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Demonstrating the Benefits of Hyper-Acceleration
The document summarizes Bigstream, a hyper-acceleration layer that allows data processing workloads to automatically leverage CPUs, GPUs, FPGAs, and other accelerators without requiring code changes. It discusses how Moore's Law is ending and hardware accelerators are needed to continue performance improvements for big data and AI workloads. It then provides examples of how Bigstream can accelerate Spark queries for business intelligence and ETL/ML pipelines for adtech workloads by 3-30x compared to CPU-only processing. The document concludes by announcing the availability of Bigstream on Amazon EMR.
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Demonstrating the Benefits of Hyper-Acceleration
- 1. Roop Ganguly, SolutionArchitect
- 2. The End ofMoore’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 BigData 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 CloudVendors Respond ASIC GPU FPGA
- 6. Data Scientists & Developers PerformanceTeam 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 Architecturewith Bigstream
- 9.
- 10. Business Intelligence Query •Basedon 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 UseCase Demo
- 12.
- 13. Adtech ETL/ML DataPipeline 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 CaseDemo
- 15.
- 16. Setting the bootstrapscript Bigstream ON EMR Add the Bigstream bootstrap URL and your cluster has hyper-acceleration
- 17.
Editor's Notes
- #7 At the very high level, our hyper-accelerator layer consists of three sub-layers:
- #8 At the very high level, our hyper-accelerator layer consists of three sub-layers:
- #14 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.