GPU Acceleration for Financial Services
11
Click here to load reader
Transcript of GPU Acceleration for Financial Services
Kinetica – GPU Acceleration for Financial ServicesJames Mesney, EMEA Systems Engineering Director 1
AI will bring significant cost savings and revenue growth opportunities to Banks
AI is transforming Financial Services
Cost Savings and Revenue Growth Opportunities from AI by 2025 – (Goldman Sachs)
7+NEW
ML LibrariesOpen Source Released in the last year
Presenter
Presentation Notes
Use cases in play include: Real-time sentiment analysis Real-time anomaly detection to prevent fraud Reallocate resources on the fly based upon personnel, environmental and seasonal data Track terrorist and other national security threats in real time Optimize energy generation and uptime based on fluctuating usage patterns and unpredictable natural disasters Improve customer engagements by correlating data from point of sales (POS) systems, social media streams, weather forecasts, and other data. Track inventory in real-time, enabling efficient replenishment and avoiding out-of-stock situations Connected home, connected car, connected cities More rapid query response time and data discovery capabilities for end users Faster iterations to risk modeling and modeling optimization
Deep Learning for Mortgage Risk
Mortgage Riskhttps://arxiv.org/abs/1607.02470
2005
2008
2010
2012
• Deep Neural Network
• 20 Years Mortgage Data
• 120 Million Loans
• 3.5 Billion Records
• 2 TB Data
• 300 Explanatory Features
Presenter
Presentation Notes
Terabytes of data from 120 Million mortgages over 20 years was used to train a Deep Learning model able to better predict mortgage default and prepayment of loans. GPU parallel computing was used. (This work is from Stanford University). The plot shows actual mortgage prepayment (x-axis) versus DNN predicted prepayment (y-axis). The black line is represents theoretical 100% perfect prediction. The blue points are predicted by the DL model and you can see the prediction in near perfect. The red points are the Linear Regression standard model used historically as a baseline and we see the accuracy is much worse. The predictions are a full 1 year ahead for each loan. This allows better credit risk scoring and better mortgage-backed portfolio construction. The maps on right show cumulative foreclosures per 1000 population.
GPU Accelerated In-memory
Scalable
Natural Language Processing based full-text search
Geospatial /location-based
analytics built-in
Analytics in millisecondsReduced server sprawlCommodity hardware
Cloud or on-prem
Real time connectors to ingest data
Integrated with Hadoop, Spark, NiFi, Kafka, Storm,
TensorFlow, Caffe, Torch…+ Rich set of APIs
Data Visualisation built-in
A Shopping List for Quants and Data Scientists
100x – 1000x faster than legacy databases
5
WHAT
• Accelerated database designed for parallel processing across GPU-accelerated hardware
• Ingests billions of records per minute
• Scales to terabytes in-memory
• No indexing or optimizations required
• Integrated visualization engine with native geospatial support
• Integrated with AI/ML libraries
WHY
• 100-1000x faster than legacy in-memory and NoSQL databases
• Sub-second response times for billion row queries
• Ask questions at the speed of thought
• Reduce operational complexity, hardware footprint and cost
• Converge AI and BI - User Defined Functions
• Built-in Visualisation, Dashboards, Maps
Introducing Kinetica
Why Kinetica ?
• Performance• CPU-only, in-memory databases suffer from lacklustre performance and scalability issues• Systems struggle to ingest and query simultaneously • Hadoop can’t deliver acceptable response times for streaming data and near-real-time use cases
• Cost• Traditional EDWs are expensive and restrictive• Traditional in-memory databases costly to scale
• Complexity• Traditional systems require ETL, frequent changes to data models, hardware and software optimizations• Expensive and hard-to-find skills • Multiple point-solutions enforces sampling, aggregation, latency. • Too much “data herding” wastes time.
6
The GPU
4,500 cores per device versus 8 to 32 cores per typical CPU
High performance computing trend to using GPUs to solve
massive processing challenges GPU acceleration brings high
performance compute to commodity hardware
Parallel processing is ideal for scanning/filtering/aggregating massive datasets
GPUs have thousands of small, efficient cores exceptionally suited to parallel processing. GPUs are well-suited to compute-intensive workloads, HPC, machine intelligence, deep-learning, AI.
Presenter
Presentation Notes
Accelerated by NVIDIA
Kinetica: CoreIN-MEMORY ANALYTICS DATABASE ACCELERATED BY GPUs
KINETICA
Commodity Hardwarew/ GPUs
Disk
A1 B1 C1
A2 B2 C2
A3 B3 C3
A4 B4 C4
GPU AcceleratedColumnar In-memory Database
HTTP Head Node
• GPU-accelerated, distributed architecture• Data stored across tiers – VRAM, System memory, SSD,
NVMe, Flash SAN• Columnar design, relational model…tables, rows, columns• Column level Dictionary Encoding and Compression• Native GIS & IP address object support• Interact with Kinetica through native REST API,
Java/C++/Python API, SQL, ODBC, JDBC, or connectors• Security
• Authentication : AD/LDAP/Kerberos• Authorization: Kinetica RBAC• Audit: Audit log for queries by user and security changes• Encryption: on disk, 3rd party tool for In-Memory• SSL/TLS support Typical hardware setup: 2 CPU
Sockets and 256GB - 1TB memory with 2-4 GPUs per node.
Risk ManagementMILLISECONDS
STREAM PROCESSING
ON DEMAND SCALE OUT
IN-DATABASE PROCESSING
MONITORINGGlobal Positions
Regional Positions
ACCOUNTABILITYVaR Limits
PRE-TRADEWhat-if Scenarios
RISK MANAGEMENTP&L, Commissions
SensitivitiesLiquidity Risk
Counterparty Risk
Global / Regional Heads
Desk Managers
Traders
Spot Prices, Transactions,Market Risk Data
ExternalTransactional Records
UDF Functions
POSITIONS ENGINE
CALCULATIONS PRICING MODELS
RISKAPPLICATIONS
More data available.Data flowing faster. Many new types of users demanding
sophisticated real-time analysis.
Kinetica Connectors
Presenter
Presentation Notes
-- Stream data via a plethora of open source connectors -- Perform aggregations and SQL queries on-demand rather than pre-calculated -- Retain historical data in-memory for historical analysis -- Deploy any ML or deep learning model as a UDF leveraging open API’s Other UCs: Risk Weighted Assets, Trade Decisioning, Stock Tick Analytics, AML, …
UNMATCHEDPERFORMANCE
SCALABLE ACROSS
MULTIPLE NODES
UDF DELIVER 1st
CONVERGED AI AND BI WORKLOADS
INDUSTRY-STANDARD CONNECTORS TO DATA
SOURCES & APPS
GPU Accelerated In-Memory Streaming and NRT
Commodity hardware On-premises or Cloud Scales to 100’s of TB 40x less infrastructure
Machine Learning AI In-database
Kafka, Storm, NiFi, Spark ODBC, JDBC ANSI SQL/92 API’s for Java, JS, C++,
Python, node.js, REST
Summary - Kinetica GPU Accelerated Analytics
www.kinetica.com
Take the Kinetica Challenge!Try it with your own data
Email: [email protected]
Thank You!
