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SKU/Artículo: AMZ-B0GMRGJG23

GPU PROGRAMMING WITH C++ AND CUDA: Advanced Strategies for Optimizing Legacy Code and Creating Scalable Parallel Solutions

Format:

Paperback

Hardcover

Kindle

Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.51 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

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USA

Sobre este producto

Is your software running at a fraction of its potential?You have a powerful graphics card sitting in your machine, likely capable of trillions of operations per second. Yet, your C++ application is still chugging along on the CPU, processing data sequentially, one instruction at a time. Are you tired of watching progress bars crawl when you know the hardware to make them fly is already sitting in the chassis?The Free Lunch Is OverFor decades, developers could be lazy. We waited 18 months, bought a new processor, and our code magically ran faster. That era ended years ago. Single-core speeds have plateaued. Today, performance doesn't come from faster clocks; it comes from massive parallelism.The problem is that writing parallel code is hard. Really hard.You might be an expert in C++, but CUDA requires you to think differently. It demands that you understand the physical architecture of the hardware. If you treat a GPU like a CPU with more cores, your code won't just be slow—it might actually be slower than the single-threaded version you started with. You have to worry about memory coalescing, warp divergence, bank conflicts, and PCIe bottlenecks. It’s enough to make even seasoned engineers stick to the safety of the CPU.A Bridge from C++ to SiliconGPU Programming with C++ and CUDA is not a theoretical textbook filled with abstract math. It is a practical engineering guide designed to take you from "I know C++" to "I can architect high-performance parallel systems."This book respects your time. It skips the fluff and goes straight to the mechanics of how NVIDIA GPUs actually work. You won't just learn how to write a kernel; you will learn why the hardware behaves the way it does. We dismantle the black box, showing you exactly how data moves from global memory to the streaming multiprocessors and how to structure your algorithms to keep those processors fed.What You Will Gain:Speed, Not Hype: Learn the specific optimization hierarchies that turn average code into production-grade software. You will master memory coalescing, shared memory tiling, and asynchronous streams.Modern Engineering: This isn't C-style CUDA from 2010. You will learn to integrate standard C++ templates, lambdas, and classes into your GPU code, keeping your codebase clean and maintainable.Legacy Migration Strategies: Most of us don't get to start from scratch. This book dedicates entire chapters to the difficult reality of porting existing, messy C++ applications to the GPU without breaking them.Profiling Mastery: Stop guessing where the bottleneck is. You will learn to use professional profiling tools to visualize your code’s execution, seeing exactly where every microsecond goes.Real-World Application: From financial Monte Carlo simulations to real-time image processing, you will see how these concepts apply to the actual problems engineers solve daily.Stop Letting Your Hardware IdleThe future of computing is heterogeneous. The most exciting jobs in finance, AI, scientific research, and gaming now demand GPU skills. Don't get left behind in the era of serial processing.Grab your copy today, and start building software that runs as fast as the hardware allows.

U$S 52,48

60% OFF

U$S 20,99

U$S 52,48

60% OFF

U$S 20,99

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