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Data-Oriented C++ in Scientific Programming

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Data-Oriented C++ in Scientific Programming

Dozenten: Jonas Latt

Bei enthalten

Mehr erfahren

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Software architecture using an object-oriented and a data-oriented approach
Efficient programming using language-specific features in C++
GPU and multi-core CPU software development in C++

Kompetenzen, die Sie erwerben

Kategorie: Memory Management
Kategorie: System Programming
Kategorie: Object Oriented Design
Kategorie: Data-oriented programming

Wichtige Details

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54 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 5 Module

Learn how to write efficient, maintainable C++ code for data-intensive applications in this hands-on course. Key application areas include scientific simulation software, statistical data processing, and computer graphics.

You’ll begin by exploring how modern C++ supports high-efficiency programming and review the principles of a central piece of hardware in data-intensive applications: computer memory. Next, the course shifts to software design. You’ll examine performance shortcomings of traditional object-oriented programming and dive into data-oriented perspective, which improves performance by structuring code around the data itself. You’ll learn how this paradigm overcomes bottlenecks and leads to more scalable, high-performance solutions. Finally, you’ll apply these principles to modern multi-threaded systems. Through hands-on experience you will learn to use C++'s built-in parallel features to target both multi-core CPUs and GPUs.

Moduldetails

To follow this MOOC, you should be familiar with the fundamentals of the C++ language. In this first module however, you will find a refresher on the most important concepts needed to complete the course. You will (re)discover variable types (stack variables, references, smart pointers), containers and algorithms, classes and polymorphism. If you already master these notions but your knowledge predates the C++11 standard, we still encourage you to follow this module for up-to-date recommendations on the efficient use of C++. In particular, have a look at Lesson 4 if you are not yet familiar with the notion of ranges in C++20 and later versions.

Das ist alles enthalten

17 Videos1 Lektüre11 Aufgaben

17 VideosInsgesamt 119 Minuten

MOOC trailer2 Minuten
Welcome to this MOOC4 Minuten
Arrays, vectors, and initialization8 Minuten
Scope of variables and memory allocation10 Minuten
Transfer of data from and into a function11 Minuten
More about containers11 Minuten
Algorithms and lambda functions11 Minuten
Ranges: an introduction6 Minuten
Ranges: more data-oriented examples 5 Minuten
Views11 Minuten
Smart pointers in C++ : basic concepts8 Minuten
Smart pointers in C++ : vectors of smart pointers5 Minuten
Structures and classes : Creation and initialization9 Minuten
Structures and classes : function objects4 Minuten
Polymorphic classes : general ideas7 Minuten
Polymorphic classes : abstract and derived classes at work4 Minuten
Let's wrap up1 Minute

1 LektüreInsgesamt 30 Minuten

📢 Important preliminary information: Assessments and subtitles30 Minuten

11 AufgabenInsgesamt 114 Minuten

Do you need Module 1 ?30 Minuten
Arrays, Vectors, Variable Initialization8 Minuten
Scope of variables and memory allocation5 Minuten
Transfer of data from and into a function10 Minuten
Containers10 Minuten
Algorithms and lambda functions8 Minuten
Ranges8 Minuten
Views8 Minuten
Smart pointers6 Minuten
Structures and classes6 Minuten
Polymorphic classes15 Minuten

Part of the work of processing data efficiently consists in reducing the amount of computations and the complexity of the execution path. A complex execution path is often implemented to express design choices in the organization of software components. In the form of a compile-time execution framework, C++ offers a way to offload this complexity to the compilation stage and keep the execution environment simple and efficient.

Das ist alles enthalten

12 Videos1 Lektüre10 Aufgaben

12 VideosInsgesamt 98 Minuten

Presentation of the module3 Minuten
Introduction to templates11 Minuten
Abbreviated function templates9 Minuten
Template specialization and integer arguments9 Minuten
Constant expressions12 Minuten
Compile-time loops6 Minuten
Compile-time polymorphism using templates10 Minuten
Loop unrolling9 Minuten
Variants and visitors8 Minuten
Runtime polymorphism11 Minuten
Interface-based development8 Minuten
Let's wrap up2 Minuten

1 LektüreInsgesamt 10 Minuten

Variants and Visitors (complement)10 Minuten

10 AufgabenInsgesamt 127 Minuten

Using templates6 Minuten
Using abbreviated syntax for function templates6 Minuten
Integers as template arguments20 Minuten
Which expressions can be evaluated at compile-time?6 Minuten
Compile-time polymorphism using templates15 Minuten
Loop unrolling7 Minuten
Implement the variant-visitor strategy15 Minuten
From dynamic to variant-based polymorphism15 Minuten
Extend a library with templated polymorphism7 Minuten
Final quiz30 Minuten

Memory is one of the most critical resources of a computing platform. In this module, you will learn how memory is used in the C++ language and how memory access works on different types of machines. You will translate this knowledge into code that is at the same time clearly structured and efficient.

Das ist alles enthalten

15 Videos1 Lektüre13 Aufgaben

15 VideosInsgesamt 127 Minuten

Presentation of the module3 Minuten
Memory organization11 Minuten
Virtual memory9 Minuten
Memory management unit6 Minuten
Memory latency11 Minuten
Latency vs bandwidth8 Minuten
Cache memory11 Minuten
Cache latency in practice11 Minuten
Data prefetching13 Minuten
Vector instructions14 Minuten
RAM vs CPU9 Minuten
Definitions: FLOPS and AI5 Minuten
Roofline performance model10 Minuten
Roofline performance model in practice3 Minuten
Let's wrap up3 Minuten

1 LektüreInsgesamt 10 Minuten

Memory size comparison10 Minuten

13 AufgabenInsgesamt 170 Minuten

Memory organization10 Minuten
Virtual memory10 Minuten
Memory management unit5 Minuten
Memory latency10 Minuten
Latency vs bandwidth15 Minuten
Cache memory10 Minuten
Memory size comparison10 Minuten
Cache latency in practice10 Minuten
Data prefetching15 Minuten
Vector instructions15 Minuten
Performance metrics15 Minuten
Roofline performance model15 Minuten
Final quiz30 Minuten

Learn to think about your program in a way that puts the critical hardware resource, memory, in the center of your thoughts. Apply these principles to discrete-event systems, a problem solving strategy with applications in all applied areas of science. Understand that data-oriented programming is not opposed to object-oriented programming but rather offers a way to model your objects in a way that accounts for your data structure, your algorithms, and your hardware.

Das ist alles enthalten

8 Videos9 Aufgaben

8 VideosInsgesamt 40 Minuten

Presentation of the module2 Minuten
Linear memory layout6 Minuten
Array of Structures vs Structure of Arrays6 Minuten
Complex Agent Based Model6 Minuten
From Object Oriented to Data Oriented5 Minuten
Scalable molecular dynamics simulation5 Minuten
Data oriented solution8 Minuten
Let's wrap up2 Minuten

9 AufgabenInsgesamt 103 Minuten

Memory Layout Estimation4 Minuten
Recognizing SoA and AoS6 Minuten
Reading the example code15 Minuten
Object modeling vs. data modeling10 Minuten
Code transformation15 Minuten
Efficiency analysis25 Minuten
What data layout?3 Minuten
Implementing the example5 Minuten
Final quiz20 Minuten

This module introduces you to an amazing feature available in the C++17 and onward: the ability to parallelize an algorithm with almost no effort, using C++ parallel algorithms. You will learn that you can gain a manifold speedup with simple changes by running your code on multi-core CPUs and on GPUs. You will dive once more into the working principles of computer memories to understand how to reach substantial performance improvements, and you will link the concepts of data-oriented programming to efficient paralellism.