© 2007 IBM Corporation Next Generation Grid: Emerging technologies poised to revolutionize financial markets Kevin Pleiter Director – Global Financial

© 2007 IBM Corporation

Next Generation Grid: Emerging technologies poised to revolutionize financial markets

Kevin PleiterDirector – Global Financial Services Sector

© 2007 IBM Corporation2 FMA | October, 2007

Companies need to grow

aggressivelyby leveraging

differentiating technology

Financial Market Analytics


Financial Services Sector Trends, 2007-2015

Informed clients are increasing demands

Global integration is reshaping industries

Regulatory burdens are growing

Innovation is becoming imperative

FSS Industry Trends Challenges

Data Explosion

Managing Complexity

Managing Business Integrity

Collaboration & Partnering

Speed & Transparency


0

50

100

150

200

250

300

350

1997 1998 1999 2000 2001 2002 2003 2004

Agency Asset-Backed Corporate Derivatives

Mortgage-Backed Money Market Municipal Repo

Treasury/Sovereign Other

Electronification will target all asset classes…1

0

500

1,000

1,500

2,000

2,500

3,000

3,500

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Average Daily Trade Volume (millions)(10 year CAGR 19%)

Security brokers and services personnel ('000)(10 year CAGR 3%)

Average Daily Trade Volumes vs. Headcount, 1994-2004 (Millions of Shares; Number of Employees (‘000), Volume per Employee)

Number of US and EuropeanElectronic Trading Systems1, 1997-2004

(Number of Electronic Trading Systems by Type of Market Supported)

Note: 1Totals may exceed the actual number of systems listed since some systems cover multiple product linesSource: The Bond Market Association; Reuters; SIA; IBM Institute for Business Value analysis

Volume of daily shares traded per employee (10 year CAGR 15%)


…causing agency margins to die and value to shift toward riskier activities

Note: 1Agency is risk free intermediation of a trade and principal is risk assuming intermediation of a trade Source: IBV profit map model (see slide 18 in this study)

1

Agency vs. Principal1, 2004 and 2015(US $ Billions, Margin Percentage)

Firms unanimously agreed that proprietary trading will be an increasingly important source of profit over the next ten years; regulators indicated they will not prevent it.

020406080

100120140160180200

Principal trading Agency execution0%

10%

20%

30%

40%

50%

60%

70%

2004

2015

RevenueShare50%

Financing and Structured Products Revenue,2004 and 2015

(US $ Billions)

6

18

1

4

0

5

10

15

20

25

2004 2015

Financing

Structured products

CAGR 11%

RevenueShare50%

Revenue Margins

020406080

100120140160180200

Principal trading Agency execution0%

10%

20%

30%

40%

50%

60%

70%

RevenueShare 30%

RevenueShare70%

RevenueShare

50%

RevenueShare50%


The data center energy, power and cooling challenge is a major hurdle for the ongoing success of Financial Services Grids

Power Use

35

30

25

20

15

10

5

0Chiller/

Cooling Tower

InformationTechnology

LightingSwitch/Gen

UninterruptiblePowerSupply

Power Distribution

Unit

ComputerRoom Air

Conditioner

Humidifier

Cooling systems Electrical and building systems

% o

f to

tal

da

ta c

en

ter

ele

ctr

icit

y u

se


These Business and Technology imperatives result in the following Technology trends in financial markets globally

Aggressive adoption of Model and Algo driven trading +

Demand for real time portfolio and risk management +

Massive build out of computing infrastructure

Next Generation Grid

Powered by Hybrid Computing and Stream computing

enabling the use of Data and Analytics as a Weapon


Specifically Financial firms analytic infrastructures are challenged to support ongoing business growth in five dimensions

Application Management

Workload Management

Data Management

Systems Management

Hardware Management

Inconsistent and unrepeatableInability to quickly deploy new apps

High cost modelLack of scalability and Performance

I/O bottle necks and geographic data dispersion issues not addressed

Data center CrisisCost, utilization, power and space

High cost, manual and reactiveNo automated / integrated feedback


The need for speed

Exploit advanced technology

to accelerate performance


Hybrid computing powered Grids incorporating the Cell Broadband Engine™ (Cell/B.E.)

Ten year collaboration with Sony®, Toshiba®

– Extended through 2012

IBM BladeCenter® QS20 system introducedSeptember 2006, QS21 in 2007 and QS22 2008

– “Just another Blade”

– Operates in standard BladeCenter® chassis

– Infiniband, Gig-E and 10GigE support

– Runs Redhat Linux and a host of other standard industry applications

Cell/B.E. Highlights

– Significant performance advantages over traditional systems

– 64-bit Power™

– Streaming Processors

– Low power consumption

– Real-Time

– Security Enabled


Cell use cases in the Financial Service Sector

Various domains

– Fixed Income and FI Derivatives, Equities and Equity Derivatives, Risk Management, Portfolio Trading, Algorithmic Trading and Analytics

Functional areas

– Pricing of Options, Swaps, and Futures

– Interest Rate Modeling, CDO, MBS

– Path Dependent Calculations

– Risk evaluations – VaR, EPE, PCE, etc

– Stream Processing - market and analytic data


Typical Algorithms being deployed in Cell grids

Algorithms typically used include

– Analytical and approximate (crude) solutions through to Black-Scholes

– PDE solvers (e.g.,Crank-Nicolson)

– Householder transformation, Cholesky decomposition, SVD, PCA

– Monte Carlo Simulations

– Statistical Analysis

Standard Library routines being called

– BLAS, LAPACK, FFT

– Random Number Generation (Sobol, MT, Box-Muller, Moro’s Inversion)

– Math Library’s (R, NAG)


Rapidly growing Application Development, Math library and Grid ecosystem for Financial Services

BLAS – DPFFT – DP

Cell Blade Hardware (QS22)

BLAS – SP

Financial Analytic Libraries and ISV applications NAG, Encirq, …

FFT – SP

FSS Stream Workloads ISVs - Encirq

IBM – SystemS IBM – Websphere Front Office

RHEL 5.1, 5.2 with full Cell support

Application Layer

Base SDKProgramming framework

LANL-centric

Hardware / Firmware

Libraries

SP – Single PrecisionDP – Double Precision

1H 2008 Interoperability/Deployment (Windows + Java)

Grid Schedulers via Partners

SOMA CAB – Analytics workstation

LAPACK – SP/DP

Multi-core Cell SDK (SPE Mgmt) + ALF + DACs + Compiler Roadrunner framework

zCell – Back office offload + new workloads

Platform Computing

Proprietary FSS Analytic TradingAnd Risk Applications

IBM – Dynamic Application Virtualization (DAV)Partners

1Q’09 SLES – Cell extensions

RNG/MKL

Available


x86 Linux master cluster

Cell/B.E. accelerator

cluster

Hybrid Supercomputing Reference

Targeting 1.4 petaflop peak, 1.0 petaflop sustained performance


For more information download “How much is a microsecond worth?” at www.ibm.com/financialmarkets


Documents

© 2007 IBM Corporation Next Generation Grid: Emerging technologies poised to revolutionize financial markets Kevin Pleiter Director – Global Financial