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Generative Programming

Generative Programming. Generic vs Generative. Generic Programming focuses on representing families of domain concepts Generative Programming also includes the process of creating concrete instances of concepts. Overview. Translator. Generative Component. Finished Configuration.

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Generative Programming

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  1. Generative Programming

  2. Generic vs Generative • Generic Programming focuses on representing families of domain concepts • Generative Programming also includes the process of creating concrete instances of concepts

  3. Overview Translator Generative Component Finished Configuration Specification in a configuration DSL Implementation components

  4. Why Generators? • Raise the intentionality of system descriptions • E.g. using domain specific notation • Produce an efficient implementation • Nontrivial mapping into implementation concepts • Avoid the library scaling problem • Library built as concrete component double in size for each new added feature

  5. Transformation Model System Requirements System Requirements System Requirements Manually implement Manually implement Manually implement High Level System Specification Implement with tools Source in DSL Source in DSL Source Code (C++, Java) Source Code (C++, Java) Source Code (C++, Java) compile compile compile System Implementation System Implementation System Implementation

  6. Type of transformations • Vertical • Horizontal

  7. Vertical Transformation • Refines higher-level structure into lower level, preserving structure • Typical of step-wise refinement and CASE or GUI builders

  8. Horizontal Transformation • Modifies modular structure at the same level • Merges, deletes or modifies existing modules

  9. Kind of transformations • Compiler transformations • Source to source transformations

  10. Compiler Transformations • Refinements • Decomposition • Choice of representation • Choice of algorithm • Specialization • Concretization • Optimizations

  11. Compiler Optimizations • Inlining • Constant folding • Data caching • Loop fusion • Adding matrixes A+B+C • Loop unrolling • When number of iterations is small • Code motion • Move invariant code outside of loop

  12. Compiler Optimizations (2) • Common subexpression elimination • Dead-code elimination • Partial evaluation • Partially evaluate a function based on knowledge of some of its parameters to be constants in a special context • Finite differencing x = x + 2 x = x + 2; y = x * 3; y = y + 6;

  13. y = x * 3 dy/dx = 3 dx = 2 yi+1 = yi + 3 dx

  14. Source to source Transformations • Editing transformations • Refactoring • Abstraction and generalization • Introducing new variant points • Simplification

  15. Approaches • Aspect-Oriented Programming • Subject-Oriented Programming • Software Transformation Technologies • Intentional Programming • Domain Engineering • Generative Programming

  16. Aspect Oriented Programming • To improve the modularity of designs and implementations by allowing a better encapsulation of cross-cutting concerns: • synchronization, distribution, authentication, data traversal, memory allocation, tracing, caching, etc. • New kind of modularity called “aspect” • Aspects represent an orthogonal parameterization concept compared to what's available in current languages

  17. Subject Oriented Programming • Related to AOP • Focuses on capturing different subjective perspectives on a single object model • It allows composing applications out of "subjects" (partial object models) by means of declarative composition rules

  18. Software Transformations • aid software development activities by providing mechanized support for manipulating program representations • Examples: • extracting views • Refinement • Refactoring • optimizations of program representations

  19. Intentional Programming • an extendible programming environment based on transformation technology and direct manipulation of active program representations • New programming notations and transformations can be distributed and used as plug-ins • The system replaces parsing technology with the direct entry and editing of resolved ASTs

  20. Domain Engineering • Domain engineering comprises the development of a common model and concrete components, generators, and reuse infrastructures for a family of software systems

  21. Goals of Generative Programming • Each language implements its own libraries: types are hard to match • Problem: int add(int i, int j) { return i+j; } add(1, x); int inc(int x) { return add(1, x); } class Complex { double r, i; } Complex add(Complex x, Complex y) { return Complex(x.r + y.r, x.i + y.i); }

  22. Complex inc(Complex x) { return add(Complex(1, 0), x); } • Compiler can’t optimize, since it does not know the Complex type • Class used to represent concepts in domain, but semantics of domain is not conveyed to compiler

  23. Partial Evaluation Matrix A, B, C, D; D = A.add(B.add(C)); Requires allocation of temporary intermediate matrix and two loops Compiler is not capable, DSL for algebra could incorporate, e.g. write Matrix.add(A, B, C);

  24. C++ • Using template metaprogramming one can produce specialized code • BLITZ matrix library: faster than Fortran

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