C++ for Scientists - Technische Universität Dresden

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202 CHAPTER 7. EFFECTIVE PROGRAMMING: THE POLYMORPHIC WAY of scarce computing resources and a lack of mature and powerful tools. Under these circumstances the overall performance, in terms of execution speed or memory consumption is solely dependent on the skill and ingenuity of the programmer and has resulted in the almost mythical ”hand optimized” code. However, to achive the desired specifications in such a fashion the clarity and readability, and therby the maintainability of the code were sacrificed. Furthermore, the low level of abstraction also hinders portability, as different architectures favour different assumptions to produce efficient execution. To address this effect, implementations were duplicated in order to optimize for different architectures and platforms, which of course makes a mockery of goals such as code reusability or even extensiblity. This paradigm and the derived techniques are then used differently in Section 2.11, where generic programming is used to offer an efficient approach for matrix operations.
7.2. GENERIC PROGRAMMING 203 7.2 Generic Programming Generic programming may be viewed as having been developed in order to further facilitate the goals of code reusability and extensibility. From a general view the generic programming paradigm is about generalizing software components so that they can be directly reused easily in a wide variety of situations. While these are among the goals, which lead to the development of object oriented programming, it may vary quite profoundly in the realization. A major distinction from object oriented programming, which is focused on data structures and their states, is that it especially allows for a very abstract and orthogonal description of algorithms. To achieve this kind of generalization a separation of the basic tools of programming are important: algorithms, containers (data structures), and a glue between them (so called iterators or more generally traversors). As introduced as an important part for effective programming, the minimization of glue code, iterators and traversal objects operate as a minimal but fully abstract interface between data structures and algorithms. While the desired functionality is often implemented using static polymorphism mechanisms, such as templates in C++, generic programming should not be equated with simply programming with templates. However, when generic programming is realized using purely compile time facilities such as static polymorphism, not only is implementation effort reduced but the resulting run time performance optimized. In the following, the process of generic programming is given by elevating a procedural code to a generic one simultanioulsy fullfilling the important topics of effective programming (efficiency, type-safety, code reuse): • Algorithm: Generic algorithms are generic in two ways. First the data type which they are operating on is arbitrary and second, the type of container within which the elements are held is arbitrary. To get in touch with the generic approach, a generalization of the memcpy() function of the C standard library is discussed. An implementation of memcpy() might look somewhat like the following: void∗ memcpy(void∗ region1, const void∗ region2, size t n) { const char∗ first = (const char∗)region2; const char∗ last = ((const char∗)region2) + n; char∗ result = (char∗)region1; while (first != last) ∗result++ = ∗first++; return result; } The memcpy() function is already generalized to some extent by the use of void∗ so that the function can be used to copy arrays of different kinds of data. Looking at the body of memcpy(), the function’s minimal requirements are that it needs to traverse the sequence using some sort of pointer, access the elements pointed to, copy the elements to the destination, and compare pointers to know when to stop. The memcpy() function can then be written in a generic manner: template OutputIterator
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Technische Universität Dresden Fak
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Contents I Understanding C++ 7 Intr
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CONTENTS 5 10.5 Unix and Linux . .
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Part I Understanding C ++ 7
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10 C ++ was not a reliable computer
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12 CHAPTER 1. GOOD AND BAD SCIENTIF
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20 CHAPTER 2. C++ BASICS • std::c
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22 CHAPTER 2. C++ BASICS In the fir
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24 CHAPTER 2. C++ BASICS int main (
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26 CHAPTER 2. C++ BASICS Operator A
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28 CHAPTER 2. C++ BASICS The bitwis
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30 CHAPTER 2. C++ BASICS cast (type
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32 CHAPTER 2. C++ BASICS complicate
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34 CHAPTER 2. C++ BASICS } else if
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36 CHAPTER 2. C++ BASICS eps/= 2.0;
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38 CHAPTER 2. C++ BASICS for (...;
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40 CHAPTER 2. C++ BASICS 2.6.1 Inli
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42 CHAPTER 2. C++ BASICS To make su
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44 CHAPTER 2. C++ BASICS float divi
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46 CHAPTER 2. C++ BASICS The first
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48 CHAPTER 2. C++ BASICS #ifndef at
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50 CHAPTER 2. C++ BASICS float A[7]
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52 CHAPTER 2. C++ BASICS Encapsulat
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54 CHAPTER 2. C++ BASICS As a pract
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56 CHAPTER 2. C++ BASICS A function
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58 CHAPTER 2. C++ BASICS Now that t
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60 CHAPTER 2. C++ BASICS we need sm
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62 CHAPTER 2. C++ BASICS 2.12 Exerc
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64 CHAPTER 2. C++ BASICS 2.13 Opera
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66 CHAPTER 3. CLASSES apply symm bl
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68 CHAPTER 3. CLASSES int main() {
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70 CHAPTER 3. CLASSES class solver
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72 CHAPTER 3. CLASSES • Compilers
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74 CHAPTER 3. CLASSES a real number
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76 CHAPTER 3. CLASSES } return ∗t
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78 CHAPTER 3. CLASSES This mechanis
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80 CHAPTER 3. CLASSES One could not
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82 CHAPTER 3. CLASSES class matrix
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84 CHAPTER 3. CLASSES Approach 3: R
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86 CHAPTER 3. CLASSES of the decrem
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88 CHAPTER 3. CLASSES There are two
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90 CHAPTER 4. GENERIC PROGRAMMING }
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92 CHAPTER 4. GENERIC PROGRAMMING c
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94 CHAPTER 4. GENERIC PROGRAMMING A
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100 CHAPTER 4. GENERIC PROGRAMMING
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134 CHAPTER 5. META-PROGRAMMING exp
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140 CHAPTER 5. META-PROGRAMMING Fir
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146 CHAPTER 5. META-PROGRAMMING tra
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148 CHAPTER 5. META-PROGRAMMING tem
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150 CHAPTER 5. META-PROGRAMMING 5.3
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Real-World Programming Chapter 12 1
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12.1. TRANSCENDING LEGACY APPLICATI
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12.1. TRANSCENDING LEGACY APPLICATI
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Parallelism Chapter 13 13.1 Multi-T
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13.2. MESSAGE PASSING 261 int main
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Numerical exercises Chapter 14 In t
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14.1. COMPUTING AN EIGENFUNCTION OF
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14.3. THE SOLUTION OF A SYSTEM OF D
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14.4. GOOGLE’S PAGE RANK 275 Taki
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14.5. THE BISECTION METHOD FOR FIND
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14.6. THE NEWTON-RAPHSON METHOD FOR
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14.7. SEQUENTIAL NOISE REDUCTION OF
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14.7. SEQUENTIAL NOISE REDUCTION OF
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Programmierprojekte Kapitel 15 Die
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15.6. MATRIX-SKALIERUNG 287 Siehe h
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15.10. ANWENDUNG MTL4 AUF TYPEN MIT
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Acknowledgement Chapter 16 Special
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Bibliography [AG04] David Abrahams
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