Numerix CrossAsset XL and Windows HPC Server 2008 R2 · Numerix CrossAsset XL and Windows HPC Server 2008 R2 ... linearly as more compute nodes were added to a HPC cluster. In terms

Numerix CrossAsset XL and Windows HPC Server 2008 R2 Faster Performance for Valuation and Risk Management in Complex Derivative Portfolios

Microsoft Corporation

Published: February 2011

Abstract

Numerix, a leading provider of cross-asset analytics for derivative portfolio valuation and

risk management, working together with Microsoft to enable Numerix CrossAsset XL with

Windows HPC Server 2008 R2 and Windows HPC Services for Excel 2010. This high-

performance computing (HPC) solution allows financial services professionals—from

traders and risk managers to insurance actuaries—to more efficiently manage their

portfolios and assess risk on an interactive and day-to-day basis because the solution

provides enhanced accuracy and more timely pricing and risk information.

This paper presents benchmark and performance test results for typical derivative

portfolio use cases. The test results show that portfolio calculation speed increased almost

linearly as more compute nodes were added to a HPC cluster. In terms of compute time,

the testing showed the following excellent results:

For a portfolio of 10,000 Foreign Exchange (FX) trades, it took 100.2 minutes to compute

results on a standalone desktop with two quad-core processors. Running it on a two-

node cluster with one head node and one compute node (two quad-core processors per

node) reduced compute time to 12.5 minutes—an 88 percent improvement.

On a nine-node cluster (two quad-core processors per node), calculation speed for

10,000 FX trades was almost 50 times faster than with a standalone desktop.

For a variable annuity Guaranteed Minimum Benefit (GMxB) policy set of 10,000, total

compute time was reduced from 139.6 minutes on a desktop to 2.6 minutes on a 72-

core HPC cluster—a 98 percent improvement.

These faster calculations save large amounts of time. Financial services professionals get the

answers they need faster, so they can respond more quickly to changing market dynamics as

they manage their derivative portfolios.

Windows HPC Server 2008 R2/Numerix Benchmark Results White Paper

Disclaimer

©2011 Microsoft Corporation. All rights reserved. This document is provided "as-is." Information and

views expressed in this document, including URL and other Internet Web site references, may change

without notice. You bear the risk of using it.

Some examples are for illustration only and are fictitious. No real association is intended or inferred.

This document does not provide you with any legal rights to any intellectual property in any Microsoft

product. You may copy and use this document for your internal, reference purposes.


Table of Contents

Challenges to Managing Complex Derivative Portfolios on a Daily Basis .............................. 1

Risk Management with Numerix and Microsoft High Performance Computing ........................... 1

HPC Performance Testing for Derivative Portfolio Calculations .............................................. 2

Test Environment ......................................................................................................................... 2

Use Cases and Test Results ......................................................................................................... 3

Use Case 1: FX Trader in Numerix CrossAsset XL ..................................................................... 3

Use Case 1 Test Results .......................................................................................................... 4

Use Case 2: Variable Annuity GMxB Policy Pricing in Numerix CrossAsset XL ......................... 6

Use Case 2 Test Results .......................................................................................................... 7

Conclusion ................................................................................................................................... 10

Resources .................................................................................................................................... 11


Benchmark Results for Windows HPC Server 2008 R2 and Numerix CrossAsset XL 1

Challenges to Managing Complex Derivative Portfolios on a Daily Basis

Managing risk and valuing complex derivative portfolios on a daily basis is challenging and

computationally intensive. Traders, risk managers, and actuaries in capital markets and insurance

must have timely and accurate information to assess value and risk. However, they are faced with

several major challenges:

Choosing between time and accuracy: Traders, risk managers, and actuaries have had to rely

on estimates and rough calculations based on stale data.

Capturing all derivative trading activity into a common framework with consistent valuations

across asset classes has been time-consuming, cumbersome, and error prone.

Running risk analysis on large, computationally intensive derivative portfolios and bespoke

deal types generally takes too long to provide information that can be used for daily risk

management.

Market uncertainty, increasing regulatory demands, and

new accounting standards (as outlined in Basel II, FAS

133/157, and IAS 39, for example) are creating increased

pressure on derivative portfolio managers to establish

accurate, timely, and consistent pricing, risk, and reporting

measures enterprise-wide.

To meet these challenges, financial services professionals need a

powerful, highly scalable solution for the pricing, valuation, and

risk management of today’s most complex derivative portfolios.

Risk Management with Numerix and Microsoft High-Performance Computing

Currently, many financial services professionals are limited in their

ability to perform valuations and assess risk by the computing

power of their individual desktops. To increase performance,

forward-looking companies are creating solutions that use high-

performance computing. Numerix and Microsoft are working

together to provide a cost-effective HPC-based solution for

managing risk and valuing derivative or variable annuity portfolios.

Numerix CrossAsset XL takes advantage of the features of the

Windows HPC Server 2008 R2 and Windows HPC Services for Excel

solution to access powerful grid-computing capabilities. These

tools from Numerix, when coupled with the value of an integrated

HPC solution from Microsoft, provide:

Rapid unified risk calculations.

Accelerated real-time valuations.

Improved systems productivity.

Interoperability and full transparency for deal definitions.

Numerix CrossAsset XL

A flexible, Microsoft Excel–based

platform for structuring, pricing, and

analyzing derivative or structured

products.

Features

Grid-computing enabled for

parallel Excel computations,

with built-in support for

Windows HPC Server 2008 R2.

Broad instrument support,

available as a complete cross-

asset solution or individual

modules.

Comprehensive library of single-

and multi-factor models and

high-performance numerical

methods.

Hundreds of templates and

examples, and a “Structuring

Wizard” for new deal types.

Simple payoff scripting language

for defining bespoke

instruments.

Full valuations and Greeks (even

for structured products).



HPC Performance Testing for Derivative Portfolio Calculations

In this paper, Numerix and Microsoft demonstrate that adding computational capacity significantly

improves performance on complex calculations for derivative portfolios. The performance testing was

designed to answer the following questions:

Does performance improve by using an HPC cluster for portfolio calculations?

How much does performance change as the number of cores increases?

What is the HPC cluster overhead?

Test Environment

A series of tests were conducted on a 12-node HPC cluster. Each node was configured identically as

shown in Table 1. Each test was run on a standalone node to establish a baseline for standalone

desktop performance. The desktop system had two quad-core processors, for a total of eight cores.

For cluster performance testing, one of the

nodes was designated as the head node,

which performed the following tasks:

Deployed copies of the test

workbook to the rest of the nodes

(called compute nodes).

Opened and closed Excel on the

compute nodes.

Hosted the master Excel

spreadsheet used for each test.

Updated the results in the master

spreadsheet.

The compute nodes handled the calculations using Excel in server mode (no GUI running) and sent the

results back to the head node.

Software used in the testing included the following:


Windows HPC Server 2008 R2

Windows Services for Excel 2010

Microsoft Office Excel 2010

The performance tests were run two to three times to ensure that the results were consistent.

96-Core HPC Cluster Compute Node Configuration

Compute nodes 12

Processors per node 2 quad-core

Cores per node 8

Total cores in HPC cluster 96

Type of processor Intel Xeon

E5345

(8 MB Cache, 2.33 GHz)

Table 1. HPC cluster compute node configuration



Use Cases and Test Results

Two use cases, each one common to a particular industry or asset class, were tested using the HPC

cluster:

1. Foreign Exchange: Commonly traded and semi-exotic deals

(Numerix CrossAsset XL FX Trader module).

2. Insurance industry: Variable annuity GMxB policy pricing

(Numerix CrossAsset XL).

Use Case 1: FX Trader in Numerix CrossAsset XL

This use case tested HPC performance on the Numerix CrossAsset

XL FX Trader module, which provides an intuitive Excel-based

workflow interface for traders to apply sophisticated pricing and

risk models to many commonly traded and semi-exotic deals. The

CrossAsset XL FX Trader module provides all the building blocks

needed to simulate unique portfolio trades, along with pre-built

templates to get started quickly.

Sets of 1,000, 5,000, and 10,000 trades were conducted with

increasing numbers of cores. The following types of trades were

tested in this use case:

Barrier

Digital Barrier

European

Spot/Forward

Touch

The tests computed the present value (PV) of each trade, along

with the following first and second order Greeks:

Delta

Delta CCY

Gamma

Phi

Rho

Theta

Vanna

Vega

Volga


An interoperable HPC solution with a

productive development

environment for organizations that

have not had access to HPC

capabilities in the past.

Features

Seamlessly scale from

workstation to cluster by making

it possible for users to harness

the power of distributed

computing through a familiar

Windows desktop environment

without requiring specialized

skills or training.

Rapidly develop HPC

applications using the Microsoft

Visual Studio development

system, which provides a

comprehensive parallel

programming environment.

Improve systems administration

and cluster interoperability by

simplifying the overall

deployment, administration,

and management over the

entire system lifetime, while

ensuring interoperability with

existing systems infrastructure.



Use Case 1 Test Results

Cluster vs. Desktop Performance

Testing showed that performance improved significantly when trade calculations were run on an HPC

cluster as opposed to a standalone desktop. As shown in Figure 1, the 10,000 trade set exhibited a

substantial increase in speed as more cores were added. With as few as 32 cores, calculation speed

was 27 times faster than on the desktop. With 72 cores, calculation speed was almost 50 times faster.

Figure 1. FX trade calculation speed up on an HPC cluster relative to a standalone desktop

From a total compute time perspective, simply

using a two-node cluster with eight cores to

calculate large trade sets resulted in dramatic

improvements in compute time. As Table 2 shows,

it took 100.2 minutes to compute the results of

10,000 FX trades on a standalone desktop with two

quad-core processors. Running the same

calculations on a two-node cluster with a head

node and one compute node, each with two quad-

core processors, took only 12.5 minutes—an 88

percent improvement.

The 5,000 and 1,000 trade sets experienced similar

Total Compute Time (minutes)

FX trade count

10,000 5,000 1,000

Desktop 100.2 48.0 9.6

Cluster w/8 cores 12.5 6.6 1.9

Improvement 88% 86% 81%

Table 2. Improvement in compute time for FX trade sets

on a two-node HPC cluster versus a desktop



improvements in compute time at 86 and 81 percent, respectively. These significant improvements in

compute time are due to Excel calculation being spread across multiple cores and multiple machines

that operate in parallel.

Cluster Performance

In all of the tests, larger calculation sets experienced much greater performance gains than smaller

sets as cores increased (Figure 2). Calculation times dropped dramatically for the 5,000 and 10,000

trade sets as up to 32 cores were added. Although smaller, the performance gains from 32 to 72 cores

were still considerable. For example, running 60 portfolios with 10,000 trades each on 72 cores versus

32 cores would save almost two hours in computing time (1.7 minutes less per portfolio), giving

financial services professionals vital information much faster.

Figure 2. HPC cluster performance gains based on total compute time in minutes for FX trades

The long, tapering performance tail shown in Figure 2 was common in all use cases. Two primary

factors explain the decrease in HPC cluster performance after 32 cores:

Bandwidth between the head node and the compute nodes

As more cores send data back to the head node, bandwidth is consumed very quickly, creating

a bottleneck for incoming data coming into the head node. Also, the head node handles

workbook deployment to the compute nodes. In some cases, the head node may still be

deploying workbooks when the first results begin to come in. This two-way traffic decreases

available bandwidth to receive results from the compute nodes. Though there are techniques

for improving overhead, such as shared memory, none were employed here.

The head node’s ability to process results coming in from the compute nodes

The master Excel spreadsheet resides on the head node. Performance may be constrained by



how quickly the incoming results can be aggregated and properly placed in the master

spreadsheet.

The reduction in compute time for large trade sets between eight and 32 cores is very positive. For

10,000 FX trades, the compute time dropped from 12.5 minutes on eight cores to 3.7 minutes on 32

cores. Similar performance gains can be extrapolated for much larger trade sets.

Cluster Overhead

As cores increased, HPC cluster overhead—activities not related to calculations—also increased.

However, larger sets of trade calculations exhibited less overhead than smaller sets (Figure 3). For

example, a small 1,000 trade set running on 72 cores spent 53 percent of the total computing time on

overhead activities. In contrast, the 10,000 trade set running on 72 cores only spent 23 percent of the

total computing time on cluster overhead. The trend shown here suggests that the most efficient use

of an HPC cluster is to run large sets of calculations on fewer cores, thus reducing overhead.

Figure 3. Percent of total computing time spent on HPC cluster overhead during FX trade calculations

Use Case 2: Variable Annuity GMxB Policy Pricing in Numerix CrossAsset XL

This use case focuses on pricing variable annuity GMxB policies. These standard insurance derivative

products have many options and must rely on pricing models and computational algorithms to create

accurate prices for the policies. The data used in this case came from the Numerix CrossAsset XL GMxB

Excel workbook, which contains millions of policies with many different variables. The data sets tested

here represent a well-defined sample of policies.

The tests calculated the present value of each policy in the set using 1,000 Monte Carlo paths. The

model used for each asset was:



Hybrid: [HW2 (USD) + Heston (SPX) + Heston (RTY) + Credit (CDX) ] + Actuarial data

(Mortality/Lapse/Withdrawal Tables)

Performance was measured as increasing numbers of cores were used to run the calculations on the

HPC cluster.

Use Case 2 Test Results

Cluster vs. Desktop Performance

As with the FX trades, larger calculation sets experienced much greater performance gains than

smaller sets when run on an HPC cluster. Compared to a standalone desktop, the 10,000 policy

calculation was 29.5 times faster on 32 cores and 53.7 times faster on 72 cores (Figure 4).

Figure 4. GMxB policy calculation speed up on an HPC cluster relative to a standalone desktop

The 1,000 policy set exhibited smaller performance gains, running 13.2 times faster on 72 cores than

on a desktop. Given the results for the 1,000 policy set, it is clear that the larger the calculation set,

the better the performance on an HPC cluster.



Total compute time for the 10,000 policy set

on a desktop was 139.6 minutes, or two

hours 19 minutes. When run on 72 cores in

an HPC cluster, it took only 2.6 minutes.

Similar performance gains happened with

the smaller policy sets (Table 3).

Cluster Performance

In all of the policy pricing tests, the larger

calculation sets experienced greater

performance gains than smaller sets as cores

increased (Figure 5), especially as up to 32

cores were added. The performance gains from 32 to 72 cores were smaller but still created important

time savings for situations where many portfolios are calculated.

Figure 5. HPC cluster performance gains based on total compute time in minutes for GMxB policies

These results are very similar to the performance gains seen in the FX trades testing, as are the smaller

performance gains above 32 cores. Improvements in compute time for large trade sets between eight

and 32 cores remained very positive for the GMxB policies: For the 10,000 policy set, compute time

dropped from 16.5 minutes on eight cores to 4.7 minutes on 32 cores. Based on these findings, similar

performance gains can be extrapolated for much larger policy sets.

Total Compute Time (Minutes)

Variable annuity policy count

10,000 5,000 1,000

Desktop 139.6 69.9 14.2

Cluster w/72 cores 2.6 1.7 1.1

Improvement 98% 98% 92%

Table 3. Improvement in compute time for GMxB policy sets on

a two-node HPC cluster versus a desktop



Cluster Overhead

The HPC cluster overhead results for GMxB pricing calculations mirrored the results from the FX trades

testing (Figure 6). The trend shown here reiterates the finding that, in terms of reducing overhead, the

most efficient use of an HPC cluster is to run large sets of calculations on fewer cores.

The increase in overhead as more cores are added can most likely be explained by the same factors

that reduced cluster performance with more cores, as explained in Use Case 1:

Bandwidth between the head node and the compute nodes to receive results.

The head node’s ability to process results coming in from the compute nodes.

Figure 6. Percent of total computing time spent on HPC cluster overhead during GMxB policy pricing calculations



Conclusion

The test results presented in this paper clearly demonstrate the value that HPC brings to complex

derivative portfolio management. Numerix and Microsoft together provide a powerful, highly scalable

solution that helps financial services professionals to more efficiently and accurately price, value, and

manage risk based on available information.

Testing also clearly shows that adding more cores to an HPC cluster significantly increases

performance:

An eight core cluster ran 10,000 FX trades in only 12.5 minutes.

Calculating the present value for 10,000 GMxB policies took only 2 minutes using 72 cores.

Using an HPC solution that is based on Numerix CrossAsset XL and Windows HPC Server 2008 R2 with

Windows HPC Services for Excel lets traders, risk managers, and actuarial professionals access the

most powerful grid computing capabilities available to the financial industry today. This also helps to

solve common challenges by:

Providing more timely and accurate information for daily risk management.

Capturing relevant derivative trading activity into a common framework with enhanced

consistency of valuations across asset classes.

Significantly reducing the time needed to run risk analyses on large, computationally intensive

derivative portfolios and bespoke deal types.

Meeting regulatory demands and new accounting standards for accurate, timely, and

consistent pricing, risk, and reporting measures enterprise-wide.



Resources

For more information, visit the following resources:


http://www.numerix.com/crossasset


http://www.microsoft.com/hpc/en/us/default.aspx

http://www.numerix.com/crossasset

http://www.microsoft.com/hpc/en/us/default.aspx

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Numerix CrossAsset XL and Windows HPC Server 2008 R2 · Numerix CrossAsset XL and Windows HPC Server 2008 R2 ... linearly as more compute nodes were added to a HPC cluster. In terms