James Tien Sales and Marketing, Taiwan
  • 4. 8 Years Young Amazon Simple Storage Service (S3) launched: March 14th 2006
  • 5. Pace of Innovation In 2013: 280 new services, significant features and updates 24 48 61 82 159 280
  • 6. On average, AWS adds enough new server capacity every day to support Amazon’s global infrastructure when it was a $7B business.
  • 7. Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q2 2012 Q3 2012 Q4 2012 Q1 2013 Q2 2013 Q3 2013 Over 1,500,000 peak requests/sec Amazon Simple Storage Service (S3): Trillions of Total Objects
  • 8. 10 AWS Regions Worldwide 25 Availability Zones Tokyo Region Sydney Region Singapore Region China Region
  • 9. Global Content Delivery Network 51 Edge Locations Europe Amsterdam (2) Dublin Frankfurt (3) London (3) Madrid Marseille Milan Paris (2) Stockholm Warsaw Asia Chennai Hong Kong (2) Manila Mumbai Osaka Seoul Singapore (2) Sydney Taipei Tokyo (2) South America Sao Paulo Rio de Janeiro North America Ashburn, VA (3) Atlanta, GA Dallas, TX (2) Hayward, CA Jacksonville, FL Los Angeles, CA (2) Miami, FL Newark, NJ New York, NY (3) Palo Alto, CA Seattle, WA San Jose, CA South Bend, IN St. Louis, MO
  • 10. Compute Networking Storage & CDN Database App Services Management Amazon EC2 Amazon ELB AutoScaling Amazon WorkSpaces Amazon Route 53 Amazon VPC AWS Direct Connect Amazon S3 Amazon Glacier Amazon EBS AWS Storage Gateway AWS Import/Export Amazon CloudFront Amazon RDS Amazon DynamoDB Amazon Elasticache Amazon RedShift Amazon AppStream Amazon CloudSearch Amazon SWF Amazon SQS Amazon SNS Amazon SES Amazon Elastic Transcoder Mobile Push AWS IAM Amazon CloudWatch AWS Elastic Beanstalk AWS CloudFormation AWS OpsWorks AWS CloudHSM AWS CloudTrail AWS Trusted Advisor AWS Marketplace AWS Premium Support AWS Professional Services AWS Training Over 40 Broad & Deep Services to Support Virtually Any Cloud Workload Analytics AWS Data Pipeline Amazon Kinesis Amazon EMR
  • 11. Hundreds of Thousands of Customers in 190 Countries
  • 12. AWS Hong Kong and Taiwan Customers
  • 13. Media Sharing Explosive traffic accommodation Consumer social app Ticket pricing optimization SAP & Sharepoint Securities Trading Data Archiving Marketing campaign Marketing web site Interactive TV apps Fast development and deployment R&D data analysis Machine Learning system development Big data analytics Customized movie suggestion Disaster recovery Media streaming Web and mobile apps Streaming webcasts Facebook app Consumer social app Every Imaginable Use Case Global game service
  • 14. Why are customers adopting cloud computing? 15
  • 15. On-Premises Requires significant, up-front capital expense Pay As You Go $0 to get started 1. Trade Capital Expense for Variable Expense 16
  • 16. 2. Lower Total Cost of IT Scale allows us to constantly reduce our costs We are comfortable running a high volume, low margin business We pass the savings along to our customers in the form of low prices 42 Price Reductions
  • 17. Self Hosting Waste Customer Dissatisfaction Actual demand Predicted Demand Rigid Elastic Actual demand AWS 3. You Don’t Need to Guess Capacity 18
  • 18. 4. Dramatically Increase Speed and Agility Old World Infrastructure in Weeks Infrastructure in Minutes Add New Dev Environment Add New Production Environment Add New Environment in Japan Add 1,000 Servers Remove 1,000 servers Number of Instances 1,000 Instance Type M3 Extra Large Availability Zone US-West-2b Launch aws.amazon.com/managementconsole 19
  • 19. Experiment Often Fail quickly at a low cost More Innovation 4. Increase Agility when Innovation is Fast and Low Risk On-Premises Experiment Infrequently Failure is expensive Less Innovation 20 Nearly $0 $ Millions
  • 20. Data Centers Power Cooling Cabling Networking Racks Servers Storage Labor Capacity Planning Buy and install new hardware Setup and configure new software Build or upgrade data centers Repeat investments to go global Toil with scaling distributed systems Pay massive margins So you don’t have to … 5. Stop Spending $$$ on Undifferentiated Heavy Lifting We take care of it… 21
  • 21. 6. Go Global in Minutes 22
  • 22. AWS Cloud for Electronics and Semiconductor Introduction and Case Studies April 10, 2014 David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 23. Cloud for Scalable EDA • Technical capabilities • Business realities Cloud for Secure Global Collaboration • New, more innovative solutions for EDA users • New opportunities for EDA software vendors Cloud for Big Data Analytics • For manufacturing yield analytics • For improved Design-for-Manufacturing Themes: for Today and the Future
  • 24. Scalability: Go wide, go large for faster time- to-results at higher accuracy Global Collaboration: For enhanced IP security, more efficient operations Agility: React quickly to changing needs with flexible cloud capacity Motivators for the Cloud
  • 25. We understand this is a journey
  • 26. Agenda 13:30 AWS Cloud forIT Enterprise – Overview and Case Studies Tom O'Reilly, Head of Hong Kong & Taiwan,Amazon Web Services 14:00 15:00 AWS Cloud for Design and Simulation – Case Studies in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 15:15 16:00 Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 16:00 16:45 MentorGraphics Design Collaboration Julian Sun,Business Development Director,Mentor Graphics David Pellerin,HPC Business Development Principal,Amazon Web Services 16:45 AWS Kinesis – Big Data Management and Analytics in Manufacturing Ken Chan,Solutions Architect,Amazon Web Services David Pellerin,HPC Business Development Principal,Amazon Web Services 17:15 ClosingRemarks and Q&A James Tien,Sales & Business Development Manager,Amazon Web Services
  • 27. AWS Cloud for Design and Simulation Why Scalability Matters for CAE and EDA April 10, 2014 David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 28. Computer-Aided Design, Simulation, Analysis, Visualization • Across industries, the trend is Simulation-Driven Design and Discovery • Aerospace, semiconductor, automotive, civil engineering, energy exploration, consumer products, finance, pharmaceuticals, many others Examples in Design and Manufacturing • Computer-Aided Design (CAD) including 3D models • Finite Element Analysis (FEA) and Thermal Analysis • Electronic Design Automation (EDA) • Computational Fluid Dynamics • Multi-physics simulations • Molecular simulations for drug discovery A Simulation-Driven World
  • 29. A Collaborative World Collaboration between functional groups • Product Lifecycle Management • Collaborative Design • Concurrent Design Collaboration for global teams • Secure remote access to IP and applications
  • 30. A Data-Intensive World Managing big data for competitive advantage • For design, engineering, production environments • Quality and Yield Analysis • Statistical Process Control Processing Input Yield analysis Manufacturing facilitymonitoring In-field devicemonitoring Logging Log4J Appender push to Kinesis ElasticMapReduce Hive Pig Cascading MapReduce pull from
  • 31. What are AWS Customers Telling Us? “HGST is using AWS for a higher performance, lower cost, faster deployed solution vs buying a huge on-site cluster.” - Steve Philpott, CIO HGST application roadmap:  Molecular dynamics  CAD, CFD, EDA  Collaboration tools for engineering  Big data for manufacturing yield analysis Every application presents unique challenges… some technical, some business
  • 32. Cloud Provides Agility Wasted Resources Project Delays Actual demand Predicted Demand Rigid On-Premise Resources Elastic Cloud-Based Resources Actual demand Resources scaled to demand 3 to 5 year architecture commitment Little or no architecture commitment
  • 33. Maintaining an EDA cluster is expensive Is it worth your organization’s time and effort?
  • 34. Agility
  • 35. Consider a typical big compute job… such as ASIC timing simulation or mask verification
  • 36. …for which a departmental cluster is too small, or simply takes too long to complete…
  • 37. You can run the job using a central shared cluster…
  • 38. …if you can get through the job queue! ?
  • 39. The Hidden Cost of Queues Conflicting goals • EDA users seek fastest possible time-to-results • Simulations are not steady-state workloads • IT support team seeks highest possible utilization Result: • The job queue becomes the capacity buffer • Job completion times are hard to predict • Users are frustrated and run fewer simulations Fewer simulations = lost opportunity! ?
  • 40. The Hidden Cost of Queues This is what 100% utilization looks like
  • 41. On the cloud, clusters are created on-demand and can be balanced dynamically for each job…
  • 42. …neither too large…
  • 43. …nor too small…
  • 44. …with multiple clusters running at the same time
  • 45. Match the Architectures to the Jobs Scale up and scale out…
  • 46. Use automation to manage cluster sizing and monitor jobs and costs AWS Auto Scaling works with existing HPC scheduling software
  • 47. Who Uses Cloud Today? global enterprises, global applications
  • 48. Worldwide Research and Development “The Amazon Virtual Private Cloud was a unique option that offered an additional level of security and an ability to integrate with other aspects of our infrastructure.” “AWS enables Pfizer’s WRD to explore specific difficult or deep scientific questions in a timely, scalable manner and helps Pfizer make better decisions more quickly” Dr. Michael Miller, Head of HPC for R&D, Pfizer http://aws.amazon.com/solutions/case-studies/pfizer/
  • 49. Supporting Innovation in Compliance-Sensitive Industries
  • 50. Courtesy of Cypress Semiconductor Supporting Innovation in Electronic Product Design
  • 51. EM Field Simulations for TRUETOUCH® Touchscreen Controllers– Cypress Semiconductor 3D FEM simulations SPICE OUTPUT: sensor speed, SNR, and signal disparity OUTPUT: unit cell parameters in respect to sensor design  Finite-element mesh used for 3D simulations consists of over one million vertices  Lack of computational resources can limit the capability to model complex geometries and/or increase simulation time Courtesy of Cypress Semiconductor
  • 52. MASTER Node 01 Virtual Private Cloud Job 01: parameter set 2 Job 02: parameter set 2 Job NN: parameter set NN Job submission Accumulated simulation results  Simulations can be submitted as an array of jobs that share the same executable and libraries, different input parameters  Result: simulation time reduced from weeks to just hours Node 02 Node N EM Field Simulations for TRUETOUCH® Touchscreen Controllers– Cypress Semiconductor Courtesy of Cypress Semiconductor
  • 53. US West (Northern California) US East (Northern Virginia) EU (Ireland) Asia Pacific (Singapore) Asia Pacific (Tokyo) AWS Regions (10) AWS Edge Locations US West (Oregon) South America (Sao Paulo) Regions and Availability Zones GovCloud (ITAR Compliance) Asia Pacific (Sydney) China (Beijing)
  • 54. What Does Scale Mean in the Cloud? 18 hours 205,000 materials analyzed 156,314 AWS Spot cores at peak 2.3M core-hours Total spending: $33K (Under 1.5 cents per core-hour)
  • 55. How do you Scale an EDA Cluster? Actual demand Predicted Demand What size of cluster do you need? • A different size of cluster is needed at different points in the engineering process • Pace of innovationwill depend on making the right sizing decision And what kind of cluster is it? • Large memory? • More and faster cores? • Faster storage? • Faster networks? • What generation of processor? • IT hardware is a long-term commitment – when is the right time to buy?
  • 56. AWS Has the Scale to Constantly Innovate
  • 57. 2006 2007 2008 2009 2010 2011 2012-2013 March, 2014 m1.small m1.xlarge m1.large m1.small m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small cr1.8xlarge hs1.8xlarge m3.xlarge m3.2xlarge hi1.4xlarge m1.medium cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small g2.2xlarge hs1.8xlarge m3.xlarge m3.2xlarge hi1.4xlarge m1.medium cc2.8xlarge cc1.4xlarge cg1.4xlarge t1.micro m2.xlarge m2.2xlarge m2.4xlarge c1.medium c1.xlarge m1.xlarge m1.large m1.small c1.medium c1.xlarge m1.xlarge m1.large m1.small new existing EC2 Instance History c3.large c3.xlarge c3.2xlarge c3.4xlarge c3.8xlarge m3.medium m3.large i2.large i2.xlarge i2.4xlarge i2.8xlarge r3.large r3.xlarge r3.2xlarge r3.4xlarge r3.8xlarge hs1.xlarge hs1.2xlarge hs1.4xlarge deprecated Increasing customer choice…
  • 58. Performance Factors: CPU • Intel Xeon E5-26XX v2 (Ivy Bridge) CPUs • Available in AWS C3, R3, I2 instance types • 2.8 GHz, Turbo enabled up to 3.6 GHz • Intel® Advanced Vector Extensions (Intel® AVX): • 256 bit instruction set extension • Designed for applications that are floating-point (FP) intensive • The “Ivy Bridge” microarchitecture enhances this with the addition of float 16 format conversion instructions
  • 59. C3: CPU-Optimized Instance Type • 2.8 GHz Intel Xeon E5-2680v2 (Ivy Bridge) CPU • Turbo enabled to 3.6 GHz • Various instance sizes with 2, 4, 8, 16, 32 vCPUs • From 3.75GiB to 60GiB RAM • From 32GB to 640GB SSD • High PPS, low-latency Enhanced Networking: over 1M PPS • Supporting Cluster Placement Groups for all sizes
  • 60. R3: Memory-Optimized Instance Type • 2.5 GHz Intel Xeon E5-2680v2 (Ivy Bridge) CPU • Multiple instances sizes with 2, 4, 8, 16, 32 vCPUs • Up to 244 GiB RAM (~ 8GiB/vCPU) • SSD Based Instance Storage • High PPS, low-latency Enhanced Networking
  • 61. I2: High-IOPS Instance Type • 2.5 GHz Intel Xeon E5-2680v2 (Ivy Bridge) CPU • Various instances sizes with 4, 8, 16, 32 vCPUs • 30.5, 61, 122, 244 GiB RAM • 16 vCPU: 3.2 TB SSD; 32 vCPU: 6.4 TB SSD • 365K random read IOPS for 32 vCPU instance • High PPS, low-latency Enhanced Networking
  • 62. Performance Factors: Networks • AWS proprietary, 10Gb networking • Highest performance in .8xlargeinstance sizes • Full bi-section bandwidth in placement groups • No network oversubscription • Enhanced Networking • Availableon C3, R3, I2 (in VPC with HVM) • Over 1M PPS performance, reduced instance-to-instance latencies, more consistentperformance than earlier generation AWS networks
  • 63. Performance Factors: Accelerators NVIDIA GPUs! • For computing and for remote graphics • CG1 and G2 instances • GPU accelerators augment CPU-based computing by offloading specialized processing • Performance gains depend on application- level support
  • 64. Today?  Testing and development,patch testing, user training  EDA vendor sales enablement via “test drives”  Customer POCs, using real production examples  Customer-managed production EDA • With or without EDA vendor involvement Tomorrow… • Vendor-approved and documented cloud architectures for EDA • Customer-approved security and compliance best-practices • New EDA license models supporting extreme scalability • New software architectures allowing faster time-to-results, higher quality at reduced infrastructure cost Cloud for EDA: Today and Tomorrow
  • 65. 1) Customer Managed Application Hosting • Customer has account with cloud provider and manages virtual infrastructure • Cloud used for batch jobs via cluster management software • Customer can also remote login and globally collaborate using GPU instances • Customer maintains traditional software vendor relationships • Software vendor optionally offers license flexibility for scalable computing 2) Software Vendor Managed Application Hosting • SaaS or hybrid model for acceleration of batch tasks, for example rendering • Customer pays software vendor for cloud-hosted services • Customer does not need to manage virtual infrastructure Options for Software Licensing
  • 66. Scale Global Collaboration Agility Cloud Offers… …with higher performance and lower cost than on-premise HPC
  • 67. Cost Innovations
  • 68. Cost Benefits of HPC in the Cloud On-Premise HPC Metered, Pay As You Go Model Use only what you need, using on-demand, reserved, or spot Flexible Capital Expense Model High upfront capital cost, high cost of ongoing support Inflexible Cloud-Based HPC
  • 69. Optimize Costs by Combining Reserved, Spot, and On-Demand Instances 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Heavy Utilization Reserved Instances Light RI Light RILight RILight RI On-DemandSpot and On-Demand 100% 80% 60% 40% 20% Percentage of Peak Requirements Over Time
  • 70. Cloud has Lower TCO
  • 71. Agenda 13:30 AWS Cloud forIT Enterprise – Overview and Case Studies Tom O'Reilly, Head of Hong Kong & Taiwan,Amazon Web Services 14:00 15:00 AWS Cloud for Design and Simulation – Case Studies in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 15:15 16:00 Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 16:00 16:45 MentorGraphics Design Collaboration Julian Sun,Business Development Director,Mentor Graphics David Pellerin,HPC Business Development Principal,Amazon Web Services 16:45 AWS Kinesis – Big Data Management and Analytics in Manufacturing Ken Chan,Solutions Architect,Amazon Web Services David Pellerin,HPC Business Development Principal,Amazon Web Services 17:15 ClosingRemarks and Q&A James Tien,Sales & Business Development Manager,Amazon Web Services
  • 72. Using Cloud for Global Collaboration Use-Cases in CAE and EDA April 10, 2014 David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 73. Cloud Collaboration is Secure Collaboration
  • 74. Global Collaboration for Global Manufacturing Cloud provides a global, distributed, secure, and scalable environment for collaborative design and manufacturing
  • 75. Collaboration is More Secure in the Cloud Bring the users to the data, don’t send the data to the users
  • 76. Collaboration is More Secure in the Cloud Bring the users to the data, don’t send the data to the users
  • 77. Secure Remote Access Data and computation hosted in a secure, customer-managed virtual private cloud, with controlled access via a wide variety of client devices. Virtual Private Cloud Powered by NVIDIA GPUs
  • 78. NVIDIA GRID K520 in AWS Cloud Product Name GRID K520 GPUs 2 x GK104 GPUs CUDA cores 3,072 (1,536 per GPU) Core Clocks 800 MHz Memory Size 8GB GDDR5 (4GB per GPU) HW Video Encoder 2x h.264 (1 per GPU) Power Consumption 225W Supported APIs OpenGL 4.3, DirectX 9, 10, 11, CUDA 5.5, OpenCL 1.1, NVFBC, NVIFR, NVENC
  • 79. Application Streaming Middleware
  • 80. • Application Streaming • Remote visualization • Thin client 3D applications Amazon AppStream
  • 81. Thin Client Remote Collaboration Calgary Scientific PureWeb™ www.calgaryscientific.com/resolutionmd/web/ demos.getpureweb.com/
  • 82. Autodesk 360 on AWS
  • 83. Remote Evaluation and Training MentorGraphics® Virtual Labs on AWS www.mentor.com
  • 84. AWS Test Drive • Provides softwarevendors with a controlled, secure, convenient environment for product evaluation and training • Any application listed on AWS Test Drive is available for purchasefrom the ISV, and can be deployed on AWS if desired
  • 85. Cloud-based PLM with fast deployment and simplified scalability Dynamically scale PLM infrastructure up and down based on project needs
  • 86. Agenda 13:30 AWS Cloud forIT Enterprise – Overview and Case Studies Tom O'Reilly, Head of Hong Kong & Taiwan,Amazon Web Services 14:00 15:00 AWS Cloud for Design and Simulation – Case Studies in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 15:15 16:00 Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 16:00 16:45 MentorGraphics Design Collaboration Julian Sun,Business Development Director,Mentor Graphics David Pellerin,HPC Business Development Principal,Amazon Web Services 16:45 AWS Kinesis – Big Data Management and Analytics in Manufacturing Ken Chan,Solutions Architect,Amazon Web Services David Pellerin,HPC Business Development Principal,Amazon Web Services 17:15 ClosingRemarks and Q&A James Tien,Sales & Business Development Manager,Amazon Web Services
  • 87. Design Collaboration Featuring Mentor Graphics April 10, 2014 Julian Sun, Business Development Director, Mentor Graphics David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 88. G2 Supports AWS Test Drive Mentor Graphics HyperLynx® PI
  • 89. Agenda 13:30 AWS Cloud forIT Enterprise – Overview and Case Studies Tom O'Reilly, Head of Hong Kong & Taiwan,Amazon Web Services 14:00 15:00 AWS Cloud for Design and Simulation – Case Studies in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 15:15 16:00 Using Cloud forGlobal Collaboration – Demonstrations in CAE and EDA David Pellerin,HPC Business Development Principal,Amazon Web Services 16:00 16:45 MentorGraphics Design Collaboration Julian Sun,Business Development Director,Mentor Graphics David Pellerin,HPC Business Development Principal,Amazon Web Services 16:45 AWS Kinesis – Big Data Management and Analytics in Manufacturing Ken Chan,Solutions Architect,Amazon Web Services David Pellerin,HPC Business Development Principal,Amazon Web Services 17:15 ClosingRemarks and Q&A James Tien,Sales & Business Development Manager,Amazon Web Services
  • 90. Big Data Management For manufacturing April 10, 2014 David Pellerin, Principal Business Development Manager, HPC Amazon Web Services
  • 91. Motivator: reduce the time spent searching for data Aggregate data to a common platform, with common access tools Improve manufacturing yields by accessing more data in a more timely manner Speed up the yield improvement ramp up on new products Improve steady state yield on existing products Provide end-to-end visibility into: Every test, every diagnostic Data generated from all components of a product Data generated internally, and from field deployments Big Data Analytics in Manufacturing
  • 92. Scenarios Accelerated Ingest-Transform-Load Continual Metrics/KPI Extraction Responsive Data Analysis Software/ Technology IT server , App logs ingestion IT operational metrics dashboards Devices / Sensor Operational Intelligence Digital Ad Tech./ Marketing Advertising Data aggregation Advertising metrics like coverage, yield, conversion Analytics on User engagement with Ads, Optimized bid/ buy engines Financial Services Market/ Financial Transaction order data collection Financial market data metrics Fraud monitoring, and Value-at-Risk assessment, Auditing of market order data Manufacturing Production line and field repair data collection and aggregation Yield and failure analysis, batch and real-time Production monitoring systems, embedded controllers, device logs Consumer Online/ E-Commerce Online customer engagement data aggregation Consumer engagement metrics like page views, CTR Customer clickstream analytics, Recommendation engines Scenarios Across Industry Segments 1 2 3
  • 93. Metrics from HGST Big Data Platform pilot project: Collecting >2M manufacturing/testing input files daily Collecting from ~500 tables across 6 databases  tens of millions of records daily HGST’s BDP is demonstrating early benefits: Example: HGST Development Engineer: demonstrated the joining of data sets for detailed logistics tracking—analyses that is very difficult to conduct with current systems Ops Engineer: a recent production issue required detailed historical data. Current systems did not have the required retention for this data. However, the team was able to pull the data from the BDP in minutes, as opposed to 3+ weeks to pull the data from tape archive Development Engineer: obtained technical data from the BDP in hours as opposed to 3+ weeks to pull from tape archive DATA SEARCH PARTIES YIELD
  • 94. Kinesis Architecture Amazon Web Services AZ AZ AZ Durable, highly consistent storage replicates data across three data centers (availability zones) Aggregate and archive to S3 Millions of sources producing 100s of terabytes per hour Front End Authentication Authorization Ordered stream of events supports multiple readers Real-time dashboards and alarms Machine learning algorithms or sliding window analytics Aggregate analysis in Hadoop or a data warehouse Inexpensive: $0.028 per million puts
  • 95. Sending & Reading Data from Kinesis Streams HTTP Post AWS SDK LOG4J Flume Fluentd Get* APIs Kinesis Client Library + ConnectorLibrary Apache Storm Amazon Elastic MapReduce Sending Reading
  • 96. Possible Use-Case in ASIC Production Processing Input Yield analysis Manufacturing production monitoring and logging Logging Log4J Appender push to Kinesis ElasticMapReduce Hive Pig Cascading MapReduce pull from
  • 97. 107 Easy Administration Managed service for real-time streaming data collection,processingandanalysis. Simply create a new stream,set the desired level of capacity,andlet the service handle the rest. Real-time Performance Perform continual processingonstreaming big data. Processinglatencies fall to a few seconds,comparedwiththe minutes or hours associatedwithbatchprocessing. High Throughput. Elastic Seamlessly scale to matchyour data throughput rate and volume. Youcaneasily scale up to gigabytes per second. The service will scale up or downbasedon your operational or business needs. S3, Redshift, & DynamoDB Integration Reliably collect,process,andtransformall of your data in real-time & deliver to AWS data stores of choice,withConnectors for S3, Redshift,and DynamoDB. Build Real-time Applications Client libraries that enable developers to design and operate real-time streamingdata processingapplications. Low Cost Cost-efficient for workloads of any scale. You canget startedby provisioninga small stream,and pay low hourly rates only for what youuse. Amazon Kinesis: Key Developer Benefits