This book will take you through C from the ground up in a step-by-step manner. You'll start with the building blocks of C , which include basic data types, variables, strings, arrays, operators, control statements, and loops. Once comfortable with the basics, you'll then progress to learning object-oriented programming concepts such as classes and structures, objects, interfaces, and abstraction.
Generics, functional programming, dynamic, and asynchronous programming are covered in detail. This book also takes you through regular expressions, reflection, memory management, pattern matching, exceptions, and many other advanced topics.
As you advance, you'll explore the. NET Framework. Finally, you'll understand how to run unit tests with the Microsoft unit testing frameworks available in Visual Studio. What you will learn Get to grips with all the new features of C 8 Discover how to use attributes and reflection to build extendable applications Utilize LINQ to uniformly query various sources of data Use files and streams and serialize data to JSON and XML Write asynchronous code with the async-await pattern Employ.
NET Core tools to create, compile, and publish your applications Create unit tests with Visual Studio and the Microsoft unit testing frameworks Who this book is for If you have little experience in coding or C and want to learn the essentials of C programming to develop powerful programming techniques, this book is for you. It will also help aspiring programmers to write scripts or programs to accomplish specific tasks.
Compatible with any devices. The most important step in making a software system efficient is the proper selection of data structures and algorithms; many papers and textbooks have been devoted to these topics.
Most discussions, however, neglect another important activity: that of writing machine-independent efficient code. This paper examines a set of techniques for accomplishing that step. We examine those techniques both in an abstract setting and in their application of a real program, where they led to a speedup of a factor of over six.
Because these techniques should be employed rarely, an important part of this paper is describing exactly when one should and should not use them. It is also one of the most difficultprogramming languages to master. Increasingly, scientists and engineers must quickly and efficiently analyze and visualize extremely large sets of data.
A popular data analysis and visualization programming environment, IDL is used worldwide by scientists and engineers in fields as diverse as the physical sciences, medical physics, and engineering test and analysis.
Gumley provides a solid foundation in the fundamentals of procedural programming in IDL. He presents concise information on how to develop IDL programmers that are well structured, reliable, and efficient. The example programs in the book demonstrate key concepts and provide functionality that can be applied immediately. In addition, the book offers readers practical tips and advice on IDL programming, which they would otherwise discover only after years of experience.
While only modest prior programming experience is assumed, readers with experience in any procedural language will quickly translate their skills to IDL, learning the best programming practices for this new environment. Scientists, engineers, and students in educational, government, and commercial research and development environments will all appreciate the author's guidance in helping them effectively analyze and visualize data.
Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings.
This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice.
The results developed in this thesis serve to ease the transition into the multicore era. The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice.
The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory.
The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. Writing Efficient and Portable Macros Chapter Macro Language Elements Beginning Python In its current version, JavaFX 8 is supported in the Java platform by.
Immutable and persistent data structures are thread safe by definition and hence very appealing for writing robust concurrent programs. How do we express traditional algorithms in functional setting? Background Citations. Methods Citations. Topics from this paper. Data structure Algorithm Machine code Software engineer. High-level programming language High- and low-level Compiler Algorithmic efficiency.
Software system Systems design Lurker Book. Citation Type. Has PDF. Publication Type. More Filters. Refactoring Workbook. Performance engineering of multicore software: developing a science of fast code for the post-Moore era.
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