Dr. Dobb's Journal - June 2008 - (Page 47)

d06keas_p4ds 4/10/08 1:49 PM Page 47 The advantage of grouping arrays topologically is that they can often be nested in hierarchies. For example, one array class could contain array data common to all the nodes of a mesh, while another class could contain extra array data pertaining to a subset. An index set could be used to map array indices in the subset to corresponding indices in the larger mesh as a way to implement inheritance. Topological grouping is a good guideline, but it’s only a guideline. There may be performance-sensitive groups of arrays that should be split off into separate classes for easier cache tuning. Once you have decided how to group your classes, there is one other important point. For classes that have an underlying implementation that is Struct-like, make sure that variables that are likely to be used together are in adjacent locations in the struct. This greatly increases your cache hits, and thus your performance. Resist the urge to order struct members strictly alphabetically because that can cause a huge performance penalty. Conclusion Performance portability is likely to become a necessary part of programming in the near future. Already, some compilers will only create good SSE code for the x86 processor architecture if stride one vectors are used (also known as Array-like layouts). At the same time, older superscalar machines usually perform best when Struct-like layouts are used as often as possible. Without the flexibility to switch memory layouts, performance almost certainly suffers as code is ported, and this is more likely to be so as we head into the future. The technique I describe here is used in a large multiphysics project that encompasses hundreds of thousands of lines of code. The technique was originally used as a refactoring tool more than a performance portability tool. At one point, the project was able to quickly refactor the hydrodynamics portion of their physics code for a 42–100 percent speedup depending on the problem being solved and the machine being used. The hydrodynamics can be the dominant portion of the runtime for many physics applications, so doubling the performance with just a change of data structures is impressive. Part of the performance gain probably came from the compiler recognizing extra optimizations that could be applied (same compiler flags), and the rest came from different cache latency characteristics. Thanks to Brian McCandless for questioning me during a presentation on the sidebar material, and inspiring me to write this article. His group uses a variant of this technique in their software. References Triangle and Quadrilateral areas (softsurfer.com/algorithms.htm). Grandy, Jeff. “Efficient Computation of Volume of Hexahedral Cells,” Lawrence Livermore National Laboratory UCRL-ID-128886, Oct 30, 1997. DDJ Get moving. Stay focused. Take control. Enterprise Architect from Sparx Systems redefines visual modeling with a huge set of built-in tools, technologies and capabilities, coupled with a lightweight footprint and great agility. With deep support for UML 2.1 and its related standards, Enterprise Architect 7.1 is the ideal tool to analyze, design, build and manage your next software project. Keep your project moving, keep your team focused and stay in total control. Visit sparxsystems.com for a free trial June 2008 l www.ddj.com l Dr. Dobb’s Journal 47 http://softsurfer.com/algorithms.htm http://sparxsystems.com http://sparxsystems.com http://www.ddj.com

Table of Contents Feed for the Digital Edition of Dr. Dobb's Journal - June 2008

Dr. Dobb's Journal - June 2008 Contents Friday Night Fish Fry Alia Vox Developer Diaries There Must Be Contest Conversations Building a Test Harness for RTOS QT and Windows CE Software to Hardware Parallelization Performance Portable C++ Effective Concurrency The Agile Edge Swaine's Flames

Dr. Dobb's Journal - June 2008

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