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68276318
Intel Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism
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Item #:
68276318
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Enables you to specify tasks instead of threads for better portability, easier programming, more understandable source code, and better performance and scalability in general
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What Stands Out
Optimized Parallelism
Enhances C++ with advanced threading capabilities, allowing developers to efficiently utilize multi-core processors, optimizing application performance for modern hardware.
Ease of Use
Provides a high-level abstraction for multi-threading, making it accessible for developers while reducing the complexity of traditional threading models.
Comprehensive Documentation
Includes detailed explanations, examples, and best practices, helping users to quickly adopt and effectively implement threading solutions in their C++ applications.
Product Details
| Publisher | O'Reilly Media |
| Publication date | August 21, 2007 |
| Edition | 1st |
| Language | English |
| Print length | 332 pages |
| ISBN-10 | 0596514808 |
| ISBN-13 | 978-0596514808 |
| Item Weight | 1.18 pounds (540 grams) |
| Dimensions | 7 x 0.71 x 9.19 inches (17.8 x 1.8 x 23.3 cm) |
Who Should Buy?
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C++ Developers
C++ programmers seeking to optimize their applications using multi-core processors will find valuable insights and techniques.
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Performance Engineers
Engineers focused on improving software performance in multi-threaded environments will benefit from effective parallel programming strategies.
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Academic Researchers
Researchers studying parallel computing concepts can leverage this book for foundational knowledge and real-world applications.
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Beginners in C++
Newcomers to C++ may struggle with the advanced concepts of threading and parallelism presented in this book.
Product Description
Intel Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism
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Features & Benefits
- Intel Threading Building Blocks (TBB) is a portable C++ library that allows C++ programmers to write applications that can run on a multi-core system
- It enables you to specify tasks instead of threads for better portability, easier programming, more understandable source code, and better performance and scalability in general
- Focuses on the goal of parallelizing computationally intensive work to deliver high-level solutions
- Is compatible with other threading packages, and doesn't force you to pick one package for your entire program
- Emphasizes scalable, data-parallel programming, which allows program performance to increase as you add processors
- Relies on generic programming, which enables you to write the best possible algorithms with the fewest constraints
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