Demonstrating the Benefits of Hyper-Acceleration
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Big Data Meetup 03/29
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Demonstrating the Benefits of Hyper-Acceleration
- 1. Roop Ganguly, Solution Architect
- 2. The End of Moore’s Law 350 nm 180 nm 130 nm 90 nm 65 nm 1.0 2.0 3.0 1970 1980 1990 2000 Power Wall GHz Gordon Moore
- 3. Implications for Big Data Security AnalyticsRisk Management Behavioral Analytics Natural Language Processing AI/Deep Learning Machine Learning
- 4. CPU-Bound Applications – A New Bottleneck 40Gb- 100Gb Network Now that faster networking and disk technologies have emerged, CPUs act like “stop signs” for computation Node 1 Node 2 Node 3
- 5. Accelerators Microprocessor and Cloud Vendors Respond ASIC GPU FPGA
- 6. Data Scientists & Developers Performance Team Inhibitor: Programming Model Gap for Hardware Accelerators Two wildly different skill sets CPU GPU FPGA Data Science Programming Model BIG DATA PLATFORMS Acceleration Programming Model Programming Model Gap
- 7. Cross Platform Cross Hardware Intelligent, automatic computation routing Zero code change Introducing Bigstream Hyper-acceleration Layer Dataflow Adaptation Layer Bigstream Dataflow Bigstream Hypervisor HYPER-ACCELERATION LAYER BIG DATA PLATFORMS CPU GPU FPGA 3X to 30X acceleration
- 8. Accelerated Spark Architecture with Bigstream
- 9. 9 Business Intelligence Use Case
- 10. Business Intelligence Query •Based on Transaction Processing Performance Council – Decision Support (TPC-DS) Benchmark •Spark/SQL Query: SELECT i_item_id , avg(ss_quantity) agg1, avg(ss_list_price) agg2, avg(ss_coupon_amt) agg3 | FROM store_sales, customer_demographics, date_dim, item, promotion WHERE ss_sold_date_sk = d_date_sk AND ss_item_sk = i_item_sk AND……. •Input: approximately 2GB of avro table data •Simultaneously run software-accelerated and unaccelerated on identical Amazon EMR clusters
- 11. Business Intelligence Use Case Demo
- 12. 12 ETL Adtech Use Case
- 13. Adtech ETL/ML Data Pipeline Spark Streaming Spark Streaming APPLICATION/ WEB SERVERS KAFKA clicks clicks, likes impressions USERS Spark ML RTB Systems Distributed messaging system (tens of servers) Distributed computation system (hundreds of servers) Millions of users
- 14. ETL Use Case Demo
- 15. Announcement – Bigstream on AWS EMR
- 16. Setting the bootstrap script Bigstream ON EMR Add the Bigstream bootstrap URL and your cluster has hyper-acceleration
- 17. Thank You
Editor's Notes
- At the very high level, our hyper-accelerator layer consists of three sub-layers:
- At the very high level, our hyper-accelerator layer consists of three sub-layers:
- This is a typical streaming Big Data pipeline. As users browse the internet, our servers send different events through messaging bus, like a Kafka cluster. There is a Spark cluster, which reads the data from Kafka, cleans up the data, passes the statistical summary to a machine learning engine. You can see a similar pipeline architect in an IoT architecture, security, AdTech pipeline, A similar scenario can happen when you have a batch data, which is not necessarily streamed. In the next slide, I will show a demo for Spark cluster with Bigstream hyper-acceleration software stack already installed. The demo shows the execution without and with acceleration.