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The C++ programming language offers many features useful for tackling complex scientific computing problems: inheritance, polymorphism, generic programming, and operator overloading are some of the most important. Unfortunately, these advanced features came with a hefty performance pricetag: until recently, C++ lagged behind Fortran's performance by anywhere from 20% to a factor of ten. As a result, the adoption of C++ for scientific computing has been slow.

Is there a way to soup up C++ so that we can keep the advanced language features but ditch the poor performance? This is the goal of the Blitz++ project: to develop techniques which will enable C++ to rival -- and in some cases even exceed -- the speed of Fortran for numerical computing, while preserving an object-oriented interface. The Blitz++ Numerical Library is being constructed as a testbed for these techniques.

Recent benchmarks show C++ encroaching steadily on Fortran's high-performance monopoly, and for some benchmarks, C++ is even faster than Fortran! These results are being obtained not through better optimizing compilers, preprocessors, or language extensions, but through the use of template techniques. By using templates cleverly, optimizations such as loop fusion, unrolling, tiling, and algorithm specialization can be performed automatically at compile time.

Another goal of Blitz++ is to extend the conventional dense array model to incorporate new and useful features. Some examples of such extensions are flexible storage formats, tensor notation and index placeholders.