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Porting workshop, Part 6: Tying it all together

A consolidated view of modifications while porting compute-intense applications to the Cell Broadband Engine architecture

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Level: Introductory

John Easton (JKJ@uk.ibm.com), Infrastructure Architect, Emerging Technologies, IBM
Ingo Meents (MEENTS@de.ibm.com), Architect for Cell Solutions, Advanced Planning, Simulation, and Optimization, IBM
Olaf Stephan (STEPHANO@de.ibm.com), Server Specialist, DB2, Warehousing BI Solutions, IBM
Horst Zisgen (horst_zisgen@de.ibm.com), Program Manager Simulation/Operations Research, IBM
Sei Kato (SEIKATO@jp.ibm.com), Research Staff Member, IBM

16 Oct 2007

The seven quick-read parts of this "Porting workshop" series take you on a real-world trip from strategy and planning through workload execution, performance tweaking, optimization, and a solid conclusion. The series describes how to most effectively port compute-intensive applications to the Cell Broadband Engine platform. In this Part 6, the authors provide a summary of what the series has covered so far.

This seven-part, quick-read workshop series is taken from the real-world case study whitepaper, "Porting Financial Markets Applications to the Cell Broadband Engine Architecture" (written by John Easton, Ingo Meents, Olaf Stephan, Horst Zisgen, and Sei Kato, IBM Systems and Technology Group, June 2007; see Resources). You can probably spend less than 10 minutes reading each installment and come out at the end with a strong basic knowledge of the requirements for effectively porting a compute-intensive application (in this case, a financial market application) to the Cell/B.E. processor.

Editor's note: The performance results in this series were obtained using Versions 1 and 2.1 of the Cell Broadband Engine Software Developer Kit (SDK). The current version of the SDK, the IBM Software Development Kit for Multicore Acceleration, Version 3.0, has recently become available and offers many enhancements in functionality, ease of use, and performance over the earlier versions. While the results documented in this article are correct for the earlier versions of the SDK, different results will be obtained with SDK 3.0. Watch for updates to the articles in this series that will describe the latest performance improvements obtained using SDK 3.0.

Workshop series
|_ 1. Porting strategies (developerWorks, August 2007)
|
|_ 2. Analysis of the original code (developerWorks, August 2007)
|
|_ 3. Initial performance results (developerWorks, September 2007)
|
|_ 4. Mersenne-Twister (developerWorks, September 2007)
|
|_ 5. Mixed-precision workloads (developerWorks, September 2007)
|
|_ 6. Tying it all together.
|
|_ 7. Getting the most performance.

Introducing the application

The example application modified in this article is a piece of code used to price a European Option to highlight the benefits of the Cell/B.E. blade. A European Option is a simple financial contract with strict terms and properties that gives the buyer the right to trade a given asset at a specific price on a specific date. It is generally an option that can be exercised only at the end of its life. By contrast, an American Option can be traded at any time between its purchase date and the date at which the contract expires. Because a European Option is traded on a fixed date, it is a simpler calculation to perform because the time variability of the American Option is removed.

You can use several different models price a European Option, depending on the type of asset that underlies it. For example, an option based on currency is calculated using a slightly different model than an option based on futures. In the example described in this series, the calculation is based on a simple Monte Carlo simulation technique. You will generate 200,000,000 uniform, pseudo-random numbers. These numbers are transformed to a log-normal distribution using a Box-Müller transform. Using the random numbers generated, you will execute the financial model repeatedly to simulate a random walk. The final stage of the analysis will be the calculation of the relevant statistics, such as the minimum, maximum, and average and the 95 percent quartile for losses.



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Tying it all together

The series has applied a number of different techniques to improve the performance of the same piece of European Option pricing code. To conclude with a consolidated view of all of these different results, see Figure 5 (Diagram 1 from the original white paper).


Figure 5. Comparison of the different optimization techniques
Comparison of the different optimization techniques

The original and OpenMP results were measured on an Intel® quad-core (Cloverton) system. The Cell/B.E. results were measured on the IBM BladeCenter® QS20. All numbers compare blade-to-blade: a single blade with either two Intel Cloverton CPUs or two Cell Broadband Engine CPUs.

As you can see, initial efforts in porting to the Cell/B.E. processor, even when compared to modern x86 hardware, still deliver performance improvements of nine and four times for single precisions and double precisions, respectively. With the latest improvements to the sample code and to the ecosystem (SDK improvements, XLC compiler enhancements, and so forth), these numbers are now 39 and 11 times respectively.

If you consider the OpenMP-optimized version of the code, you can see that the latest Cell/B.E. versions still deliver an 11 times performance improvement for single-precision and a 3 times improvement for double-precision.

All of this is achieved on a system that many view as being unsuitable for users because of its (presumed) poor double-precision performance. As you can see, this view of poor performance is not supported by these results. And when you consider the plans for a future enhanced Double-Precision version of the Cell Broadband Engine technology that will offer a further five-time performance improvement for these double-precision numbers, you can see that systems based on Cell/B.E. technology are an excellent platform for compute-intensive applications.



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Acknowledgments

Many other individuals contributed (both knowingly and unknowingly) to this piece of work. The authors wish to acknowledge their kind contributions. Without this assistance, this paper would never have been written.

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About the authors

John is currently leading a worldwide emerging technologies team within IBM Systems and Technology Group. He has several roles competing for his time, all of which revolve around advising organizations on how best to exploit new technologies. John has been working for IBM for over 20 years in a variety of technical roles. He worked in Distributed Systems Development in Austin before the launch of the RS/6000, and he holds several patents in the areas of security and systems software. Before taking his current role, he was the European technical leader for grid computing.


Ingo Meents joined IBM nine years ago and works currently as an IT Architect in IBM Global Engineering Solutions (GES). His current focus is to provide IBM customers with knowledge of the latest Cell/B.E. software technology by consulting, educating, briefing, and creating solutions for this platform. Before his work on the Cell/B.E. platform, he was lead architect for a modeling, simulation, and production planning solution used by the IBM 300mm semiconductor line in Fishkill. Starting as a research student at IBM, Ingo Meents received his doctor's degree from the University of Clausthal in 2001.


Olaf Stephan joined IBM in 1998 and currently works as an IT Specialist in IBM Global Engineering Solutions (GES). His focus is to provide IBM customers with knowledge of the latest Cell/B.E. software technology by consulting, educating, briefing, and development for this platform. Before his work on the Cell/B.E. platform, he worked in the areas of data management, data warehousing, business intelligence, and data integration. Olaf holds a Masters degree in Electrical Engineering, specializing in Communications Technology, from the University of Applied Sciences, Koblenz, Germany.


Horst has over 10 years of experience in the application of simulation methods and the development of mathematical models in different areas. He is currently leading a development team in IBM Global Engineering Solutions (GES) that is working on a simulation and planning solution used by IBM 300mm manufacturing in Fishkill and by external customers as well. Horst is also the European subject matter expert for the GES supply chain offerings. In addition, Horst regularly gives lectures at universities about simulation and mathematical modeling. Horst is a member of a standardization group for simulation and optimization.


Sei Kato is a staff member in IBM Research, Tokyo Research Laboratory. He joined IBM in 2002 after receiving his PhD in Mathematical Science from the University of Tokyo. After joining IBM, Sei has worked on modeling and simulating the performance of Web systems. His is currently working on the acceleration of financial calculations and on large-scale traffic simulations.




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